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SongZihuan
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CoTan
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Merge branch 'ML'

Huan 5 سال پیش
والد
fbd0eee081 8a43f3914e
کامیت
6e5a386c96
100فایلهای تغییر یافته به همراه7136 افزوده شده و 2654 حذف شده
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تفاوت فایلی نمایش داده نمی شود زیرا این فایل بسیار بزرگ است
+ 0 - 5
$Show_Des_Sheet.html


+ 0 - 433
$Show_Sheet.html
مشاهده فایل 

@@ -1,433 +0,0 @@
 
-<!DOCTYPE html>
 
-<html>
 
-<head>
 
-    <meta charset="UTF-8">
 
-    <title>CoTan:查看表格</title>
 
-    
-    
-</head>
 
-<body>
 
-        <style>
 
-        .tab {
 
-            overflow: hidden;
 
-            border: 1px solid #ccc;
 
-            background-color: #f1f1f1;
 
-        }
 
-
 
-        .tab button {
 
-            background-color: inherit;
 
-            float: left;
 
-            border: none;
 
-            outline: none;
 
-            cursor: pointer;
 
-            padding: 12px 16px;
 
-            transition: 0.3s;
 
-        }
 
-
 
-        .tab button:hover {
 
-            background-color: #ddd;
 
-        }
 
-
 
-        .tab button.active {
 
-            background-color: #ccc;
 
-        }
 
-
 
-        .chart-container {
 
-            display: none;
 
-            padding: 6px 12px;
 
-            border-top: none;
 
-        }
 
-    </style>
 
-    <div class="tab">
 
-            <button class="tablinks" onclick="showChart(event, '88fed6788ece44b0a7e06ff69286761e')">表格:Sheet[0]</button>
 
-            <button class="tablinks" onclick="showChart(event, '88fed6788ece44b0a7e06ff69286761e')">表格:Sheet[0]</button>
 
-            <button class="tablinks" onclick="showChart(event, '88fed6788ece44b0a7e06ff69286761e')">表格:Sheet[0]</button>
 
-            <button class="tablinks" onclick="showChart(event, '88fed6788ece44b0a7e06ff69286761e')">表格:Sheet[0]</button>
 
-    </div>
 
-
 
-    <div class="box">
 
-                        <style>
 
-            .fl-table {
 
-                margin: 20px;
 
-                border-radius: 5px;
 
-                font-size: 12px;
 
-                border: none;
 
-                border-collapse: collapse;
 
-                max-width: 100%;
 
-                white-space: nowrap;
 
-                word-break: keep-all;
 
-            }
 
-
 
-            .fl-table th {
 
-                text-align: left;
 
-                font-size: 20px;
 
-            }
 
-
 
-            .fl-table tr {
 
-                display: table-row;
 
-                vertical-align: inherit;
 
-                border-color: inherit;
 
-            }
 
-
 
-            .fl-table tr:hover td {
 
-                background: #00d1b2;
 
-                color: #F8F8F8;
 
-            }
 
-
 
-            .fl-table td, .fl-table th {
 
-                border-style: none;
 
-                border-top: 1px solid #dbdbdb;
 
-                border-left: 1px solid #dbdbdb;
 
-                border-bottom: 3px solid #dbdbdb;
 
-                border-right: 1px solid #dbdbdb;
 
-                padding: .5em .55em;
 
-                font-size: 15px;
 
-            }
 
-
 
-            .fl-table td {
 
-                border-style: none;
 
-                font-size: 15px;
 
-                vertical-align: center;
 
-                border-bottom: 1px solid #dbdbdb;
 
-                border-left: 1px solid #dbdbdb;
 
-                border-right: 1px solid #dbdbdb;
 
-                height: 30px;
 
-            }
 
-
 
-            .fl-table tr:nth-child(even) {
 
-                background: #F8F8F8;
 
-            }
 
-        </style>
 
-        <div id="88fed6788ece44b0a7e06ff69286761e" class="chart-container" style="">
 
-            <p class="title" style="font-size: 18px; font-weight:bold;" > 表格:Sheet[0]</p>
 
-            <p class="subtitle" style="font-size: 12px;" > CoTan~机器学习:查看表格</p>
 
-            <table class="fl-table">
 
-    <tr>
 
-        <th>Sheet[0]</th>
 
-        <th>2</th>
 
-        <th>3</th>
 
-        <th>4</th>
 
-    </tr>
 
-    <tr>
 
-        <td>广东</td>
 
-        <td>12</td>
 
-        <td>北京</td>
 
-        <td>[##H]4</td>
 
-    </tr>
 
-    <tr>
 
-        <td>湖北</td>
 
-        <td>8</td>
 
-        <td>北京</td>
 
-        <td>[##H]19</td>
 
-    </tr>
 
-    <tr>
 
-        <td>上海</td>
 
-        <td>6</td>
 
-        <td>北京</td>
 
-        <td>[##S]19</td>
 
-    </tr>
 
-    <tr>
 
-        <td>北京</td>
 
-        <td>7</td>
 
-        <td>新疆</td>
 
-        <td>[##S]20</td>
 
-    </tr>
 
-</table>
 
-        </div>
 
-
 
-                        <style>
 
-            .fl-table {
 
-                margin: 20px;
 
-                border-radius: 5px;
 
-                font-size: 12px;
 
-                border: none;
 
-                border-collapse: collapse;
 
-                max-width: 100%;
 
-                white-space: nowrap;
 
-                word-break: keep-all;
 
-            }
 
-
 
-            .fl-table th {
 
-                text-align: left;
 
-                font-size: 20px;
 
-            }
 
-
 
-            .fl-table tr {
 
-                display: table-row;
 
-                vertical-align: inherit;
 
-                border-color: inherit;
 
-            }
 
-
 
-            .fl-table tr:hover td {
 
-                background: #00d1b2;
 
-                color: #F8F8F8;
 
-            }
 
-
 
-            .fl-table td, .fl-table th {
 
-                border-style: none;
 
-                border-top: 1px solid #dbdbdb;
 
-                border-left: 1px solid #dbdbdb;
 
-                border-bottom: 3px solid #dbdbdb;
 
-                border-right: 1px solid #dbdbdb;
 
-                padding: .5em .55em;
 
-                font-size: 15px;
 
-            }
 
-
 
-            .fl-table td {
 
-                border-style: none;
 
-                font-size: 15px;
 
-                vertical-align: center;
 
-                border-bottom: 1px solid #dbdbdb;
 
-                border-left: 1px solid #dbdbdb;
 
-                border-right: 1px solid #dbdbdb;
 
-                height: 30px;
 
-            }
 
-
 
-            .fl-table tr:nth-child(even) {
 
-                background: #F8F8F8;
 
-            }
 
-        </style>
 
-        <div id="88fed6788ece44b0a7e06ff69286761e" class="chart-container" style="">
 
-            <p class="title" style="font-size: 18px; font-weight:bold;" > 表格:Sheet[0]</p>
 
-            <p class="subtitle" style="font-size: 12px;" > CoTan~机器学习:查看表格</p>
 
-            <table class="fl-table">
 
-    <tr>
 
-        <th>Sheet[0]</th>
 
-        <th>2</th>
 
-        <th>3</th>
 
-        <th>4</th>
 
-    </tr>
 
-    <tr>
 
-        <td>广东</td>
 
-        <td>12</td>
 
-        <td>北京</td>
 
-        <td>[##H]4</td>
 
-    </tr>
 
-    <tr>
 
-        <td>湖北</td>
 
-        <td>8</td>
 
-        <td>北京</td>
 
-        <td>[##H]19</td>
 
-    </tr>
 
-    <tr>
 
-        <td>上海</td>
 
-        <td>6</td>
 
-        <td>北京</td>
 
-        <td>[##S]19</td>
 
-    </tr>
 
-    <tr>
 
-        <td>北京</td>
 
-        <td>7</td>
 
-        <td>新疆</td>
 
-        <td>[##S]20</td>
 
-    </tr>
 
-</table>
 
-        </div>
 
-
 
-                        <style>
 
-            .fl-table {
 
-                margin: 20px;
 
-                border-radius: 5px;
 
-                font-size: 12px;
 
-                border: none;
 
-                border-collapse: collapse;
 
-                max-width: 100%;
 
-                white-space: nowrap;
 
-                word-break: keep-all;
 
-            }
 
-
 
-            .fl-table th {
 
-                text-align: left;
 
-                font-size: 20px;
 
-            }
 
-
 
-            .fl-table tr {
 
-                display: table-row;
 
-                vertical-align: inherit;
 
-                border-color: inherit;
 
-            }
 
-
 
-            .fl-table tr:hover td {
 
-                background: #00d1b2;
 
-                color: #F8F8F8;
 
-            }
 
-
 
-            .fl-table td, .fl-table th {
 
-                border-style: none;
 
-                border-top: 1px solid #dbdbdb;
 
-                border-left: 1px solid #dbdbdb;
 
-                border-bottom: 3px solid #dbdbdb;
 
-                border-right: 1px solid #dbdbdb;
 
-                padding: .5em .55em;
 
-                font-size: 15px;
 
-            }
 
-
 
-            .fl-table td {
 
-                border-style: none;
 
-                font-size: 15px;
 
-                vertical-align: center;
 
-                border-bottom: 1px solid #dbdbdb;
 
-                border-left: 1px solid #dbdbdb;
 
-                border-right: 1px solid #dbdbdb;
 
-                height: 30px;
 
-            }
 
-
 
-            .fl-table tr:nth-child(even) {
 
-                background: #F8F8F8;
 
-            }
 
-        </style>
 
-        <div id="88fed6788ece44b0a7e06ff69286761e" class="chart-container" style="">
 
-            <p class="title" style="font-size: 18px; font-weight:bold;" > 表格:Sheet[0]</p>
 
-            <p class="subtitle" style="font-size: 12px;" > CoTan~机器学习:查看表格</p>
 
-            <table class="fl-table">
 
-    <tr>
 
-        <th>Sheet[0]</th>
 
-        <th>2</th>
 
-        <th>3</th>
 
-        <th>4</th>
 
-    </tr>
 
-    <tr>
 
-        <td>广东</td>
 
-        <td>12</td>
 
-        <td>北京</td>
 
-        <td>[##H]4</td>
 
-    </tr>
 
-    <tr>
 
-        <td>湖北</td>
 
-        <td>8</td>
 
-        <td>北京</td>
 
-        <td>[##H]19</td>
 
-    </tr>
 
-    <tr>
 
-        <td>上海</td>
 
-        <td>6</td>
 
-        <td>北京</td>
 
-        <td>[##S]19</td>
 
-    </tr>
 
-    <tr>
 
-        <td>北京</td>
 
-        <td>7</td>
 
-        <td>新疆</td>
 
-        <td>[##S]20</td>
 
-    </tr>
 
-</table>
 
-        </div>
 
-
 
-                        <style>
 
-            .fl-table {
 
-                margin: 20px;
 
-                border-radius: 5px;
 
-                font-size: 12px;
 
-                border: none;
 
-                border-collapse: collapse;
 
-                max-width: 100%;
 
-                white-space: nowrap;
 
-                word-break: keep-all;
 
-            }
 
-
 
-            .fl-table th {
 
-                text-align: left;
 
-                font-size: 20px;
 
-            }
 
-
 
-            .fl-table tr {
 
-                display: table-row;
 
-                vertical-align: inherit;
 
-                border-color: inherit;
 
-            }
 
-
 
-            .fl-table tr:hover td {
 
-                background: #00d1b2;
 
-                color: #F8F8F8;
 
-            }
 
-
 
-            .fl-table td, .fl-table th {
 
-                border-style: none;
 
-                border-top: 1px solid #dbdbdb;
 
-                border-left: 1px solid #dbdbdb;
 
-                border-bottom: 3px solid #dbdbdb;
 
-                border-right: 1px solid #dbdbdb;
 
-                padding: .5em .55em;
 
-                font-size: 15px;
 
-            }
 
-
 
-            .fl-table td {
 
-                border-style: none;
 
-                font-size: 15px;
 
-                vertical-align: center;
 
-                border-bottom: 1px solid #dbdbdb;
 
-                border-left: 1px solid #dbdbdb;
 
-                border-right: 1px solid #dbdbdb;
 
-                height: 30px;
 
-            }
 
-
 
-            .fl-table tr:nth-child(even) {
 
-                background: #F8F8F8;
 
-            }
 
-        </style>
 
-        <div id="88fed6788ece44b0a7e06ff69286761e" class="chart-container" style="">
 
-            <p class="title" style="font-size: 18px; font-weight:bold;" > 表格:Sheet[0]</p>
 
-            <p class="subtitle" style="font-size: 12px;" > CoTan~机器学习:查看表格</p>
 
-            <table class="fl-table">
 
-    <tr>
 
-        <th>Sheet[0]</th>
 
-        <th>2</th>
 
-        <th>3</th>
 
-        <th>4</th>
 
-    </tr>
 
-    <tr>
 
-        <td>广东</td>
 
-        <td>12</td>
 
-        <td>北京</td>
 
-        <td>[##H]4</td>
 
-    </tr>
 
-    <tr>
 
-        <td>湖北</td>
 
-        <td>8</td>
 
-        <td>北京</td>
 
-        <td>[##H]19</td>
 
-    </tr>
 
-    <tr>
 
-        <td>上海</td>
 
-        <td>6</td>
 
-        <td>北京</td>
 
-        <td>[##S]19</td>
 
-    </tr>
 
-    <tr>
 
-        <td>北京</td>
 
-        <td>7</td>
 
-        <td>新疆</td>
 
-        <td>[##S]20</td>
 
-    </tr>
 
-</table>
 
-        </div>
 
-
 
-    </div>
 
-
 
-    <script>
 
-    </script>
 
-    <script>
 
-        (function() {
 
-            containers = document.getElementsByClassName("chart-container");
 
-            if(containers.length > 0) {
 
-                containers[0].style.display = "block";
 
-            }
 
-        })()
 
-
 
-        function showChart(evt, chartID) {
 
-            let containers = document.getElementsByClassName("chart-container");
 
-            for (let i = 0; i < containers.length; i++) {
 
-                containers[i].style.display = "none";
 
-            }
 
-
 
-            let tablinks = document.getElementsByClassName("tablinks");
 
-            for (let i = 0; i < tablinks.length; i++) {
 
-                tablinks[i].className = "tablinks";
 
-            }
 
-
 
-            document.getElementById(chartID).style.display = "block";
 
-            evt.currentTarget.className += " active";
 
-        }
 
-    </script>
 
-</body>
 
-</html>

+ 1 - 1
CGB/__init__.py
مشاهده فایل 

@@ -1,2 +1,2 @@
 
 from CGB.Write import Draw as Draw
 
 from CGB.Write import Draw as Draw
 
-print('CGB加载完毕...')
 
+print('草稿版加载完毕...')

BIN
CGB/__pycache__/__init__.cpython-37.pyc
مشاهده فایل


+ 1 - 1
DSGC/__init__.py
مشاهده فایل 

@@ -1,2 +1,2 @@
 
 from DSGC.Algebra_Systemctl import Alg
 
 from DSGC.Algebra_Systemctl import Alg
 
-print('DSGC加载完毕...')
 
+print('代数工厂加载完毕...')

BIN
DSGC/__pycache__/Algebra_Systemctl.cpython-37.pyc
مشاهده فایل


BIN
DSGC/__pycache__/__init__.cpython-37.pyc
مشاهده فایل


تفاوت فایلی نمایش داده نمی شود زیرا این فایل بسیار بزرگ است
+ 383 - 308
Data_Science/Learn.py


+ 253 - 140
Me_learn.py → Data_Science/Me_learn.py
مشاهده فایل 

@@ -1,7 +1,7 @@
 
 import tkinter
 
 import tkinter
 
 from tkinter.filedialog import askopenfilename, asksaveasfilename
 
 from tkinter.filedialog import askopenfilename, asksaveasfilename
 
 import tkinter.messagebox
 
 import tkinter.messagebox
 
-import Learn
 
+from Data_Science import Learn
 
 import webbrowser
 
 import webbrowser
 
 import os
 
 import os
 
 from tkinter.scrolledtext import ScrolledText
 
 from tkinter.scrolledtext import ScrolledText
 
@@ -86,7 +86,7 @@ max_Visual_mapping #映射的最大值
 
 def Machine_learning():
 
 def Machine_learning():
 
     global top, ML, Form_List, PATH, bg, ft1, Stored_List, Clean_List, R_Dic, Over_Up, Over_Down,Learn_Dic
 
     global top, ML, Form_List, PATH, bg, ft1, Stored_List, Clean_List, R_Dic, Over_Up, Over_Down,Learn_Dic
 
     R_Dic = {}  # 保存了画图的List
 
     R_Dic = {}  # 保存了画图的List
 
-    Learn_Dic = {} #保存机器学习
 
+    Learn_Dic = {} #保存数据处理
 
     PATH = os.getcwd()
 
     PATH = os.getcwd()
 
     Form_List = []
 
     Form_List = []
 
     ML = Learn.Machine_Learner()
 
     ML = Learn.Machine_Learner()
 
@@ -97,7 +97,7 @@ def Machine_learning():
 
     top["bg"] = bg
 
     top["bg"] = bg
 
     FONT = ('黑体', 11)  # 设置字体
 
     FONT = ('黑体', 11)  # 设置字体
 
     ft1 = ('黑体', 13)
 
     ft1 = ('黑体', 13)
 
-    top.title('CoTan机器学习')
 
+    top.title('CoTan数据处理')
 
     top.resizable(width=False, height=False)
 
     top.resizable(width=False, height=False)
 
     top.geometry('+10+10')  # 设置所在位置
 
     top.geometry('+10+10')  # 设置所在位置
 
     width_B = 13  # 标准宽度
 
     width_B = 13  # 标准宽度
 
@@ -116,18 +116,24 @@ def Machine_learning():
 
     tkinter.Button(top, bg=bbg, fg=fg, text='导入Py', command=Add_Python, font=FONT, width=width_B,
 
     tkinter.Button(top, bg=bbg, fg=fg, text='导入Py', command=Add_Python, font=FONT, width=width_B,
 
                    height=height_B).grid(column=a_x + 1, row=a_y,
 
                    height=height_B).grid(column=a_x + 1, row=a_y,
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='导入HTML', command=Add_Html, font=FONT, width=width_B,
 
-                   height=height_B).grid(column=a_x + 2, row=a_y,
 
-                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='导出CSV', command=to_CSV,font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 2, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
 
 
+    global name_Input
 
     a_y += 1
 
     a_y += 1
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='删除表格', font=FONT, width=width_B,
 
+    tkinter.Label(top, text='表格名称:', bg=bg, fg=fg, font=FONT, width=width_B, height=height_B).grid(column=a_x,
 
+                                                                                                   row=a_y)  # 设置说明
 
+    name_Input = tkinter.Entry(top, width=width_B)
 
+    name_Input.grid(column=a_x + 1, row=a_y,columnspan=2, sticky=tkinter.E + tkinter.W)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='删除表格',command=Del_Sheet, font=FONT, width=width_B,
 
                    height=height_B).grid(column=a_x, row=a_y,
 
                    height=height_B).grid(column=a_x, row=a_y,
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
     tkinter.Button(top, bg=bbg, fg=fg, text='查看表格', command=Show, font=FONT, width=width_B,
 
     tkinter.Button(top, bg=bbg, fg=fg, text='查看表格', command=Show, font=FONT, width=width_B,
 
                    height=height_B).grid(column=a_x + 1, row=a_y,
 
                    height=height_B).grid(column=a_x + 1, row=a_y,
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='清空表格', font=FONT, width=width_B,
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='查看单一表格', command=Show_One,font=FONT, width=width_B,
 
                    height=height_B).grid(column=a_x + 2, row=a_y,
 
                    height=height_B).grid(column=a_x + 2, row=a_y,
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
 
 
@@ -150,7 +156,6 @@ def Machine_learning():
 
     buttom = tkinter.Checkbutton(top, bg=bg, fg=fg, activebackground=bg, activeforeground=fg, selectcolor=bg,
 
     buttom = tkinter.Checkbutton(top, bg=bg, fg=fg, activebackground=bg, activeforeground=fg, selectcolor=bg,
 
                                  text='字符串类型',
 
                                  text='字符串类型',
 
                                  variable=str_must)
 
                                  variable=str_must)
 
-    buttom.select()
 
     buttom.grid(column=a_x + 2, row=a_y, sticky=tkinter.W)
 
     buttom.grid(column=a_x + 2, row=a_y, sticky=tkinter.W)
 
 
 
     a_y += 1
 
     a_y += 1
 
@@ -195,11 +200,11 @@ def Machine_learning():
 
     # 其中数字1-6是第一行，1-c是第二行，第二行在第一行下面，row变大向下移动（Row是横向行而不是横向移动） to 搞不清楚横行竖列的人
 
     # 其中数字1-6是第一行，1-c是第二行，第二行在第一行下面，row变大向下移动（Row是横向行而不是横向移动） to 搞不清楚横行竖列的人
 
 
 
     a_y += 1
 
     a_y += 1
 
-    Index_BOX = tkinter.Listbox(top, width=width_B * 3, height=height_B * 3)  # 显示符号
 
-    Index_BOX.grid(column=a_x, row=a_y, columnspan=3, rowspan=3, sticky=tkinter.E + tkinter.W + tkinter.S + tkinter.N)
 
+    Index_BOX = tkinter.Listbox(top, width=width_B * 3, height=height_B * 10)  # 显示符号
 
+    Index_BOX.grid(column=a_x, row=a_y, columnspan=3, rowspan=10, sticky=tkinter.E + tkinter.W + tkinter.S + tkinter.N)
 
 
 
     global Des_Bool
 
     global Des_Bool
 
-    a_y += 3
 
+    a_y += 10
 
     tkinter.Button(top, bg=bbg, fg=fg, text='查看数据分析', command=Show_Des, font=FONT, width=width_B,
 
     tkinter.Button(top, bg=bbg, fg=fg, text='查看数据分析', command=Show_Des, font=FONT, width=width_B,
 
                    height=height_B).grid(column=a_x, row=a_y,
 
                    height=height_B).grid(column=a_x, row=a_y,
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
@@ -210,58 +215,6 @@ def Machine_learning():
 
     tkinter.Checkbutton(top, bg=bg, fg=fg, activebackground=bg, activeforeground=fg, selectcolor=bg, text='生成统计表格',
 
     tkinter.Checkbutton(top, bg=bg, fg=fg, activebackground=bg, activeforeground=fg, selectcolor=bg, text='生成统计表格',
 
                         variable=Des_Bool).grid(column=a_x + 2, row=a_y, sticky=tkinter.W)
 
                         variable=Des_Bool).grid(column=a_x + 2, row=a_y, sticky=tkinter.W)
 
 
 
-    a_y += 1
 
-    tkinter.Label(top, text='【排序操作】', bg=bg, fg=fg, font=FONT, width=width_B * 3, height=height_B).grid(column=a_x,
 
-                                                                                                        columnspan=3,
 
-                                                                                                        row=a_y)  # 设置说明
 
-
 
-    a_y += 1
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='表格转置', command=T, font=FONT, width=width_B,
 
-                   height=height_B).grid(column=a_x, row=a_y,
 
-                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='按行名排序', command=Stored_Row, font=FONT, width=width_B,
 
-                   height=height_B).grid(column=a_x + 1, row=a_y,
 
-                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='按列名排序', command=Stored_Column, font=FONT, width=width_B,
 
-                   height=height_B).grid(column=a_x + 2, row=a_y,
 
-                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
-
 
-    global Sort_By, Ascending_Type, Ascending_New, Stored_BOX
 
-    a_y += 1
 
-    tkinter.Label(top, text='基准列(列号):', bg=bg, fg=fg, font=FONT, width=width_B, height=height_B).grid(column=a_x,
 
-                                                                                                      row=a_y)  # 设置说明
 
-    Sort_By = tkinter.Entry(top, width=width_B + 2)
 
-    Sort_By.grid(column=a_x + 1, row=a_y, sticky=tkinter.W)
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='按数据排序', command=Stored_Value, font=FONT, width=width_B,
 
-                   height=height_B).grid(column=a_x + 2, row=a_y,
 
-                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
-
 
-    a_y += 1
 
-    Ascending_Type = tkinter.IntVar()
 
-    Ascending_New = tkinter.IntVar()
 
-    lable = ['正序排列', '倒序排列']  # 复选框
 
-    for i in range(2):
 
-        tkinter.Radiobutton(top, bg=bg, fg=fg, activebackground=bg, activeforeground=fg, selectcolor=bg, text=lable[i],
 
-                            variable=Ascending_Type, value=i). \
 
-            grid(column=a_x + i, row=a_y, sticky=tkinter.W)
 
-    tkinter.Checkbutton(top, bg=bg, fg=fg, activebackground=bg, activeforeground=fg, selectcolor=bg, text='生成新表格',
 
-                        variable=Ascending_New).grid(column=a_x + 2, row=a_y, sticky=tkinter.W)
 
-
 
-    a_y += 1
 
-    Stored_BOX = tkinter.Listbox(top, width=width_B * 3, height=height_B * 3)  # 显示符号
 
-    Stored_BOX.grid(column=a_x, row=a_y, columnspan=3, rowspan=3, sticky=tkinter.E + tkinter.W + tkinter.S + tkinter.N)
 
-
 
-    a_y += 3
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='添加基准', command=add_Stored_Value, font=FONT, width=width_B,
 
-                   height=height_B).grid(column=a_x, row=a_y,
 
-                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='删除基准', command=Delete_Stored_Value, font=FONT, width=width_B,
 
-                   height=height_B).grid(column=a_x + 1, row=a_y,
 
-                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='清空基准', command=Tra_Stored_Value, font=FONT, width=width_B,
 
-                   height=height_B).grid(column=a_x + 2, row=a_y,
 
-                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
-
 
     a_x += 3
 
     a_x += 3
 
     tkinter.Label(top, text='', bg=bg, fg=fg, font=FONT, width=1).grid(column=a_x, row=a_y)  # 设置说明
 
     tkinter.Label(top, text='', bg=bg, fg=fg, font=FONT, width=1).grid(column=a_x, row=a_y)  # 设置说明
 
     a_x += 1
 
     a_x += 1
 
@@ -530,10 +483,10 @@ def Machine_learning():
 
     a_y += 2
 
     a_y += 2
 
     global Draw_asWell
 
     global Draw_asWell
 
     Draw_asWell = tkinter.IntVar()
 
     Draw_asWell = tkinter.IntVar()
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='清空渲染', command=Draw, font=FONT, width=width_B,
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='清空渲染', command=Tra_RDic, font=FONT, width=width_B,
 
                    height=height_B).grid(column=a_x, row=a_y,
 
                    height=height_B).grid(column=a_x, row=a_y,
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='导入渲染', command=Draw, font=FONT, width=width_B,
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='导入渲染', command=Import_c, font=FONT, width=width_B,
 
                    height=height_B).grid(column=a_x + 1, row=a_y,
 
                    height=height_B).grid(column=a_x + 1, row=a_y,
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
     tkinter.Checkbutton(top, bg=bg, fg=fg, activebackground=bg, activeforeground=fg, selectcolor=bg, text='马上渲染',
 
     tkinter.Checkbutton(top, bg=bg, fg=fg, activebackground=bg, activeforeground=fg, selectcolor=bg, text='马上渲染',
 
@@ -543,10 +496,10 @@ def Machine_learning():
 
     Args_Input.grid(column=a_x, row=a_y, columnspan=3, rowspan=7, sticky=tkinter.E + tkinter.W + tkinter.N + tkinter.S)
 
     Args_Input.grid(column=a_x, row=a_y, columnspan=3, rowspan=7, sticky=tkinter.E + tkinter.W + tkinter.N + tkinter.S)
 
 
 
     a_y += 7
 
     a_y += 7
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='查看词典', font=FONT, width=width_B,
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='查看词典',command=Show_Help, font=FONT, width=width_B,
 
                    height=height_B).grid(column=a_x, row=a_y,
 
                    height=height_B).grid(column=a_x, row=a_y,
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='恢复显示', font=FONT, width=width_B,
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='恢复显示',command=Show_Help, font=FONT, width=width_B,
 
                    height=height_B).grid(column=a_x + 1, row=a_y, columnspan=2,
 
                    height=height_B).grid(column=a_x + 1, row=a_y, columnspan=2,
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
 
 
@@ -577,7 +530,7 @@ def Machine_learning():
 
         RC_Type.append(tkinter.IntVar())
 
         RC_Type.append(tkinter.IntVar())
 
         tkinter.Checkbutton(top, bg=bg, fg=fg, activebackground=bg, activeforeground=fg, selectcolor=bg, text=lable[i],
 
         tkinter.Checkbutton(top, bg=bg, fg=fg, activebackground=bg, activeforeground=fg, selectcolor=bg, text=lable[i],
 
                             variable=RC_Type[-1]).grid(column=a_x + i, row=a_y, sticky=tkinter.W)
 
                             variable=RC_Type[-1]).grid(column=a_x + i, row=a_y, sticky=tkinter.W)
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='统一行号', command=num_toName, font=FONT, width=width_B, height=height_B). \
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='统一行(列)号', command=num_toName, font=FONT, width=width_B, height=height_B). \
 
         grid(column=a_x + 2, row=a_y, sticky=tkinter.E + tkinter.W)
 
         grid(column=a_x + 2, row=a_y, sticky=tkinter.E + tkinter.W)
 
 
 
     a_y += 1
 
     a_y += 1
 
@@ -653,8 +606,66 @@ def Machine_learning():
 
         tkinter.Radiobutton(top, bg=bg, fg=fg, activebackground=bg, activeforeground=fg, selectcolor=bg, text=lable[i],
 
         tkinter.Radiobutton(top, bg=bg, fg=fg, activebackground=bg, activeforeground=fg, selectcolor=bg, text=lable[i],
 
                             variable=Dtype_Func, value=i).grid(column=a_x + 1 + i, row=a_y, sticky=tkinter.W)
 
                             variable=Dtype_Func, value=i).grid(column=a_x + 1 + i, row=a_y, sticky=tkinter.W)
 
 
 
+
 
     a_y += 1
 
     a_y += 1
 
-    tkinter.Label(top, text='【机器学习与数据处理】', bg=bg, fg=fg, font=FONT, width=width_B * 3, height=height_B).grid(column=a_x,
 
+    tkinter.Label(top, text='【排序操作】', bg=bg, fg=fg, font=FONT, width=width_B * 3, height=height_B).grid(column=a_x,
 
+                                                                                                        columnspan=3,
 
+                                                                                                        row=a_y)  # 设置说明
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='.T', command=T, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='按行名排序', command=Stored_Row, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 1, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='按列名排序', command=Stored_Column, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 2, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    global Sort_By, Ascending_Type, Ascending_New, Stored_BOX
 
+    a_y += 1
 
+    tkinter.Label(top, text='基准列(列号):', bg=bg, fg=fg, font=FONT, width=width_B, height=height_B).grid(column=a_x,
 
+                                                                                                      row=a_y)  # 设置说明
 
+    Sort_By = tkinter.Entry(top, width=width_B + 2)
 
+    Sort_By.grid(column=a_x + 1, row=a_y, sticky=tkinter.W)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='按数据排序', command=Stored_Value, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 2, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    Ascending_Type = tkinter.IntVar()
 
+    Ascending_New = tkinter.IntVar()
 
+    lable = ['正序排列', '倒序排列']  # 复选框
 
+    for i in range(2):
 
+        tkinter.Radiobutton(top, bg=bg, fg=fg, activebackground=bg, activeforeground=fg, selectcolor=bg, text=lable[i],
 
+                            variable=Ascending_Type, value=i). \
 
+            grid(column=a_x + i, row=a_y, sticky=tkinter.W)
 
+    tkinter.Checkbutton(top, bg=bg, fg=fg, activebackground=bg, activeforeground=fg, selectcolor=bg, text='生成新表格',
 
+                        variable=Ascending_New).grid(column=a_x + 2, row=a_y, sticky=tkinter.W)
 
+
 
+    a_y += 1
 
+    Stored_BOX = tkinter.Listbox(top, width=width_B * 3, height=height_B * 4)  # 显示符号
 
+    Stored_BOX.grid(column=a_x, row=a_y, columnspan=3, rowspan=5, sticky=tkinter.E + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 5
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='添加基准', command=add_Stored_Value, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='删除基准', command=Delete_Stored_Value, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 1, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='打乱表格', command=Sample_Data, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 2, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+
 
+    a_x += 3
 
+    tkinter.Label(top, text='', bg=bg, fg=fg, font=FONT, width=1).grid(column=a_x, row=a_y)  # 设置说明
 
+    a_x += 1
 
+    a_y = 0
 
+
 
+    tkinter.Label(top, text='【机器学习】', bg=bg, fg=fg, font=FONT, width=width_B * 3, height=height_B).grid(column=a_x,
 
                                                                                                              columnspan=3,
 
                                                                                                              columnspan=3,
 
                                                                                                              row=a_y,
 
                                                                                                              row=a_y,
 
                                                                                                              sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)  # 设置说明
 
                                                                                                              sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)  # 设置说明
 
@@ -667,16 +678,22 @@ def Machine_learning():
 
     Put['state'] = 'readonly'
 
     Put['state'] = 'readonly'
 
     tkinter.Button(top, bg=bbg, fg=fg, text='选用学习器',command=set_Learner, font=FONT, width=width_B,height=height_B).grid(column=a_x+2, row=a_y, sticky=tkinter.E + tkinter.W)
 
     tkinter.Button(top, bg=bbg, fg=fg, text='选用学习器',command=set_Learner, font=FONT, width=width_B,height=height_B).grid(column=a_x+2, row=a_y, sticky=tkinter.E + tkinter.W)
 
 
 
+    global Split_Input
 
+    a_y += 1
 
+    tkinter.Label(top, text='测试数据分割:', bg=bg, fg=fg, font=FONT, width=width_B, height=height_B).grid(column=a_x,row=a_y)
 
+    Split_Input = tkinter.Entry(top, width=width_B * 2)
 
+    Split_Input.grid(column=a_x + 1, row=a_y, columnspan=2, sticky=tkinter.E + tkinter.W)
 
+
 
     a_y += 1
 
     a_y += 1
 
-    ML_BOX = tkinter.Listbox(top, width=width_B * 3, height=height_B*6)
 
-    ML_BOX.grid(column=a_x, row=a_y, columnspan=3, rowspan=6, sticky=tkinter.E + tkinter.W + tkinter.S + tkinter.N)
 
+    ML_BOX = tkinter.Listbox(top, width=width_B * 3, height=height_B*5)
 
+    ML_BOX.grid(column=a_x, row=a_y, columnspan=3, rowspan=5, sticky=tkinter.E + tkinter.W + tkinter.S + tkinter.N)
 
 
 
-    a_y += 6
 
+    a_y += 5
 
     tkinter.Button(top, bg=bbg, fg=fg, text='导入学习器', command=Draw, font=FONT, width=width_B,height=height_B).grid(column=a_x, row=a_y,
 
     tkinter.Button(top, bg=bbg, fg=fg, text='导入学习器', command=Draw, font=FONT, width=width_B,height=height_B).grid(column=a_x, row=a_y,
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='查看数据', command=Draw_One, font=FONT, width=width_B,height=height_B).grid(column=a_x + 1, row=a_y,
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='查看数据', command=Show_Args, font=FONT, width=width_B,height=height_B).grid(column=a_x + 1, row=a_y,
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='数据可视化', command=Del_R_BOX, font=FONT, width=width_B,height=height_B).grid(column=a_x + 2, row=a_y,
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='删除学习器', command=Del_Leaner, font=FONT, width=width_B,height=height_B).grid(column=a_x + 2, row=a_y,
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
 
 
     a_y += 1
 
     a_y += 1
 
@@ -687,13 +704,8 @@ def Machine_learning():
 
     tkinter.Button(top, bg=bbg, fg=fg, text='数据预测', command=Predict_Learner, font=FONT, width=width_B,height=height_B).grid(column=a_x + 2, row=a_y,
 
     tkinter.Button(top, bg=bbg, fg=fg, text='数据预测', command=Predict_Learner, font=FONT, width=width_B,height=height_B).grid(column=a_x + 2, row=a_y,
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
 
 
-
 
-    a_x += 3
 
-    tkinter.Label(top, text='', bg=bg, fg=fg, font=FONT, width=1).grid(column=a_x, row=a_y)  # 设置说明
 
-    a_x += 1
 
-    a_y = 0
 
-
 
-    tkinter.Label(top, text='【机器学习】', bg=bg, fg=fg, font=FONT, width=width_B * 3, height=height_B).grid(column=a_x,
 
+    a_y += 1
 
+    tkinter.Label(top, text='【学习器选择和配置】', bg=bg, fg=fg, font=FONT, width=width_B * 3, height=height_B).grid(column=a_x,
 
                                                                                                          columnspan=3,
 
                                                                                                          columnspan=3,
 
                                                                                                          row=a_y,
 
                                                                                                          row=a_y,
 
                                                                                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)  # 设置说明
 
                                                                                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)  # 设置说明
 
@@ -701,31 +713,85 @@ def Machine_learning():
 
     a_y += 1
 
     a_y += 1
 
     tkinter.Button(top, bg=bbg, fg=fg, text='线性回归', command=Add_Line, font=FONT, width=width_B,height=height_B).grid(column=a_x, row=a_y,
 
     tkinter.Button(top, bg=bbg, fg=fg, text='线性回归', command=Add_Line, font=FONT, width=width_B,height=height_B).grid(column=a_x, row=a_y,
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='岭回归', command=Draw_One, font=FONT, width=width_B,height=height_B).grid(column=a_x + 1, row=a_y,
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='岭回归', command=Add_Ridge, font=FONT, width=width_B,height=height_B).grid(column=a_x + 1, row=a_y,
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='Lasso', command=Del_R_BOX, font=FONT, width=width_B,height=height_B).grid(column=a_x + 2, row=a_y,
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='Lasso', command=Add_Lasso, font=FONT, width=width_B,height=height_B).grid(column=a_x + 2, row=a_y,
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
 
 
     a_y += 1
 
     a_y += 1
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='逻辑回归', command=Draw, font=FONT, width=width_B,height=height_B).grid(column=a_x, row=a_y,
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='逻辑回归', command=Add_LogisticRegression, font=FONT, width=width_B,height=height_B).grid(column=a_x, row=a_y,
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='决策树', command=Draw_One, font=FONT, width=width_B,height=height_B).grid(column=a_x + 1, row=a_y,
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='决策树', command=ML_Sorry, font=FONT, width=width_B,height=height_B).grid(column=a_x + 1, row=a_y,
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='SVM', command=Del_R_BOX, font=FONT, width=width_B,height=height_B).grid(column=a_x + 2, row=a_y,
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='SVM', command=ML_Sorry, font=FONT, width=width_B,height=height_B).grid(column=a_x + 2, row=a_y,
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
 
 
     a_y += 1
 
     a_y += 1
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='朴素贝叶斯(先验为多)', command=Draw, font=FONT, width=width_B,height=height_B).grid(column=a_x, row=a_y,columnspan=2,
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='朴素贝叶斯', command=ML_Sorry, font=FONT, width=width_B,height=height_B).grid(column=a_x, row=a_y,
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
-    tkinter.Button(top, bg=bbg, fg=fg, text='K近邻', command=Del_R_BOX, font=FONT, width=width_B,height=height_B).grid(column=a_x + 2, row=a_y,
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='K邻近分类', command=Add_Knn, font=FONT, width=width_B,height=height_B).grid(column=a_x + 1, row=a_y,
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
                                          sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='K邻近预测', command=Add_Knn_Class, font=FONT, width=width_B,height=height_B).grid(column=a_x + 2, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    global Args_Learner
 
+    a_y += 1
 
+    Args_Learner = tkinter.Text(top, width=width_B * 3, height=height_B * 11)
 
+    Args_Learner.grid(column=a_x, row=a_y, columnspan=3, rowspan=11, sticky=tkinter.E + tkinter.W + tkinter.N + tkinter.S)
 
+
 
     top.mainloop()
 
     top.mainloop()
 
 
 
 
 
+def Show_Help():
 
+    tkinter.messagebox.showinfo('使用提示', Args_Help)
 
+
 
+
 
+def ML_Sorry():
 
+    tkinter.messagebox.showinfo('非常抱歉', '高级别的机器学习请到机器学习板块深入研究...')
 
+
 
+
 
+def Tra_RDic():
 
+    ML.Tra_RDic()
 
+    Update_R_BOX()
 
+
 
+
 
+def Del_Sheet():
 
+    name = get_Name()
 
+    ML.Del_Form(name)
 
+    Updat_BOX()
 
+
 
+
 
+def Del_Leaner():
 
+    Learn = get_Learner(True)
 
+    set_Learne = get_Learner(False)#获取学习器Learner
 
+    if set_Learne != Learn:
 
+        ML.Del_Leaner(Learn)
 
+    Update_Leaner()
 
+
 
+
 
+def Show_Args():
 
+    learner = get_Learner(True)
 
+    new = tkinter.messagebox.askokcancel('提示', f'是否将数据生成表格。\n(可绘制成散点图对比数据)')
 
+    print(new)
 
+    Data = ML.Show_Args(learner,new)
 
+    title = f'CoTan数据处理 查看数据:{learner}'
 
+    Creat_TextSheet(f'对象:{learner}\n\n{Data[0]}\n\n\n{Data[1]}', title)
 
+    Updat_BOX()
 
+
 
+
 
+def get_Args_Learner():
 
+    global Args_Learner
 
+    return Args_Learner.get('0.0',tkinter.END)
 
+
 
+
 
 def Score_Learner():
 
 def Score_Learner():
 
     name = get_Name()#表格数据
 
     name = get_Name()#表格数据
 
     learner = get_Learner()
 
     learner = get_Learner()
 
-    socore = ML.Fit(name,learner,True)[1]
 
+    try:
 
+        split = float(Split_Input.get())
 
+        if split < 0 or 1 < split:raise Exception
 
+    except:split = 0.3
 
+    socore = ML.Fit(name,learner,Score_Only=True,split=split)[1]
 
     tkinter.messagebox.showinfo('测试完成', f'针对测试数据评分结果为:{socore}')
 
     tkinter.messagebox.showinfo('测试完成', f'针对测试数据评分结果为:{socore}')
 
 
 
 
 
@@ -733,7 +799,7 @@ def Predict_Learner():
 
     name = get_Name()#表格数据
 
     name = get_Name()#表格数据
 
     learner = get_Learner()
 
     learner = get_Learner()
 
     Data = ML.Predict(name,learner)
 
     Data = ML.Predict(name,learner)
 
-    title = f'CoTan机器学习 表格:{name} 学习器:{learner}'
 
+    title = f'CoTan数据处理 表格:{name} 学习器:{learner}'
 
     Creat_TextSheet(Data, title)
 
     Creat_TextSheet(Data, title)
 
     Updat_BOX()
 
     Updat_BOX()
 
 
 
@@ -741,9 +807,13 @@ def Predict_Learner():
 
 def Fit_Learner():
 
 def Fit_Learner():
 
     name = get_Name()#表格数据
 
     name = get_Name()#表格数据
 
     learner = get_Learner()
 
     learner = get_Learner()
 
-    socore = ML.Fit(name,learner)
 
-    tkinter.messagebox.showinfo('训练完成', f'针对训练数据(70%)评分结果为:{socore[0]}\n'
 
-                                        f'针对测试数据评分(30%)结果为:{socore[1]}')
 
+    try:
 
+        split = float(Split_Input.get())
 
+        if split < 0 or 1 < split:raise Exception
 
+    except:split = 0.3
 
+    socore = ML.Fit(name,learner,Text=get_Args_Learner(),split=split)
 
+    tkinter.messagebox.showinfo('训练完成', f'针对训练数据({(1-split)*100}%)评分结果为:{socore[0]}\n'
 
+                                        f'针对测试数据评分({split*100}%)结果为:{socore[1]}')
 
 
 
 def set_Learner():
 
 def set_Learner():
 
     global ML_OUT
 
     global ML_OUT
 
@@ -753,17 +823,39 @@ def set_Learner():
 
 def get_Learner(Type=False):
 
 def get_Learner(Type=False):
 
     global Learn_Dic,ML_BOX,ML_OUT
 
     global Learn_Dic,ML_BOX,ML_OUT
 
     if Type:
 
     if Type:
 
-        print('FF')
 
         try:
 
         try:
 
             return list(Learn_Dic.keys())[ML_BOX.curselection()[0]]
 
             return list(Learn_Dic.keys())[ML_BOX.curselection()[0]]
 
         except:
 
         except:
 
-            raise
 
-            # try:
 
-            #     return list(Learn_Dic.keys)[0]
 
-            # except:
 
-            #     return None
 
+            # raise
 
+            try:
 
+                return list(Learn_Dic.keys)[0]
 
+            except:
 
+                return None
 
     else:
 
     else:
 
-        return ML_OUT.get()
 
+        try:
 
+            return ML_OUT.get()
 
+        except:
 
+            return None
 
+
 
+
 
+def Add_Knn_Class():
 
+    Add_leaner('Knn_class')
 
+
 
+
 
+def Add_LogisticRegression():
 
+    Add_leaner('LogisticRegression')
 
+
 
+
 
+def Add_Lasso():
 
+    Add_leaner('Lasso')
 
+
 
+
 
+def Add_Knn():
 
+    Add_leaner('Knn')
 
+
 
+
 
+def Add_Ridge():
 
+    Add_leaner('Ridge')
 
 
 
 
 
 def Add_Line():
 
 def Add_Line():
 
@@ -771,7 +863,7 @@ def Add_Line():
 
 
 
 
 
 def Add_leaner(Type):#添加Lenear的核心
 
 def Add_leaner(Type):#添加Lenear的核心
 
-    ML.Add_Learner(Type)
 
+    ML.Add_Learner(Type,Text=get_Args_Learner())
 
     Update_Leaner()
 
     Update_Leaner()
 
 
 
 
 
@@ -870,11 +962,11 @@ def DTYPE():
 
     if column_list == ['']: column_list = []
 
     if column_list == ['']: column_list = []
 
     dtype = Dtype_Input.get()
 
     dtype = Dtype_Input.get()
 
     wrong = Dtype_Wrong.get()
 
     wrong = Dtype_Wrong.get()
 
-    if wrong != 'ignore': wrong = 'coerce'
 
     if type_:  # 软转换
 
     if type_:  # 软转换
 
+        if wrong != 'ignore': wrong = 'coerce'
 
         ML.Reasonable_Type(name, column_list, dtype, wrong)
 
         ML.Reasonable_Type(name, column_list, dtype, wrong)
 
     else:
 
     else:
 
-        ML.as_Type(name, column_list, dtype, wrong)
 
+        ML.as_Type(name, column_list, dtype, 'ignore')
 
     Updat_BOX()
 
     Updat_BOX()
 
 
 
 
 
@@ -1175,7 +1267,7 @@ def Done_Clean():
 
     global ML
 
     global ML
 
     name = get_Name()
 
     name = get_Name()
 
     Data = ML.Done_CleanFunc(name)
 
     Data = ML.Done_CleanFunc(name)
 
-    title = f'CoTan机器学习 表格:{name}.数据清洗'
 
+    title = f'CoTan数据处理 表格:{name}.数据清洗'
 
     Creat_TextSheet(Data, title)
 
     Creat_TextSheet(Data, title)
 
     Updat_BOX()
 
     Updat_BOX()
 
 
 
@@ -1206,7 +1298,7 @@ def Done_NaN():
 
     global Drop_Column
 
     global Drop_Column
 
     name = get_Name()
 
     name = get_Name()
 
     Data = ML.Dropna(name, True)
 
     Data = ML.Dropna(name, True)
 
-    title = f'CoTan机器学习 表格:{name}.NaN'
 
+    title = f'CoTan数据处理 表格:{name}.NaN'
 
     Creat_TextSheet(Data, title)
 
     Creat_TextSheet(Data, title)
 
     Updat_BOX()
 
     Updat_BOX()
 
 
 
@@ -1215,7 +1307,7 @@ def is_Na():
 
     global Bool_E
 
     global Bool_E
 
     name = get_Name()
 
     name = get_Name()
 
     Data = ML.is_Na(name)
 
     Data = ML.is_Na(name)
 
-    title = f'CoTan机器学习 表格:{name}.NaN'
 
+    title = f'CoTan数据处理 表格:{name}.NaN'
 
     Creat_TextSheet(Data, title)
 
     Creat_TextSheet(Data, title)
 
     Updat_BOX()
 
     Updat_BOX()
 
 
 
@@ -1226,7 +1318,7 @@ def Make_BoolSheet():
 
     name = get_Name()
 
     name = get_Name()
 
     Data = ML.Done_Bool(name, Bool_Exp, True)
 
     Data = ML.Done_Bool(name, Bool_Exp, True)
 
     print(Data)
 
     print(Data)
 
-    title = f'CoTan机器学习 表格:{name} 布尔化'
 
+    title = f'CoTan数据处理 表格:{name} 布尔化'
 
     Creat_TextSheet(Data, title)
 
     Creat_TextSheet(Data, title)
 
     Updat_BOX()
 
     Updat_BOX()
 
 
 
@@ -1242,7 +1334,7 @@ def Del_Data():
 
         Data = ML.Delete(name, Column, Row, new)
 
         Data = ML.Delete(name, Column, Row, new)
 
     except:
 
     except:
 
         Data = 'None 你的操作不被允许'
 
         Data = 'None 你的操作不被允许'
 
-    title = f'CoTan机器学习 表格:{name}'
 
+    title = f'CoTan数据处理 表格:{name}'
 
     Creat_TextSheet(Data, title)
 
     Creat_TextSheet(Data, title)
 
     Updat_BOX()
 
     Updat_BOX()
 
 
 
@@ -1301,17 +1393,28 @@ def Slice_Data():
 
         Data = ML.get_Clice(name, Column, Row, U, new)
 
         Data = ML.get_Clice(name, Column, Row, U, new)
 
     except:
 
     except:
 
         Data = 'None 你的操作不被允许'
 
         Data = 'None 你的操作不被允许'
 
-    title = f'CoTan机器学习 表格:{name}'
 
+    title = f'CoTan数据处理 表格:{name}'
 
     Creat_TextSheet(Data, title)
 
     Creat_TextSheet(Data, title)
 
     Updat_BOX()
 
     Updat_BOX()
 
 
 
 
 
+def Sample_Data():
 
+    global ML,Ascending_New
 
+    name = get_Name()
 
+    new = bool(Ascending_New.get())
 
+    Data = ML.Sample(name, new)
 
+    title = f'CoTan数据处理 打乱表格:{name}'
 
+    Creat_TextSheet(Data, title)
 
+    Updat_BOX()
 
+
 
+
 
+
 
 def Stored_Value():
 
 def Stored_Value():
 
-    global ML, Stored_List
 
+    global ML, Stored_List,Ascending_New
 
     name = get_Name()
 
     name = get_Name()
 
     new = bool(Ascending_New.get())
 
     new = bool(Ascending_New.get())
 
     Data = ML.Stored_Valuse(name, Stored_List, new)
 
     Data = ML.Stored_Valuse(name, Stored_List, new)
 
-    title = f'CoTan机器学习 表格:{name}.Stored'
 
+    title = f'CoTan数据处理 表格:{name}.Stored'
 
     Creat_TextSheet(Data, title)
 
     Creat_TextSheet(Data, title)
 
     Updat_BOX()
 
     Updat_BOX()
 
 
 
@@ -1355,7 +1458,7 @@ def Stored_Column():  # 行
 
     a = not bool(Ascending_Type.get())
 
     a = not bool(Ascending_Type.get())
 
     new = bool(Ascending_New.get())
 
     new = bool(Ascending_New.get())
 
     Data = ML.Sorted(name, False, new, a)
 
     Data = ML.Sorted(name, False, new, a)
 
-    title = f'CoTan机器学习 表格:{name}.Stored by Column'
 
+    title = f'CoTan数据处理 表格:{name}.Stored by Column'
 
     Creat_TextSheet(Data, title)
 
     Creat_TextSheet(Data, title)
 
     Updat_BOX()
 
     Updat_BOX()
 
 
 
@@ -1366,7 +1469,7 @@ def Stored_Row():  # 行
 
     new = bool(Ascending_New.get())
 
     new = bool(Ascending_New.get())
 
     a = not bool(Ascending_Type.get())
 
     a = not bool(Ascending_Type.get())
 
     Data = ML.Sorted(name, True, new, a)
 
     Data = ML.Sorted(name, True, new, a)
 
-    title = f'CoTan机器学习 表格:{name}.Stored by Row'
 
+    title = f'CoTan数据处理 表格:{name}.Stored by Row'
 
     Creat_TextSheet(Data, title)
 
     Creat_TextSheet(Data, title)
 
     Updat_BOX()
 
     Updat_BOX()
 
 
 
@@ -1376,7 +1479,7 @@ def T():
 
     name = get_Name()
 
     name = get_Name()
 
     new = bool(Ascending_New.get())
 
     new = bool(Ascending_New.get())
 
     Data = ML.T(name, new)
 
     Data = ML.T(name, new)
 
-    title = f'CoTan机器学习 表格:{name}.T'
 
+    title = f'CoTan数据处理 表格:{name}.T'
 
     Creat_TextSheet(Data, title)
 
     Creat_TextSheet(Data, title)
 
     Updat_BOX()
 
     Updat_BOX()
 
 
 
@@ -1398,7 +1501,7 @@ def Show_describe():
 
     global ML, Des_Bool
 
     global ML, Des_Bool
 
     Des = bool(Des_Bool.get())
 
     Des = bool(Des_Bool.get())
 
     name = get_Name()
 
     name = get_Name()
 
-    title = f'CoTan机器学习 表格:{name}_describe'
 
+    title = f'CoTan数据处理 表格:{name}_describe'
 
     Data = str(ML.Describe(name, Des))
 
     Data = str(ML.Describe(name, Des))
 
     Creat_TextSheet(Data, title)
 
     Creat_TextSheet(Data, title)
 
     Updat_BOX()
 
     Updat_BOX()
 
@@ -1407,7 +1510,7 @@ def Show_describe():
 
 def Write_Sheet():
 
 def Write_Sheet():
 
     global ML, top
 
     global ML, top
 
     name = get_Name()
 
     name = get_Name()
 
-    title = f'CoTan机器学习 表格:{name}'
 
+    title = f'CoTan数据处理 表格:{name}'
 
     Data = str(ML.get_Sheet(name))
 
     Data = str(ML.get_Sheet(name))
 
     Creat_TextSheet(Data, title)
 
     Creat_TextSheet(Data, title)
 
 
 
@@ -1442,6 +1545,18 @@ def Updat_IndexBOX(get_Index):
 
     Index_BOX.insert(tkinter.END, *get_Index)
 
     Index_BOX.insert(tkinter.END, *get_Index)
 
 
 
 
 
+def Show_One():
 
+    global PATH, to_HTML_Type
 
+    Dic = f'{PATH}/$Show_Sheet.html'
 
+    try:
 
+        name = get_Name()
 
+        if name == None: raise Exception
 
+        ML.to_Html_One(name, Dic)
 
+        webbrowser.open(Dic)
 
+    except:
 
+        # pass
 
+        raise
 
+
 
 def Show():
 
 def Show():
 
     global PATH, to_HTML_Type
 
     global PATH, to_HTML_Type
 
     Dic = f'{PATH}/$Show_Sheet.html'
 
     Dic = f'{PATH}/$Show_Sheet.html'
 
@@ -1451,44 +1566,46 @@ def Show():
 
         ML.to_Html(name, Dic, to_HTML_Type.get())
 
         ML.to_Html(name, Dic, to_HTML_Type.get())
 
         webbrowser.open(Dic)
 
         webbrowser.open(Dic)
 
     except:
 
     except:
 
-        raise
 
+        pass
 
 
 
 
 
-def Add_CSV():
 
+def to_CSV():
 
     global top, Seq_Input, Code_Input, str_must, Index_must
 
     global top, Seq_Input, Code_Input, str_must, Index_must
 
+    Dic = asksaveasfilename(title='选择保存的CSV', filetypes=[("CSV", ".csv")])
 
+    Seq = Seq_Input.get()
 
+    name = get_Name()
 
+    ML.to_CSV(name,Dic,Seq)
 
+    Updat_BOX()
 
+
 
+
 
+def Add_CSV():
 
+    global top, Seq_Input, Code_Input, str_must, Index_must,name_Input
 
     Dic = askopenfilename(title='选择载入的CSV', filetypes=[("CSV", ".csv")])
 
     Dic = askopenfilename(title='选择载入的CSV', filetypes=[("CSV", ".csv")])
 
+    if Dic == '':return False
 
     Seq = Seq_Input.get()
 
     Seq = Seq_Input.get()
 
     Codeing = Code_Input.get()
 
     Codeing = Code_Input.get()
 
     str_ = bool(str_must.get())
 
     str_ = bool(str_must.get())
 
     Index = bool(Index_must.get())
 
     Index = bool(Index_must.get())
 
+    name = name_Input.get().replace(' ','')
 
+    if name == '':
 
+        name = os.path.splitext(os.path.split(Dic)[1])[0]
 
+        print(name)
 
     if Codeing == '':
 
     if Codeing == '':
 
         with open(Dic, 'rb') as f:
 
         with open(Dic, 'rb') as f:
 
             Codeing = chardet.detect(f.read())['encoding']
 
             Codeing = chardet.detect(f.read())['encoding']
 
-            print(Codeing)
 
     if Seq == '': Seq = ','
 
     if Seq == '': Seq = ','
 
-    ML.Add_CSV(Dic, '', Seq, Codeing, str_, Index)
 
+    ML.Add_CSV(Dic, name, Seq, Codeing, str_, Index)
 
     Updat_BOX()
 
     Updat_BOX()
 
 
 
 
 
 def Add_Python():
 
 def Add_Python():
 
     global top, Seq_Input, Code_Input, str_must, Index_must
 
     global top, Seq_Input, Code_Input, str_must, Index_must
 
     Dic = askopenfilename(title='选择载入的py', filetypes=[("Python", ".py"), ("Txt", ".txt")])
 
     Dic = askopenfilename(title='选择载入的py', filetypes=[("Python", ".py"), ("Txt", ".txt")])
 
+    name = name_Input.get().replace(' ','')
 
+    if name == '':
 
+        name = os.path.splitext(os.path.split(Dic)[1])[0]
 
     with open(Dic, 'r') as f:
 
     with open(Dic, 'r') as f:
 
-        ML.Add_Python(f.read(), '')
 
-    Updat_BOX()
 
-
 
-
 
-def Add_Html():
 
-    global top, Seq_Input, Code_Input, str_must, Index_must
 
-    Dic = askopenfilename(title='选择载入的Html', filetypes=[("CSV", ".csv")])
 
-    Codeing = Code_Input.get()
 
-    str_ = bool(str_must.get())
 
-    Index = bool(Index_must.get())
 
-    if Codeing == '':
 
-        with open(Dic, 'rb') as f:
 
-            Codeing = chardet.detect(f.read())['encoding']
 
-            print(Codeing)
 
-    ML.Add_Html(Dic, '', Codeing, str_, Index)
 
+        ML.Add_Python(f.read(), name)
 
     Updat_BOX()
 
     Updat_BOX()
 
 
 
 
 
@@ -1508,7 +1625,3 @@ def Updat_BOX():
 
     Form_List = ML.get_FormList()
 
     Form_List = ML.get_FormList()
 
     Form_BOX.delete(0, tkinter.END)
 
     Form_BOX.delete(0, tkinter.END)
 
     Form_BOX.insert(tkinter.END, *Form_List)
 
     Form_BOX.insert(tkinter.END, *Form_List)
 
-
 
-
 
-if __name__ == '__main__':
 
-    Machine_learning()

+ 2 - 0
Data_Science/__init__.py
مشاهده فایل 

@@ -0,0 +1,2 @@
 
+from .Me_learn import Machine_learning
 
+print('数据科学模块加载完成...')

BIN
Data_Science/__pycache__/Learn.cpython-37.pyc
مشاهده فایل


BIN
Data_Science/__pycache__/Me_learn.cpython-37.pyc
مشاهده فایل


BIN
Data_Science/__pycache__/__init__.cpython-37.pyc
مشاهده فایل


+ 224 - 0
GIT.py
مشاهده فایل 

@@ -0,0 +1,224 @@
 
+import tkinter
 
+import tkinter.messagebox
 
+from tkinter.filedialog import askopenfilename, asksaveasfilename,askdirectory,askopenfilenames
 
+import os
 
+import Git_Ctrl
 
+from tkinter.scrolledtext import ScrolledText
 
+
 
+Git = Git_Ctrl.git_Ctrol()#需要去掉
 
+
 
+def Main():
 
+    global top,Git,PATH,bg,bbg,fg,Git_List,Last_Name
 
+    PATH = os.getcwd()
 
+    Git = Git_Ctrl.git_Ctrol()
 
+    Git_List = []
 
+    top = tkinter.Tk()
 
+    Last_Name = None
 
+    bg = '#FFFAFA'  # 主颜色
 
+    bbg = '#FFFAFA'  # 按钮颜色
 
+    fg = '#000000'  # 文字颜色
 
+    top["bg"] = bg
 
+    FONT = ('黑体', 11)  # 设置字体
 
+    top.title('CoTan仓库管理器')
 
+    top.resizable(width=False, height=False)
 
+    top.geometry('+10+10')  # 设置所在位置
 
+
 
+    width_B = 13  # 标准宽度
 
+    height_B = 2
 
+    a_y = 0
 
+    a_x = 0
 
+
 
+    global clone_url
 
+    a_y += 1
 
+    tkinter.Label(top, text='克隆URL:', bg=bg, fg=fg, font=FONT, width=width_B, height=height_B).grid(column=a_x,row=a_y)
 
+    Dic_Var = tkinter.StringVar()#当前的Dic
 
+    clone_url = tkinter.Entry(top, width=width_B * 2, textvariable=Dic_Var)
 
+    clone_url.grid(column=a_x+1, row=a_y, columnspan=2, sticky=tkinter.E + tkinter.W)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='克隆仓库', command=clone_git, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y, sticky=tkinter.E + tkinter.W)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='打开仓库', command=init_git, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x+1, row=a_y, sticky=tkinter.E + tkinter.W)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='查看文件', command=get_Git_Dir, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x+2, row=a_y, sticky=tkinter.E + tkinter.W)
 
+
 
+    global Git_Box,Git_Dir
 
+    a_y += 1
 
+    Git_Box = tkinter.Listbox(top, width=width_B * 3, height=height_B * 5)
 
+    Git_Box.grid(column=a_x, row=a_y, columnspan=3, rowspan=5, sticky=tkinter.E + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 5
 
+    tkinter.Label(top, text='【仓库文件列表】', bg=bg, fg=fg, font=FONT, width=width_B * 3, height=height_B).grid(
 
+        column=a_x,columnspan=3,row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)  # 设置说明
 
+
 
+    a_y += 1
 
+    Git_Dir = tkinter.Listbox(top, width=width_B * 3, height=height_B * 5)
 
+    Git_Dir.grid(column=a_x, row=a_y, columnspan=3, rowspan=5, sticky=tkinter.E + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 5
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='添加暂存区文件', command=Add_File, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y, sticky=tkinter.E + tkinter.W)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='移除暂存区文件', command=init_git, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x+1, row=a_y, sticky=tkinter.E + tkinter.W)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='提交到git', command=Commit_File, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x+2, row=a_y, sticky=tkinter.E + tkinter.W)
 
+
 
+    global commit_m,head,master
 
+    a_y += 1
 
+    tkinter.Label(top, text='提交描述:', bg=bg, fg=fg, font=FONT, width=width_B, height=height_B).grid(column=a_x,row=a_y)
 
+    commit_m = tkinter.Entry(top, width=width_B * 2)
 
+    commit_m.grid(column=a_x + 1, row=a_y, columnspan=2, sticky=tkinter.E + tkinter.W)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='查看执行日志', command=lambda :not_Args(Git.reflog), font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y, sticky=tkinter.E + tkinter.W)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='查看文件日志', command=lambda :not_Args(Git.log), font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x+1, row=a_y, sticky=tkinter.E + tkinter.W)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='查看状态', command=lambda :not_Args(Git.status), font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x+2, row=a_y, sticky=tkinter.E + tkinter.W)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='版本回退', command=Back_version, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y, sticky=tkinter.E + tkinter.W)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='文件回退', command=Back_File, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x+1, row=a_y, sticky=tkinter.E + tkinter.W)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='删除文件', command=rm_file, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x+2, row=a_y, sticky=tkinter.E + tkinter.W)
 
+
 
+    a_y += 1
 
+    tkinter.Label(top, text='diff分支:', bg=bg, fg=fg, font=FONT, width=width_B, height=height_B).grid(column=a_x,row=a_y)
 
+    master = tkinter.Entry(top, width=width_B * 2)
 
+    master.grid(column=a_x + 1, row=a_y, columnspan=2, sticky=tkinter.E + tkinter.W)
 
+
 
+    a_y += 1
 
+    tkinter.Label(top, text='回退版本号:', bg=bg, fg=fg, font=FONT, width=width_B, height=height_B).grid(column=a_x,row=a_y)
 
+    head = tkinter.Entry(top, width=width_B * 2)
 
+    head.grid(column=a_x + 1, row=a_y, columnspan=2, sticky=tkinter.E + tkinter.W)
 
+
 
+    top.mainloop()
 
+
 
+def rm_file():
 
+    global Git,head
 
+    dic = askopenfilenames(title=f'选择要删除的文件(取消为全选)')
 
+    if dic == '': return False
 
+    do_Sys(Git.rm,(get_Name(),dic))
 
+    update_Git_Dir()
 
+
 
+def Back_File():
 
+    global Git,head
 
+    dic = askopenfilenames(title=f'选择要add的文件(取消为全选)')
 
+    if dic == '': return False
 
+    do_Sys(Git.checkout_version,(get_Name(),dic))
 
+    update_Git_Dir()
 
+
 
+def Back_version():
 
+    global Git,head
 
+    HEAD = head.get()
 
+    if HEAD == '': HEAD = 'HEAD~1'
 
+    do_Sys(Git.back_version,(get_Name(),HEAD))
 
+    update_Git_Dir()
 
+
 
+def do_Sys(func,args,name='CoTan Git'):
 
+    p = func(*args)
 
+    p.wait()
 
+    if p.returncode != 0: print(p.returncode)
 
+    else:
 
+        print('success')
 
+    data = ''
 
+    while True:
 
+        i = p.stdout.readline().decode('utf-8')
 
+        if i == '': break
 
+        data += i
 
+    if data.replace('\n','').replace(' ','') != '':show(data,name)
 
+
 
+
 
+def not_Args(func):
 
+    global Git
 
+    name = get_Name()
 
+    do_Sys(func,(name,))
 
+    update_Git_Dir()
 
+
 
+def Commit_File():
 
+    global Git,commit_m
 
+    m = commit_m.get()
 
+    if m.replace(' ','') == '':
 
+        tkinter.messagebox.showinfo('警告!', '非常遗憾，我不同意你commit而不添加任何描述！\n描述是很重要的！')
 
+        return False
 
+    name = get_Name()
 
+    do_Sys(Git.commit_File,(name,m))
 
+    update_Git_Dir()
 
+
 
+def Diff_File():
 
+    global Git,master
 
+    MASTER = master.get()
 
+    if MASTER == '':MASTER = 'HEAD'
 
+    do_Sys(Git.diff_File,(get_Name(),MASTER))
 
+    update_Git_Dir()
 
+
 
+def Add_File():
 
+    global Git,Last_Name
 
+    dic = askopenfilenames(title=f'选择要add的文件(取消为全选)')
 
+    if dic == '':dic = '*'
 
+    do_Sys(Git.add_File,(get_Name(),dic))
 
+    update_Git_Dir()
 
+
 
+def update_Git_Dir():
 
+    global Last_Name
 
+    if Last_Name == None:return False
 
+    Git_dir(Last_Name)
 
+
 
+def get_Git_Dir():
 
+    name = get_Name()
 
+    Git_dir(name)
 
+
 
+def Git_dir(name):
 
+    global Git, Git_Dir,Last_Name
 
+    dir_list = Git.get_Dir(name)
 
+    Git_Dir.delete(0,tkinter.END)
 
+    Git_Dir.insert(tkinter.END,*dir_list)
 
+    Last_Name = name
 
+
 
+def clone_git():#克隆仓库
 
+    global clone_url
 
+    new_Dic = askdirectory(title = '选择仓库地址')
 
+    if new_Dic == '':return False
 
+    Git.Clone_init(new_Dic, clone_url.get())
 
+    Updata_GitBox()
 
+
 
+def init_git():#创建仓库
 
+    global Git
 
+    new_Dic = askdirectory(title = '选择仓库地址')
 
+    if new_Dic == '':return False
 
+    Git.Add_init(new_Dic,)
 
+    Updata_GitBox()
 
+
 
+def get_Name():  # 获得名字统一接口
 
+    global Git,Git_List,Git_Box
 
+    try:
 
+        return Git_List[Git_Box.curselection()[0]]
 
+    except:
 
+        try:
 
+            return Git_List[0]
 
+        except:
 
+            return None
 
+
 
+def Updata_GitBox():
 
+    global Git,Git_List,Git_Box
 
+    Git_List = list(Git.get_git_Dic().keys())
 
+    Git_Box.delete(0,tkinter.END)
 
+    Git_Box.insert(tkinter.END,*Git_List)
 
+
 
+def show(data,name):
 
+    global bg, ft1
 
+    new_top = tkinter.Toplevel(bg=bg)
 
+    new_top.title(name)
 
+    new_top.geometry('+10+10')  # 设置所在位置
 
+    text = ScrolledText(new_top, font=('黑体', 11), height=50)
 
+    text.pack(fill=tkinter.BOTH)
 
+    text.insert('0.0', data)
 
+    text.config(state=tkinter.DISABLED)
 
+    new_top.resizable(width=False, height=False)
 
+
 
+if __name__ == '__main__':
 
+    Main()

+ 133 - 0
Git_Ctrl.py
مشاهده فایل 

@@ -0,0 +1,133 @@
 
+# -*- coding: <encoding name> -*-
 
+from git import Repo
 
+from os.path import split,exists
 
+import os
 
+import subprocess
 
+
 
+sys_seeting = dict(shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE)
 
+git_path = 'git'
 
+
 
+class git_Repo:#git的基类
 
+    def __init__(self,Dic,name,*args,**kwargs):
 
+        self.Repo_Dic = Dic  # 仓库地址
 
+        self.url = None
 
+        if not exists(Dic + r'/.git'):  # 是否为git 仓库
 
+            subprocess.Popen(f'{git_path} init',cwd=self.Repo_Dic,**sys_seeting).wait()
 
+        self.repo = Repo(Dic)
 
+        self.name = name
 
+
 
+    def Flie_List(self,file_list,is_file=True,pat=' '):
 
+        if file_list == '*':
 
+            file = '*'
 
+        else:
 
+            file_ = []
 
+            for i in file_list:
 
+                if i[:len(self.Repo_Dic)] == self.Repo_Dic:
 
+                    file_.append(i[len(self.Repo_Dic) + 1:])  # +1是为了去除/
 
+            if not is_file:return file_
 
+            file = pat.join(file_)
 
+        return file
 
+
 
+    def dir_list(self,all=True):
 
+        listfile = []
 
+        if all:
 
+            listfile += [f'[当前分支] {self.repo.active_branch} 工作区{"不" if self.repo.is_dirty() else ""}干净 -> {self.name}']
 
+        listfile += [f'{"[配置文件]" if i == ".git" else "[未跟踪]"if i in self.repo.untracked_files else "[已跟踪]"} {i}'
 
+                     for i in os.listdir(self.Repo_Dic)]
 
+        return listfile
 
+
 
+    def Add_File(self,file_list):
 
+        file = self.Flie_List(file_list)
 
+        return subprocess.Popen(f'{git_path} add {file}',cwd=self.Repo_Dic,**sys_seeting)
 
+
 
+    def Commit_File(self,m):
 
+        return subprocess.Popen(f'{git_path} commit -m "{m}"',cwd=self.Repo_Dic,**sys_seeting)
 
+
 
+    def Status(self):#执行status
 
+        return subprocess.Popen(f'{git_path} status',cwd=self.Repo_Dic,**sys_seeting)
 
+
 
+    def Log(self):#执行log
 
+        return subprocess.Popen(f'{git_path} log',cwd=self.Repo_Dic,**sys_seeting)
 
+
 
+    def refLog(self):#执行reflog
 
+        return subprocess.Popen(f'{git_path} reflog',cwd=self.Repo_Dic,**sys_seeting)
 
+
 
+    def Diff_File(self,MASTER='HEAD'):#执行diff
 
+        return subprocess.Popen(f'{git_path} diff {MASTER}',cwd=self.Repo_Dic,**sys_seeting)
 
+
 
+    def reset(self,HEAD='HEAD~1'):
 
+        return subprocess.Popen(f'{git_path} reset --hard {HEAD}',cwd=self.Repo_Dic,**sys_seeting)
 
+
 
+    def checkout(self,file_list):
 
+        if len(file_list) >= 1:#多于一个文件，不用--，空格
 
+            file = self.Flie_List(file_list,pat=' ')
 
+            return subprocess.Popen(f'{git_path} checkout {file}',cwd=self.Repo_Dic,**sys_seeting)
 
+        elif len(file_list) == 1:
 
+            return subprocess.Popen(f'{git_path} checkout -- {file_list[0]}', cwd=self.Repo_Dic, **sys_seeting)
 
+        else:
 
+            return subprocess.Popen(f'{git_path} checkout *', cwd=self.Repo_Dic, **sys_seeting)
 
+
 
+    def rm(self,file_list):#删除版本库中的文件
 
+        file = self.Flie_List(file_list)
 
+        return subprocess.Popen(f'{git_path} rm {file}', cwd=self.Repo_Dic,**sys_seeting)
 
+
 
+class Clone_git(git_Repo):#Clone一个git
 
+    def __init__(self,Dic,url,name,*args,**kwargs):
 
+        super(Clone_git, self).__init__(Dic,name, *args, **kwargs)
 
+        self.url = url
 
+        Repo.clone_from(url=url,to_path=Dic)
 
+        self.git = self.repo.git
 
+        self.index = self.repo.index
 
+
 
+class git_Ctrol:
 
+    def __init__(self):
 
+        self.git_Dic = {}#名字-文件位置
 
+        self.gitType_Dic = {}#名字-类型
 
+
 
+    def Add_init(self,Dic,**kwargs):
 
+        name = split(Dic)[-1]
 
+        git = git_Repo(Dic,name)
 
+        self.git_Dic[name] = git
 
+        self.gitType_Dic[name] = 'init'
 
+
 
+    def Clone_init(self,Dic,url,**kwargs):
 
+        name = split(Dic)[-1]
 
+        git = Clone_git(Dic,url,name)
 
+        self.git_Dic[name] = git
 
+        self.gitType_Dic[name] = 'clone'
 
+
 
+    def get_git(self,name):
 
+        return self.git_Dic[name]
 
+
 
+    def get_git_Dic(self):
 
+        return self.git_Dic.copy()
 
+
 
+    def get_Dir(self,name):
 
+        return self.get_git(name).dir_list()
 
+
 
+    def add_File(self,name,dic_list):
 
+        return self.get_git(name).Add_File(dic_list)
 
+
 
+    def commit_File(self,name,m):
 
+        return self.get_git(name).Commit_File(m)
 
+
 
+    def log(self,name):
 
+        return self.get_git(name).Log()
 
+
 
+    def reflog(self,name):
 
+        return self.get_git(name).refLog()
 
+
 
+    def status(self,name):
 
+        return self.get_git(name).Status()
 
+
 
+    def diff_File(self,name,MASTER):
 
+        return self.get_git(name).Diff_File(MASTER)
 
+
 
+    def back_version(self,name,HEAD):
 
+        return self.get_git(name).reset(HEAD)
 
+
 
+    def checkout_version(self,name,file):
 
+        return self.get_git(name).checkout(file)
 
+
 
+    def rm(self,name,file):
 
+        return self.get_git(name).rm(file)

+ 1 - 1
HSCH/__init__.py
مشاهده فایل 

@@ -1,3 +1,3 @@
 
 from HSCH.Func_Matlib import Func_Control as Func_Control
 
 from HSCH.Func_Matlib import Func_Control as Func_Control
 
 from HSCH.Func_advanced import Advanced_Control as Advanced_Control
 
 from HSCH.Func_advanced import Advanced_Control as Advanced_Control
 
-print('HSCH加载完毕...')
 
+print('函数工厂和函数测绘加载完毕...')

BIN
HSCH/__pycache__/__init__.cpython-37.pyc
مشاهده فایل


+ 46 - 41
Hello.py
مشاهده فایل 

@@ -1,53 +1,66 @@
 
-print('启动')
 
-from time import time
 
-print('开始加载')
 
-start = time()
 
-import tkinter
 
-print(f'图形加载完毕...')
 
-import tkinter.font as tkFont
 
-print(f'字体加载完毕...')
 
-from PIL import ImageTk, Image
 
-print(f'图片加载完毕...')
 
-import CGB,HSCH,DSGC
 
-from New_TK import DragWindow
 
 from multiprocessing import Process
 
 from multiprocessing import Process
 
-print(f'加载完毕...{round(time() - start,3)}s')
 
+
 
+def painting_board():
 
+    from CGB import Draw
 
+    Draw()
 
 
 
 def Draw():
 
 def Draw():
 
     global top,HTB
 
     global top,HTB
 
-    HTB = Process(target=CGB.Draw)
 
+    HTB = Process(target=painting_board)
 
     HTB.start()
 
     HTB.start()
 
-    # top.destroy()
 
-    # CGB.Draw()
 
-    # Main()
 
+
 
+def Data_Science():
 
+    from Data_Science import Machine_learning
 
+    Machine_learning()
 
+
 
+def SJKX():
 
+    global top,SJ
 
+    SJ = Process(target=Data_Science)
 
+    SJ.start()
 
+
 
+def Function_mapping():
 
+    from HSCH import Func_Control
 
+    Func_Control()
 
 
 
 def Hsch():
 
 def Hsch():
 
     global top,CH
 
     global top,CH
 
-    CH = Process(target=HSCH.Func_Control)
 
+    CH = Process(target=Function_mapping)
 
     CH.start()
 
     CH.start()
 
-    # top.destroy()
 
-    # HSCH.Func_Control()
 
-    # Main()
 
+
 
+def Function_factory():
 
+    from HSCH import Advanced_Control
 
+    Advanced_Control()
 
 
 
 def HSGC():
 
 def HSGC():
 
     global top,HsGC
 
     global top,HsGC
 
-    HsGC = Process(target=HSCH.Advanced_Control)
 
+    HsGC = Process(target=Function_factory)
 
     HsGC.start()
 
     HsGC.start()
 
-    # top.destroy()
 
-    # HSCH.Advanced_Control()
 
-    # Main()
 
+
 
+def Algebraic_factory():
 
+    from DSGC import Alg
 
+    Alg()
 
 
 
 def Dsgc():
 
 def Dsgc():
 
     global top,DsGC
 
     global top,DsGC
 
-    DsGC = Process(target=DSGC.Alg)
 
+    DsGC = Process(target=Algebraic_factory)
 
     DsGC.start()
 
     DsGC.start()
 
-    # top.destroy()
 
-    # DSGC.Alg()
 
-    # Main()
 
+
 
+def Machine_Learning():
 
+    from Machine_learning_analysis import Main
 
+    Main()
 
+
 
+def MLA():
 
+    global top, DsGC
 
+    Mla = Process(target=Machine_Learning())
 
+    Mla.start()
 
 
 
 def Main():
 
 def Main():
 
     global top
 
     global top
 
-    # top = tkinter.Tk()  # 设置屏幕
 
+    import tkinter
 
+    import tkinter.font as tkFont
 
+    from PIL import ImageTk, Image
 
+    from New_TK import DragWindow
 
+    print('加载完毕')
 
     top = DragWindow(alpha=0.97,width=1200,height=800)
 
     top = DragWindow(alpha=0.97,width=1200,height=800)
 
     ft = tkFont.Font(family='Comic Sans MS', size=20, weight=tkFont.BOLD)
 
     ft = tkFont.Font(family='Comic Sans MS', size=20, weight=tkFont.BOLD)
 
     ft1 = tkFont.Font(family='Comic Sans MS', size=16, weight=tkFont.BOLD)
 
     ft1 = tkFont.Font(family='Comic Sans MS', size=16, weight=tkFont.BOLD)
 
@@ -82,8 +95,8 @@ def Main():
 
     tkinter.Button(F1,text='函数测绘',cursor=bc,command=Hsch,height=2,font=ft2,bg=bg,activebackground=abg,bd=0,justify=tkinter.LEFT).grid(column =0,row = 7,sticky=tkinter.N + tkinter.E + tkinter.W)
 
     tkinter.Button(F1,text='函数测绘',cursor=bc,command=Hsch,height=2,font=ft2,bg=bg,activebackground=abg,bd=0,justify=tkinter.LEFT).grid(column =0,row = 7,sticky=tkinter.N + tkinter.E + tkinter.W)
 
     tkinter.Button(F1, text='函数工厂',cursor=bc,command=HSGC, height=2, font=ft2, bg=bg,activebackground=abg, bd=0, justify=tkinter.LEFT).grid(column=0, row=8,sticky=tkinter.N + tkinter.E + tkinter.W)
 
     tkinter.Button(F1, text='函数工厂',cursor=bc,command=HSGC, height=2, font=ft2, bg=bg,activebackground=abg, bd=0, justify=tkinter.LEFT).grid(column=0, row=8,sticky=tkinter.N + tkinter.E + tkinter.W)
 
     tkinter.Button(F1, text='代数工厂',cursor=bc,command=Dsgc, height=2, font=ft2, bg=bg,activebackground=abg, bd=0, justify=tkinter.LEFT).grid(column=0, row=9,sticky=tkinter.N + tkinter.E + tkinter.W)
 
     tkinter.Button(F1, text='代数工厂',cursor=bc,command=Dsgc, height=2, font=ft2, bg=bg,activebackground=abg, bd=0, justify=tkinter.LEFT).grid(column=0, row=9,sticky=tkinter.N + tkinter.E + tkinter.W)
 
-    tkinter.Button(F1, text='机器学习',cursor=bc, height=1, font=ft2, bg=bg,activebackground=abg, bd=0, justify=tkinter.LEFT).grid(column=0, row=10,sticky=tkinter.N + tkinter.E + tkinter.W)
 
-    tkinter.Button(F1, text='几何分析',cursor=bc, height=1, font=ft2, bg=bg,activebackground=abg, bd=0, justify=tkinter.LEFT).grid(column=0, row=11,sticky=tkinter.N + tkinter.E + tkinter.W)
 
+    tkinter.Button(F1, text='数据科学',cursor=bc,command=SJKX, height=1, font=ft2, bg=bg,activebackground=abg, bd=0, justify=tkinter.LEFT).grid(column=0, row=10,sticky=tkinter.N + tkinter.E + tkinter.W)
 
+    tkinter.Button(F1, text='机器学习',cursor=bc,command=MLA, height=1, font=ft2, bg=bg,activebackground=abg, bd=0, justify=tkinter.LEFT).grid(column=0, row=11,sticky=tkinter.N + tkinter.E + tkinter.W)
 
 
 
     abg='#F5FFFA'
 
     abg='#F5FFFA'
 
     tkinter.Label(F1, text='物化系统', bg=abg, font=ft1).grid(column=0, row=12, sticky=tkinter.W + tkinter.E)
 
     tkinter.Label(F1, text='物化系统', bg=abg, font=ft1).grid(column=0, row=12, sticky=tkinter.W + tkinter.E)
 
@@ -108,12 +121,4 @@ def Main():
 
     top.mainloop()
 
     top.mainloop()
 
 
 
 if __name__ == "__main__":
 
 if __name__ == "__main__":
 
-    HTB = Process(target=CGB.Draw)
 
-    CH = Process(target=HSCH.Func_Control)
 
-    HsGC = Process(target=HSCH.Advanced_Control)
 
-    DsGC = Process(target=DSGC.Alg)
 
-    Main()
 
-    # HTB.join()
 
-    # CH.join()
 
-    # HsGC.join()
 
-    # DsGC.join()
 
+    Main()

+ 3947 - 0
Machine_learning_analysis/Learn_Numpy.py
مشاهده فایل 

@@ -0,0 +1,3947 @@
 
+from os.path import split as path_split
 
+from os.path import exists,basename,splitext
 
+from os import mkdir,getcwd
 
+import tarfile
 
+import pickle
 
+import joblib
 
+from pyecharts.globals import CurrentConfig
 
+CurrentConfig.ONLINE_HOST = f"{getcwd()}/assets/"
 
+from pyecharts.components import Table as Table_Fisrt#绘制表格
 
+from pyecharts.components import Image
 
+from pyecharts import options as opts
 
+from random import randint
 
+from pyecharts.charts import *
 
+from pyecharts.charts import Tab as tab_First
 
+from pyecharts.options.series_options import JsCode
 
+from scipy.cluster.hierarchy import dendrogram, ward
 
+import matplotlib.pyplot as plt
 
+from pandas import DataFrame,read_csv
 
+import numpy as np
 
+import re
 
+from sklearn.model_selection import train_test_split
 
+from sklearn.linear_model import *
 
+from sklearn.neighbors import KNeighborsClassifier,KNeighborsRegressor
 
+from sklearn.tree import DecisionTreeClassifier,DecisionTreeRegressor,export_graphviz
 
+from sklearn.ensemble import (RandomForestClassifier,RandomForestRegressor,GradientBoostingClassifier,
 
+                              GradientBoostingRegressor)
 
+from sklearn.metrics import *
 
+from sklearn.feature_selection import *
 
+from sklearn.preprocessing import *
 
+from sklearn.impute import SimpleImputer
 
+from sklearn.decomposition import PCA, IncrementalPCA,KernelPCA,NMF
 
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
 
+from sklearn.svm import SVC,SVR#SVC是svm分类，SVR是svm回归
 
+from sklearn.neural_network import MLPClassifier,MLPRegressor
 
+from sklearn.manifold import TSNE
 
+from sklearn.cluster import KMeans,AgglomerativeClustering,DBSCAN
 
+from scipy import optimize
 
+from scipy.fftpack import fft,ifft,ifftn,fftn#快速傅里叶变换
 
+
 
+
 
+#设置
 
+np.set_printoptions(threshold=np.inf)
 
+global_Set = dict(toolbox_opts=opts.ToolboxOpts(is_show=True),legend_opts=opts.LegendOpts(pos_bottom='3%',type_='scroll'))
 
+global_Leg = dict(toolbox_opts=opts.ToolboxOpts(is_show=True),legend_opts=opts.LegendOpts(is_show=False))
 
+Label_Set = dict(label_opts=opts.LabelOpts(is_show=False))
 
+
 
+More_Global = False#是否使用全部特征绘图
 
+All_Global = True#是否导出charts
 
+CSV_Global = True#是否导出CSV
 
+CLF_Global = True#是否导出模型
 
+TAR_Global = True#是否打包tar
 
+NEW_Global = True#是否新建目录
 
+
 
+class Tab(tab_First):
 
+    def __init__(self, *args,**kwargs):
 
+        super(Tab, self).__init__(*args,**kwargs)
 
+        self.element = {}#记录tab组成元素 name:charts
 
+
 
+    def add(self, chart, tab_name):
 
+        self.element[tab_name] = chart
 
+        return super(Tab, self).add(chart, tab_name)
 
+
 
+    def render(self,path: str = "render.html",template_name: str = "simple_tab.html",*args,**kwargs,) -> str:
 
+        if All_Global:
 
+            Dic = path_split(path)[0]
 
+            for i in self.element:
 
+                self.element[i].render(Dic + '/' + i + '.html')
 
+        return super(Tab, self).render(path,template_name,*args,**kwargs)
 
+
 
+class Table(Table_Fisrt):
 
+    def __init__(self,*args,**kwargs):
 
+        super(Table, self).__init__(*args,**kwargs)
 
+        self.HEADERS = []
 
+        self.ROWS = [[]]
 
+
 
+    def add(self, headers, rows, attributes = None):
 
+        if len(rows) == 1:
 
+            new_headers = ['数据类型','数据']
 
+            new_rows = list(zip(headers,rows[0]))
 
+            self.HEADERS = new_headers
 
+            self.ROWS = new_rows
 
+            return super().add(new_headers,new_rows,attributes)
 
+        else:
 
+            self.HEADERS = headers
 
+            self.ROWS = rows
 
+            return super().add(headers, rows, attributes)
 
+
 
+    def render(self,path= "render.html",*args,**kwargs,) -> str:
 
+        if CSV_Global:
 
+            Dic,name = path_split(path)
 
+            name = splitext(name)[0]
 
+            try:
 
+                DataFrame(self.ROWS,columns = self.HEADERS).to_csv(Dic + '/' + name + '.csv')
 
+            except:
 
+                pass
 
+        return super().render(path,*args,**kwargs)
 
+
 
+def make_list(first,end,num=35):
 
+    n = num / (end - first)
 
+    if n == 0: n = 1
 
+    re = []
 
+    n_first = first * n
 
+    n_end = end * n
 
+    while n_first <= n_end:
 
+        cul = n_first / n
 
+        re.append(round(cul,2))
 
+        n_first += 1
 
+    return re
 
+
 
+def list_filter(list_,num=70):
 
+    #假设列表已经不重复
 
+    if len(list_) <= num:return list_
 
+    n = int(num / len(list_))
 
+    re = list_[::n]
 
+    return re
 
+
 
+def Prediction_boundary(x_range,x_means,Predict_Func,Type):#绘制回归型x-x热力图
 
+    #r是绘图大小列表,x_means是其余值,Predict_Func是预测方法回调
 
+    # a-特征x，b-特征x-1，c-其他特征
 
+    o_cList = []
 
+    if len(x_means) == 1:
 
+        return o_cList
 
+    for i in range(len(x_means)):
 
+        for j in range(len(x_means)):
 
+            if j <= i:continue
 
+            n_ra = x_range[j]
 
+            Type_ra = Type[j]
 
+            n_rb = x_range[i]
 
+            Type_rb = Type[i]
 
+            if Type_ra == 1:
 
+                ra = make_list(n_ra[0],n_ra[1],70)
 
+            else:
 
+                ra = list_filter(n_ra)#可以接受最大为70
 
+
 
+            if Type_rb == 1:
 
+                rb = make_list(n_rb[0],n_rb[1],35)
 
+            else:
 
+                rb = list_filter(n_rb)#可以接受最大为70
 
+            a = np.array([i for i in ra for _ in rb]).T
 
+            b = np.array([i for _ in ra for i in rb]).T
 
+            data = np.array([x_means for _ in ra for i in rb])
 
+            data[:, j] = a
 
+            data[:, i] = b
 
+            y_data = Predict_Func(data)[0].tolist()
 
+            value = [[float(a[i]), float(b[i]), y_data[i]] for i in range(len(a))]
 
+            c = (HeatMap()
 
+                 .add_xaxis(np.unique(a))
 
+                 .add_yaxis(f'数据', np.unique(b), value, **Label_Set)  # value的第一个数值是x
 
+                 .set_global_opts(title_opts=opts.TitleOpts(title='预测热力图'), **global_Leg,
 
+                                  yaxis_opts=opts.AxisOpts(is_scale=True, type_='category'),  # 'category'
 
+                                  xaxis_opts=opts.AxisOpts(is_scale=True, type_='category'),
 
+                                  visualmap_opts=opts.VisualMapOpts(is_show=True, max_=int(max(y_data))+1, min_=int(min(y_data)),
 
+                                                                    pos_right='3%'))#显示
 
+                 )
 
+            o_cList.append(c)
 
+    return o_cList
 
+
 
+def Prediction_boundary_More(x_range,x_means,Predict_Func,Type):#绘制回归型x-x热力图
 
+    #r是绘图大小列表,x_means是其余值,Predict_Func是预测方法回调
 
+    # a-特征x，b-特征x-1，c-其他特征
 
+    o_cList = []
 
+    if len(x_means) == 1:
 
+        return o_cList
 
+    for i in range(len(x_means)):
 
+        if i == 0:
 
+            continue
 
+        n_ra = x_range[i - 1]
 
+        Type_ra = Type[i - 1]
 
+        n_rb = x_range[i]
 
+        Type_rb = Type[i]
 
+        if Type_ra == 1:
 
+            ra = make_list(n_ra[0],n_ra[1],70)
 
+        else:
 
+            ra = list_filter(n_ra)#可以接受最大为70
 
+
 
+        if Type_rb == 1:
 
+            rb = make_list(n_rb[0],n_rb[1],35)
 
+        else:
 
+            rb = list_filter(n_rb)#可以接受最大为70
 
+        a = np.array([i for i in ra for _ in rb]).T
 
+        b = np.array([i for _ in ra for i in rb]).T
 
+        data = np.array([x_means for _ in ra for i in rb])
 
+        data[:, i - 1] = a
 
+        data[:, i] = b
 
+        y_data = Predict_Func(data)[0].tolist()
 
+        value = [[float(a[i]), float(b[i]), y_data[i]] for i in range(len(a))]
 
+        c = (HeatMap()
 
+             .add_xaxis(np.unique(a))
 
+             .add_yaxis(f'数据', np.unique(b), value, **Label_Set)  # value的第一个数值是x
 
+             .set_global_opts(title_opts=opts.TitleOpts(title='预测热力图'), **global_Leg,
 
+                              yaxis_opts=opts.AxisOpts(is_scale=True, type_='category'),  # 'category'
 
+                              xaxis_opts=opts.AxisOpts(is_scale=True, type_='category'),
 
+                              visualmap_opts=opts.VisualMapOpts(is_show=True, max_=int(max(y_data))+1, min_=int(min(y_data)),
 
+                                                                pos_right='3%'))#显示
 
+             )
 
+        o_cList.append(c)
 
+    return o_cList
 
+
 
+def Decision_boundary(x_range,x_means,Predict_Func,class_,Type,nono=False):#绘制分类型预测图x-x热力图
 
+    #r是绘图大小列表,x_means是其余值,Predict_Func是预测方法回调,class_是分类,add_o是可以合成的图
 
+    # a-特征x，b-特征x-1，c-其他特征
 
+    #规定，i-1是x轴，a是x轴，x_1是x轴
 
+    class_dict = dict(zip(class_,[i for i in range(len(class_))]))
 
+    if not nono:
 
+        v_dict = [{'min':-1.5,'max':-0.5,'label':'未知'}]#分段显示
 
+    else:v_dict = []
 
+    for i in class_dict:
 
+        v_dict.append({'min':class_dict[i]-0.5,'max':class_dict[i]+0.5,'label':str(i)})
 
+    o_cList = []
 
+    if len(x_means) == 1:
 
+        n_ra = x_range[0]
 
+        if Type[0] == 1:
 
+            ra = make_list(n_ra[0], n_ra[1], 70)
 
+        else:
 
+            ra = n_ra
 
+
 
+        a = np.array([i for i in ra]).reshape(-1,1)
 
+        y_data = Predict_Func(a)[0].tolist()
 
+        value = [[0,float(a[i]), class_dict.get(y_data[i], -1)] for i in range(len(a))]
 
+        c = (HeatMap()
 
+             .add_xaxis(['None'])
 
+             .add_yaxis(f'数据', np.unique(a), value, **Label_Set)  # value的第一个数值是x
 
+             .set_global_opts(title_opts=opts.TitleOpts(title='预测热力图'), **global_Leg,
 
+                              yaxis_opts=opts.AxisOpts(is_scale=True, type_='category'),  # 'category'
 
+                              xaxis_opts=opts.AxisOpts(is_scale=True, type_='category'),
 
+                              visualmap_opts=opts.VisualMapOpts(is_show=True, max_=max(class_dict.values()),
 
+                                                                min_=-1,
 
+                                                                is_piecewise=True, pieces=v_dict,
 
+                                                                orient='horizontal', pos_bottom='3%'))
 
+             )
 
+        o_cList.append(c)
 
+        return o_cList
 
+    #如果x_means长度不等于1则执行下面
 
+    for i in range(len(x_means)):
 
+        if i == 0:
 
+            continue
 
+
 
+        n_ra = x_range[i-1]
 
+        Type_ra = Type[i-1]
 
+        n_rb = x_range[i]
 
+        Type_rb = Type[i]
 
+        if Type_ra == 1:
 
+            ra = make_list(n_ra[0],n_ra[1],70)
 
+        else:
 
+            ra = n_ra
 
+
 
+        if Type_rb == 1:
 
+            rb = make_list(n_rb[0],n_rb[1],35)
 
+        else:
 
+            rb = n_rb
 
+        a = np.array([i for i in ra for _ in rb]).T
 
+        b = np.array([i for _ in ra for i in rb]).T
 
+        data = np.array([x_means for _ in ra for i in rb])
 
+        data[:, i - 1] = a
 
+        data[:, i] = b
 
+        y_data = Predict_Func(data)[0].tolist()
 
+        value = [[float(a[i]), float(b[i]), class_dict.get(y_data[i],-1)] for i in range(len(a))]
 
+        c = (HeatMap()
 
+             .add_xaxis(np.unique(a))
 
+             .add_yaxis(f'数据', np.unique(b), value, **Label_Set)#value的第一个数值是x
 
+             .set_global_opts(title_opts=opts.TitleOpts(title='预测热力图'), **global_Leg,
 
+                              yaxis_opts=opts.AxisOpts(is_scale=True,type_='category'),#'category'
 
+                              xaxis_opts=opts.AxisOpts(is_scale=True,type_='category'),
 
+                              visualmap_opts=opts.VisualMapOpts(is_show=True,max_=max(class_dict.values()),min_=-1,
 
+                              is_piecewise=True,pieces=v_dict,orient='horizontal',pos_bottom='3%'))
 
+             )
 
+        o_cList.append(c)
 
+    return o_cList
 
+
 
+def Decision_boundary_More(x_range,x_means,Predict_Func,class_,Type,nono=False):#绘制分类型预测图x-x热力图
 
+    #r是绘图大小列表,x_means是其余值,Predict_Func是预测方法回调,class_是分类,add_o是可以合成的图
 
+    # a-特征x，b-特征x-1，c-其他特征
 
+    #规定，i-1是x轴，a是x轴，x_1是x轴
 
+    class_dict = dict(zip(class_,[i for i in range(len(class_))]))
 
+    if not nono:
 
+        v_dict = [{'min':-1.5,'max':-0.5,'label':'未知'}]#分段显示
 
+    else:v_dict = []
 
+    for i in class_dict:
 
+        v_dict.append({'min':class_dict[i]-0.5,'max':class_dict[i]+0.5,'label':str(i)})
 
+    o_cList = []
 
+    if len(x_means) == 1:
 
+        return Decision_boundary(x_range,x_means,Predict_Func,class_,Type,nono)
 
+    #如果x_means长度不等于1则执行下面
 
+    for i in range(len(x_means)):
 
+        for j in range(len(x_means)):
 
+            if j <= i:continue
 
+
 
+            n_ra = x_range[j]
 
+            Type_ra = Type[j]
 
+            n_rb = x_range[i]
 
+            Type_rb = Type[i]
 
+            if Type_ra == 1:
 
+                ra = make_list(n_ra[0],n_ra[1],70)
 
+            else:
 
+                ra = n_ra
 
+
 
+            if Type_rb == 1:
 
+                rb = make_list(n_rb[0],n_rb[1],35)
 
+            else:
 
+                rb = n_rb
 
+            a = np.array([i for i in ra for _ in rb]).T
 
+            b = np.array([i for _ in ra for i in rb]).T
 
+            data = np.array([x_means for _ in ra for i in rb])
 
+            data[:, j] = a
 
+            data[:, i] = b
 
+            y_data = Predict_Func(data)[0].tolist()
 
+            value = [[float(a[i]), float(b[i]), class_dict.get(y_data[i],-1)] for i in range(len(a))]
 
+            c = (HeatMap()
 
+                 .add_xaxis(np.unique(a))
 
+                 .add_yaxis(f'数据', np.unique(b), value, **Label_Set)#value的第一个数值是x
 
+                 .set_global_opts(title_opts=opts.TitleOpts(title='预测热力图'), **global_Leg,
 
+                                  yaxis_opts=opts.AxisOpts(is_scale=True,type_='category'),#'category'
 
+                                  xaxis_opts=opts.AxisOpts(is_scale=True,type_='category'),
 
+                                  visualmap_opts=opts.VisualMapOpts(is_show=True,max_=max(class_dict.values()),min_=-1,
 
+                                  is_piecewise=True,pieces=v_dict,orient='horizontal',pos_bottom='3%'))
 
+                 )
 
+            o_cList.append(c)
 
+    return o_cList
 
+
 
+def SeeTree(Dic):
 
+    node_re = re.compile('^([0-9]+) \[label="(.+)"\] ;$')  # 匹配节点正则表达式
 
+    link_re = re.compile('^([0-9]+) -> ([0-9]+) (.*);$')  # 匹配节点正则表达式
 
+    node_Dict = {}
 
+    link_list = []
 
+
 
+    with open(Dic, 'r') as f:  # 貌似必须分开w和r
 
+        for i in f:
 
+            try:
 
+                get = re.findall(node_re, i)[0]
 
+                if get[0] != '':
 
+                    try:
 
+                        v = float(get[0])
 
+                    except:
 
+                        v = 0
 
+                    node_Dict[get[0]] = {'name': get[1].replace('\\n', '\n'), 'value': v, 'children': []}
 
+                    continue
 
+            except:
 
+                pass
 
+            try:
 
+                get = re.findall(link_re, i)[0]
 
+                if get[0] != '' and get[1] != '':
 
+                    link_list.append((get[0], get[1]))
 
+            except:
 
+                pass
 
+
 
+    father_list = []  # 已经有父亲的list
 
+    for i in link_list:
 
+        father = i[0]  # 父节点
 
+        son = i[1]  # 子节点
 
+        try:
 
+            node_Dict[father]['children'].append(node_Dict[son])
 
+            father_list.append(son)
 
+            if int(son) == 0: print('F')
 
+        except:
 
+            pass
 
+
 
+    father = list(set(node_Dict.keys()) - set(father_list))
 
+
 
+    c = (
 
+        Tree()
 
+            .add("", [node_Dict[father[0]]], is_roam=True)
 
+            .set_global_opts(title_opts=opts.TitleOpts(title="决策树可视化"),
 
+                             toolbox_opts=opts.ToolboxOpts(is_show=True))
 
+    )
 
+    return c
 
+
 
+def make_Tab(heard,row):
 
+    return Table().add(headers=heard, rows=row)
 
+
 
+def scatter(w_heard,w):
 
+    c = (
 
+        Scatter()
 
+            .add_xaxis(w_heard)
 
+            .add_yaxis('', w, **Label_Set)
 
+            .set_global_opts(title_opts=opts.TitleOpts(title='系数w散点图'), **global_Set)
 
+    )
 
+    return c
 
+
 
+def bar(w_heard,w):
 
+    c = (
 
+        Bar()
 
+            .add_xaxis(w_heard)
 
+            .add_yaxis('', abs(w).tolist(), **Label_Set)
 
+            .set_global_opts(title_opts=opts.TitleOpts(title='系数w柱状图'), **global_Set)
 
+    )
 
+    return c
 
+
 
+# def line(w_sum,w,b):
 
+#     x = np.arange(-5, 5, 1)
 
+#     c = (
 
+#         Line()
 
+#             .add_xaxis(x.tolist())
 
+#             .set_global_opts(title_opts=opts.TitleOpts(title=f"系数w曲线"), **global_Set)
 
+#     )
 
+#     for i in range(len(w)):
 
+#         y = x * w[i] + b
 
+#         c.add_yaxis(f"系数w[{i}]", y.tolist(), is_smooth=True, **Label_Set)
 
+#     return c
 
+
 
+def see_Line(x_trainData,y_trainData,w,w_sum,b):
 
+    y = y_trainData.tolist()
 
+    x_data = x_trainData.T
 
+    re = []
 
+    for i in range(len(x_data)):
 
+        x = x_data[i]
 
+        p = int(x.max() - x.min()) / 5
 
+        x_num = np.arange(x.min(), x.min() + p * 6, p)  # 固定5个点，并且正好包括端点
 
+        y_num = x_num * w[i] + (w[i] / w_sum) * b
 
+        c = (
 
+            Line()
 
+                .add_xaxis(x_num.tolist())
 
+                .add_yaxis(f"{i}预测曲线", y_num.tolist(), is_smooth=True, **Label_Set)
 
+                .set_global_opts(title_opts=opts.TitleOpts(title=f"系数w曲线"), **global_Set)
 
+        )
 
+        t = (
 
+            Scatter()
 
+                .add_xaxis(x.tolist())
 
+                .add_yaxis(f'{i}特征', y, **Label_Set)
 
+                .set_global_opts(title_opts=opts.TitleOpts(title='类型划分图'), **global_Set)
 
+        )
 
+        t.overlap(c)
 
+        re.append(t)
 
+    return re
 
+
 
+def get_Color():
 
+    # 随机颜色，雷达图默认非随机颜色
 
+    rgb = [randint(0, 255), randint(0, 255), randint(0, 255)]
 
+    color = '#'
 
+    for a in rgb:
 
+        color += str(hex(a))[-2:].replace('x', '0').upper()  # 转换为16进制,upper表示小写(规范化)
 
+    return color
 
+
 
+def is_continuous(data:np.array,f:float=0.1):
 
+    data = data.tolist()
 
+    l = np.unique(data).tolist()
 
+    try:
 
+        re = len(l)/len(data)>=f or len(data) <= 3
 
+        return re
 
+    except:return False
 
+
 
+def make_Cat(x_data):
 
+    Cat = Categorical_Data()
 
+    for i in range(len(x_data)):
 
+        x1 = x_data[i]  # x坐标
 
+        Cat(x1)
 
+    return Cat
 
+
 
+def Training_visualization_More_NoCenter(x_trainData,class_,y):#根据不同类别绘制x-x分类散点图(可以绘制更多的图)
 
+    x_data = x_trainData.T
 
+    if len(x_data) == 1:
 
+        x_data = np.array([x_data[0],np.zeros(len(x_data[0]))])
 
+    Cat = make_Cat(x_data)
 
+    o_cList = []
 
+    for i in range(len(x_data)):
 
+        for a in range(len(x_data)):
 
+            if a <= i: continue
 
+            x1 = x_data[i]  # x坐标
 
+            x1_con = is_continuous(x1)
 
+            x2 = x_data[a]  # y坐标
 
+            x2_con = is_continuous(x2)
 
+
 
+            o_c = None  # 旧的C
 
+            for class_num in range(len(class_)):
 
+                n_class = class_[class_num]
 
+                x_1 = x1[y == n_class].tolist()
 
+                x_2 = x2[y == n_class]
 
+                x_2_new = np.unique(x_2)
 
+                x_2 = x2[y == n_class].tolist()
 
+                #x与散点图不同，这里是纵坐标
 
+                c = (Scatter()
 
+                     .add_xaxis(x_2)
 
+                     .add_yaxis(f'{n_class}', x_1, **Label_Set)
 
+                     .set_global_opts(title_opts=opts.TitleOpts(title=f'[{a}-{i}]训练数据散点图'), **global_Set,
 
+                                      yaxis_opts=opts.AxisOpts(type_='value' if x1_con else 'category',is_scale=True),
 
+                                      xaxis_opts=opts.AxisOpts(type_='value' if x2_con else 'category',is_scale=True))
 
+                     )
 
+                c.add_xaxis(x_2_new)
 
+
 
+                if o_c == None:
 
+                    o_c = c
 
+                else:
 
+                    o_c = o_c.overlap(c)
 
+            o_cList.append(o_c)
 
+    means,x_range,Type = Cat.get()
 
+    return o_cList,means,x_range,Type
 
+
 
+def Training_visualization_More(x_trainData,class_,y,center):#根据不同类别绘制x-x分类散点图(可以绘制更多的图)
 
+    x_data = x_trainData.T
 
+    if len(x_data) == 1:
 
+        x_data = np.array([x_data[0],np.zeros(len(x_data[0]))])
 
+    Cat = make_Cat(x_data)
 
+    o_cList = []
 
+    for i in range(len(x_data)):
 
+        for a in range(len(x_data)):
 
+            if a <= i: continue
 
+            x1 = x_data[i]  # x坐标
 
+            x1_con = is_continuous(x1)
 
+            x2 = x_data[a]  # y坐标
 
+            x2_con = is_continuous(x2)
 
+
 
+            o_c = None  # 旧的C
 
+            for class_num in range(len(class_)):
 
+                n_class = class_[class_num]
 
+                x_1 = x1[y == n_class].tolist()
 
+                x_2 = x2[y == n_class]
 
+                x_2_new = np.unique(x_2)
 
+                x_2 = x2[y == n_class].tolist()
 
+                #x与散点图不同，这里是纵坐标
 
+                c = (Scatter()
 
+                     .add_xaxis(x_2)
 
+                     .add_yaxis(f'{n_class}', x_1, **Label_Set)
 
+                     .set_global_opts(title_opts=opts.TitleOpts(title=f'[{a}-{i}]训练数据散点图'), **global_Set,
 
+                                      yaxis_opts=opts.AxisOpts(type_='value' if x1_con else 'category',is_scale=True),
 
+                                      xaxis_opts=opts.AxisOpts(type_='value' if x2_con else 'category',is_scale=True))
 
+                     )
 
+                c.add_xaxis(x_2_new)
 
+
 
+                #添加簇中心
 
+                try:
 
+                    center_x_2 = [center[class_num][a]]
 
+                except:
 
+                    center_x_2 = [0]
 
+                b = (Scatter()
 
+                     .add_xaxis(center_x_2)
 
+                     .add_yaxis(f'[{n_class}]中心',[center[class_num][i]], **Label_Set,symbol='triangle')
 
+                     .set_global_opts(title_opts=opts.TitleOpts(title='簇中心'), **global_Set,
 
+                                      yaxis_opts=opts.AxisOpts(type_='value' if x1_con else 'category',is_scale=True),
 
+                                      xaxis_opts=opts.AxisOpts(type_='value' if x2_con else 'category',is_scale=True))
 
+                     )
 
+                c.overlap(b)
 
+
 
+                if o_c == None:
 
+                    o_c = c
 
+                else:
 
+                    o_c = o_c.overlap(c)
 
+            o_cList.append(o_c)
 
+    means,x_range,Type = Cat.get()
 
+    return o_cList,means,x_range,Type
 
+
 
+def Training_visualization_Center(x_trainData,class_,y,center):#根据不同类别绘制x-x分类散点图(可以绘制更多的图)
 
+    x_data = x_trainData.T
 
+    if len(x_data) == 1:
 
+        x_data = np.array([x_data[0],np.zeros(len(x_data[0]))])
 
+    Cat = make_Cat(x_data)
 
+    o_cList = []
 
+    for i in range(len(x_data)):
 
+        x1 = x_data[i]  # x坐标
 
+        x1_con = is_continuous(x1)
 
+
 
+        if i == 0:continue
 
+
 
+        x2 = x_data[i - 1]  # y坐标
 
+        x2_con = is_continuous(x2)
 
+
 
+        o_c = None  # 旧的C
 
+        for class_num in range(len(class_)):
 
+            n_class = class_[class_num]
 
+            x_1 = x1[y == n_class].tolist()
 
+            x_2 = x2[y == n_class]
 
+            x_2_new = np.unique(x_2)
 
+            x_2 = x2[y == n_class].tolist()
 
+            #x与散点图不同，这里是纵坐标
 
+            c = (Scatter()
 
+                 .add_xaxis(x_2)
 
+                 .add_yaxis(f'{n_class}', x_1, **Label_Set)
 
+                 .set_global_opts(title_opts=opts.TitleOpts(title=f'[{i-1}-{i}]训练数据散点图'), **global_Set,
 
+                                  yaxis_opts=opts.AxisOpts(type_='value' if x1_con else 'category',is_scale=True),
 
+                                  xaxis_opts=opts.AxisOpts(type_='value' if x2_con else 'category',is_scale=True))
 
+                 )
 
+            c.add_xaxis(x_2_new)
 
+
 
+            #添加簇中心
 
+            try:
 
+                center_x_2 = [center[class_num][i-1]]
 
+            except:
 
+                center_x_2 = [0]
 
+            b = (Scatter()
 
+                 .add_xaxis(center_x_2)
 
+                 .add_yaxis(f'[{n_class}]中心',[center[class_num][i]], **Label_Set,symbol='triangle')
 
+                 .set_global_opts(title_opts=opts.TitleOpts(title='簇中心'), **global_Set,
 
+                                  yaxis_opts=opts.AxisOpts(type_='value' if x1_con else 'category',is_scale=True),
 
+                                  xaxis_opts=opts.AxisOpts(type_='value' if x2_con else 'category',is_scale=True))
 
+                 )
 
+            c.overlap(b)
 
+
 
+            if o_c == None:
 
+                o_c = c
 
+            else:
 
+                o_c = o_c.overlap(c)
 
+        o_cList.append(o_c)
 
+    means,x_range,Type = Cat.get()
 
+    return o_cList,means,x_range,Type
 
+
 
+def Training_visualization(x_trainData,class_,y):#根据不同类别绘制x-x分类散点图
 
+    x_data = x_trainData.T
 
+    if len(x_data) == 1:
 
+        x_data = np.array([x_data[0],np.zeros(len(x_data[0]))])
 
+    Cat = make_Cat(x_data)
 
+    o_cList = []
 
+    for i in range(len(x_data)):
 
+        x1 = x_data[i]  # x坐标
 
+        x1_con = is_continuous(x1)
 
+
 
+        if i == 0:continue
 
+
 
+        x2 = x_data[i - 1]  # y坐标
 
+        x2_con = is_continuous(x2)
 
+
 
+        o_c = None  # 旧的C
 
+        for n_class in class_:
 
+            x_1 = x1[y == n_class].tolist()
 
+            x_2 = x2[y == n_class]
 
+            x_2_new = np.unique(x_2)
 
+            x_2 = x2[y == n_class].tolist()
 
+            #x与散点图不同，这里是纵坐标
 
+            c = (Scatter()
 
+                 .add_xaxis(x_2)
 
+                 .add_yaxis(f'{n_class}', x_1, **Label_Set)
 
+                 .set_global_opts(title_opts=opts.TitleOpts(title='训练数据散点图'), **global_Set,
 
+                                  yaxis_opts=opts.AxisOpts(type_='value' if x1_con else 'category',is_scale=True),
 
+                                  xaxis_opts=opts.AxisOpts(type_='value' if x2_con else 'category',is_scale=True))
 
+                 )
 
+            c.add_xaxis(x_2_new)
 
+            if o_c == None:
 
+                o_c = c
 
+            else:
 
+                o_c = o_c.overlap(c)
 
+        o_cList.append(o_c)
 
+    means,x_range,Type = Cat.get()
 
+    return o_cList,means,x_range,Type
 
+
 
+def Training_visualization_NoClass(x_trainData):#根据绘制x-x分类散点图(无类别)
 
+    x_data = x_trainData.T
 
+    if len(x_data) == 1:
 
+        x_data = np.array([x_data[0],np.zeros(len(x_data[0]))])
 
+    Cat = make_Cat(x_data)
 
+    o_cList = []
 
+    for i in range(len(x_data)):
 
+        x1 = x_data[i]  # x坐标
 
+        x1_con = is_continuous(x1)
 
+
 
+        if i == 0:continue
 
+
 
+        x2 = x_data[i - 1]  # y坐标
 
+        x2_con = is_continuous(x2)
 
+        x2_new = np.unique(x2)
 
+        #x与散点图不同，这里是纵坐标
 
+        c = (Scatter()
 
+             .add_xaxis(x2)
 
+             .add_yaxis('', x1.tolist(), **Label_Set)
 
+             .set_global_opts(title_opts=opts.TitleOpts(title='训练数据散点图'), **global_Leg,
 
+                              yaxis_opts=opts.AxisOpts(type_='value' if x1_con else 'category',is_scale=True),
 
+                              xaxis_opts=opts.AxisOpts(type_='value' if x2_con else 'category',is_scale=True))
 
+             )
 
+        c.add_xaxis(x2_new)
 
+        o_cList.append(c)
 
+    means,x_range,Type = Cat.get()
 
+    return o_cList,means,x_range,Type
 
+
 
+def Training_W(x_trainData,class_,y,w_list,b_list,means:list):#针对分类问题绘制决策边界
 
+    x_data = x_trainData.T
 
+    if len(x_data) == 1:
 
+        x_data = np.array([x_data[0],np.zeros(len(x_data[0]))])
 
+    o_cList = []
 
+    means.append(0)
 
+    means = np.array(means)
 
+    for i in range(len(x_data)):
 
+        if i == 0:continue
 
+
 
+        x1_con = is_continuous(x_data[i])
 
+        x2 = x_data[i - 1]  # y坐标
 
+        x2_con = is_continuous(x2)
 
+
 
+        o_c = None  # 旧的C
 
+        for class_num in range(len(class_)):
 
+            n_class = class_[class_num]
 
+            x2_new = np.unique(x2[y == n_class])
 
+            #x与散点图不同，这里是纵坐标
 
+
 
+            #加入这个判断是为了解决sklearn历史遗留问题
 
+            if len(class_) == 2:#二分类问题
 
+                if class_num == 0:continue
 
+                w = w_list[0]
 
+                b = b_list[0]
 
+            else:#多分类问题
 
+                w = w_list[class_num]
 
+                b = b_list[class_num]
 
+
 
+            if x2_con:
 
+                x2_new = np.array(make_list(x2_new.min(), x2_new.max(), 5))
 
+
 
+            w = np.append(w, 0)
 
+            y_data = -(x2_new * w[i - 1]) / w[i] + b + (means[:i - 1] * w[:i - 1]).sum() + (means[i + 1:] * w[i + 1:]).sum()#假设除了两个特征意外，其余特征均为means列表的数值
 
+            c = (
 
+                Line()
 
+                    .add_xaxis(x2_new)
 
+                    .add_yaxis(f"决策边界:{n_class}=>[{i}]", y_data.tolist(), is_smooth=True, **Label_Set)
 
+                    .set_global_opts(title_opts=opts.TitleOpts(title=f"系数w曲线"), **global_Set,
 
+                              yaxis_opts=opts.AxisOpts(type_='value' if x1_con else 'category',is_scale=True),
 
+                              xaxis_opts=opts.AxisOpts(type_='value' if x2_con else 'category',is_scale=True))
 
+            )
 
+            if o_c == None:
 
+                o_c = c
 
+            else:
 
+                o_c = o_c.overlap(c)
 
+            #下面不要接任何代码，因为上面会continue
 
+        o_cList.append(o_c)
 
+    return o_cList
 
+
 
+def Regress_W(x_trainData,y,w:np.array,b,means:list):#针对回归问题(y-x图)
 
+    x_data = x_trainData.T
 
+    if len(x_data) == 1:
 
+        x_data = np.array([x_data[0],np.zeros(len(x_data[0]))])
 
+    o_cList = []
 
+    means.append(0)#确保mean[i+1]不会超出index
 
+    means = np.array(means)
 
+    w = np.append(w,0)
 
+    for i in range(len(x_data)):
 
+        x1 = x_data[i]
 
+        x1_con = is_continuous(x1)
 
+        if x1_con:
 
+            x1 = np.array(make_list(x1.min(), x1.max(), 5))
 
+        x1_new = np.unique(x1)
 
+        y_data = x1_new * w[i] + b + (means[:i] * w[:i]).sum() + (means[i+1:] * w[i+1:]).sum()#假设除了两个特征意外，其余特征均为means列表的数值
 
+        y_con = is_continuous(y_data)
 
+        c = (
 
+            Line()
 
+                .add_xaxis(x1_new)
 
+                .add_yaxis(f"拟合结果=>[{i}]", y_data.tolist(), is_smooth=True, **Label_Set)
 
+                .set_global_opts(title_opts=opts.TitleOpts(title=f"系数w曲线"), **global_Set,
 
+                          yaxis_opts=opts.AxisOpts(type_='value' if y_con else None,is_scale=True),
 
+                          xaxis_opts=opts.AxisOpts(type_='value' if x1_con else None,is_scale=True))
 
+        )
 
+        o_cList.append(c)
 
+    return o_cList
 
+
 
+def regress_visualization(x_trainData,y):#y-x数据图
 
+    x_data = x_trainData.T
 
+    y_con = is_continuous(y)
 
+    Cat = make_Cat(x_data)
 
+    o_cList = []
 
+    try:
 
+        visualmap_opts = opts.VisualMapOpts(is_show=True, max_=int(y.max()) + 1, min_=int(y.min()),
 
+                                            pos_right='3%')
 
+    except:
 
+        visualmap_opts = None
 
+        y_con = False
 
+    for i in range(len(x_data)):
 
+        x1 = x_data[i]  # x坐标
 
+        x1_con = is_continuous(x1)
 
+        #不转换成list因为保持dtype的精度，否则绘图会出现各种问题(数值重复)
 
+        if not y_con and x1_con:#y不是连续的但x1连续,ry和ry_con是保护y的
 
+            ry_con,x1_con = x1_con,y_con
 
+            x1,ry = y,x1
 
+        else:
 
+            ry_con = y_con
 
+            ry = y
 
+        c = (
 
+            Scatter()
 
+            .add_xaxis(x1.tolist())#研究表明，这个是横轴
 
+            .add_yaxis('数据',ry.tolist(),**Label_Set)
 
+             .set_global_opts(title_opts=opts.TitleOpts(title="预测类型图"),**global_Set,
 
+                              yaxis_opts=opts.AxisOpts(type_='value' if ry_con else 'category',is_scale=True),
 
+                              xaxis_opts=opts.AxisOpts(type_='value' if x1_con else 'category',is_scale=True),
 
+                              visualmap_opts=visualmap_opts
 
+                              )
 
+        )
 
+        c.add_xaxis(np.unique(x1))
 
+        o_cList.append(c)
 
+    means,x_range,Type = Cat.get()
 
+    return o_cList,means,x_range,Type
 
+
 
+def Feature_visualization(x_trainData,data_name=''):#x-x数据图
 
+    seeting = global_Set if data_name else global_Leg
 
+    x_data = x_trainData.T
 
+    only = False
 
+    if len(x_data) == 1:
 
+        x_data = np.array([x_data[0],np.zeros(len(x_data[0]))])
 
+        only = True
 
+    o_cList = []
 
+    for i in range(len(x_data)):
 
+        for a in range(len(x_data)):
 
+            if a <= i: continue#重复内容，跳过
 
+            x1 = x_data[i]  # x坐标
 
+            x1_con = is_continuous(x1)
 
+            x2 = x_data[a]  # y坐标
 
+            x2_con = is_continuous(x2)
 
+            x2_new = np.unique(x2)
 
+            if only:x2_con = False
 
+            #x与散点图不同，这里是纵坐标
 
+            c = (Scatter()
 
+                 .add_xaxis(x2)
 
+                 .add_yaxis(data_name, x1, **Label_Set)
 
+                 .set_global_opts(title_opts=opts.TitleOpts(title=f'[{i}-{a}]数据散点图'), **seeting,
 
+                                  yaxis_opts=opts.AxisOpts(type_='value' if x1_con else 'category',is_scale=True),
 
+                                  xaxis_opts=opts.AxisOpts(type_='value' if x2_con else 'category',is_scale=True))
 
+                 )
 
+            c.add_xaxis(x2_new)
 
+            o_cList.append(c)
 
+    return o_cList
 
+
 
+def Feature_visualization_Format(x_trainData,data_name=''):#x-x数据图
 
+    seeting = global_Set if data_name else global_Leg
 
+    x_data = x_trainData.T
 
+    only = False
 
+    if len(x_data) == 1:
 
+        x_data = np.array([x_data[0],np.zeros(len(x_data[0]))])
 
+        only = True
 
+    o_cList = []
 
+    for i in range(len(x_data)):
 
+        for a in range(len(x_data)):
 
+            if a <= i: continue#重复内容，跳过（a读取的是i后面的）
 
+            x1 = x_data[i]  # x坐标
 
+            x1_con = is_continuous(x1)
 
+            x2 = x_data[a]  # y坐标
 
+            x2_con = is_continuous(x2)
 
+            x2_new = np.unique(x2)
 
+            x1_list = x1.astype(np.str).tolist()
 
+            for i in range(len(x1_list)):
 
+                x1_list[i] = [x1_list[i],f'特征{i}']
 
+            if only:x2_con = False
 
+            #x与散点图不同，这里是纵坐标
 
+            c = (Scatter()
 
+                 .add_xaxis(x2)
 
+                 .add_yaxis(data_name, x1_list, **Label_Set)
 
+                 .set_global_opts(title_opts=opts.TitleOpts(title=f'[{i}-{a}]数据散点图'), **seeting,
 
+                                  yaxis_opts=opts.AxisOpts(type_='value' if x1_con else 'category',is_scale=True),
 
+                                  xaxis_opts=opts.AxisOpts(type_='value' if x2_con else 'category',is_scale=True),
 
+                                  tooltip_opts=opts.TooltipOpts(is_show = True,axis_pointer_type = "cross",formatter="{c}"))
 
+                 )
 
+            c.add_xaxis(x2_new)
 
+            o_cList.append(c)
 
+    return o_cList
 
+
 
+def Discrete_Feature_visualization(x_trainData,data_name=''):#必定离散x-x数据图
 
+    seeting = global_Set if data_name else global_Leg
 
+    x_data = x_trainData.T
 
+    if len(x_data) == 1:
 
+        x_data = np.array([x_data[0],np.zeros(len(x_data[0]))])
 
+    o_cList = []
 
+    for i in range(len(x_data)):
 
+        for a in range(len(x_data)):
 
+            if a <= i: continue#重复内容，跳过
 
+            x1 = x_data[i]  # x坐标
 
+            x2 = x_data[a]  # y坐标
 
+            x2_new = np.unique(x2)
 
+
 
+            #x与散点图不同，这里是纵坐标
 
+            c = (Scatter()
 
+                 .add_xaxis(x2)
 
+                 .add_yaxis(data_name, x1, **Label_Set)
 
+                 .set_global_opts(title_opts=opts.TitleOpts(title=f'[{i}-{a}]数据散点图'), **seeting,
 
+                                  yaxis_opts=opts.AxisOpts(type_='category',is_scale=True),
 
+                                  xaxis_opts=opts.AxisOpts(type_='category',is_scale=True))
 
+                 )
 
+            c.add_xaxis(x2_new)
 
+            o_cList.append(c)
 
+    return o_cList
 
+
 
+def Conversion_control(y_data,x_data,tab):#合并两x-x图
 
+    if type(x_data) is np.ndarray and type(y_data) is np.ndarray:
 
+        get_x = Feature_visualization(x_data,'原数据')#原来
 
+        get_y = Feature_visualization(y_data,'转换数据')#转换
 
+        for i in range(len(get_x)):
 
+            tab.add(get_x[i].overlap(get_y[i]),f'[{i}]数据x-x散点图')
 
+    return tab
 
+
 
+def Conversion_Separate(y_data,x_data,tab):#并列显示两x-x图
 
+    if type(x_data) is np.ndarray and type(y_data) is np.ndarray:
 
+        get_x = Feature_visualization(x_data,'原数据')#原来
 
+        get_y = Feature_visualization(y_data,'转换数据')#转换
 
+        for i in range(len(get_x)):
 
+            try:
 
+                tab.add(get_x[i],f'[{i}]数据x-x散点图')
 
+            except IndexError:pass
 
+            try:
 
+                tab.add(get_y[i],f'[{i}]变维数据x-x散点图')
 
+            except IndexError:pass
 
+    return tab
 
+
 
+def Conversion_Separate_Format(y_data,tab):#并列显示两x-x图
 
+    if type(y_data) is np.ndarray:
 
+        get_y = Feature_visualization_Format(y_data,'转换数据')#转换
 
+        for i in range(len(get_y)):
 
+            tab.add(get_y[i],f'[{i}]变维数据x-x散点图')
 
+    return tab
 
+
 
+def Conversion_SeparateWH(w_data,h_data,tab):#并列显示两x-x图
 
+    if type(w_data) is np.ndarray and type(w_data) is np.ndarray:
 
+        get_x = Feature_visualization_Format(w_data,'W矩阵数据')#原来
 
+        get_y = Feature_visualization(h_data.T,'H矩阵数据')#转换(先转T，再转T变回原样，W*H是横对列)
 
+        print(h_data)
 
+        print(w_data)
 
+        print(h_data.T)
 
+        for i in range(len(get_x)):
 
+            try:
 
+                tab.add(get_x[i],f'[{i}]W矩阵x-x散点图')
 
+            except IndexError:pass
 
+            try:
 
+                tab.add(get_y[i],f'[{i}]H.T矩阵x-x散点图')
 
+            except IndexError:pass
 
+    return tab
 
+
 
+def make_bar(name, value,tab):#绘制柱状图
 
+    c = (
 
+        Bar()
 
+            .add_xaxis([f'[{i}]特征' for i in range(len(value))])
 
+            .add_yaxis(name, value, **Label_Set)
 
+            .set_global_opts(title_opts=opts.TitleOpts(title='系数w柱状图'), **global_Set)
 
+    )
 
+    tab.add(c, name)
 
+
 
+def judging_Digits(num:(int,float)):#查看小数位数
 
+    a = str(abs(num)).split('.')[0]
 
+    if a == '':raise ValueError
 
+    return len(a)
 
+
 
+class Learner:
 
+    def __init__(self,*args,**kwargs):
 
+        self.numpy_Dic = {}#name:numpy
 
+        self.Fucn_Add()#制作Func_Dic
 
+
 
+    def Add_Form(self,data:np.array,name):
 
+        name = f'{name}[{len(self.numpy_Dic)}]'
 
+        self.numpy_Dic[name] = data
 
+
 
+    def read_csv(self,Dic,name,encoding='utf-8',str_must=False,sep=','):
 
+        type_ = np.str if str_must else np.float
 
+        pf_data = read_csv(Dic,encoding=encoding,delimiter=sep,header=None)
 
+        try:
 
+            data = pf_data.to_numpy(dtype=type_)
 
+        except ValueError:
 
+            data = pf_data.to_numpy(dtype=np.str)
 
+        if data.ndim == 1: data = np.expand_dims(data, axis=1)
 
+        self.Add_Form(data,name)
 
+        return data
 
+
 
+    def Add_Python(self, Text, sheet_name):
 
+        name = {}
 
+        name.update(globals().copy())
 
+        name.update(locals().copy())
 
+        exec(Text, name)
 
+        exec('get = Creat()', name)
 
+        if isinstance(name['get'], np.array):  # 已经是DataFram
 
+            get = name['get']
 
+        else:
 
+            try:
 
+                get = np.array(name['get'])
 
+            except:
 
+                get = np.array([name['get']])
 
+        self.Add_Form(get, sheet_name)
 
+        return get
 
+
 
+    def get_Form(self) -> dict:
 
+        return self.numpy_Dic.copy()
 
+
 
+    def get_Sheet(self,name) -> np.array:
 
+        return self.numpy_Dic[name].copy()
 
+
 
+    def to_CSV(self,Dic:str,name,sep) -> str:
 
+        get = self.get_Sheet(name)
 
+        np.savetxt(Dic, get, delimiter=sep)
 
+        return Dic
 
+
 
+    def to_Html_One(self,name,Dic=''):
 
+        if Dic == '': Dic = f'{name}.html'
 
+        get = self.get_Sheet(name)
 
+        if get.ndim == 1: get = np.expand_dims(get, axis=1)
 
+        get = get.tolist()
 
+        for i in range(len(get)):
 
+            get[i] = [i+1] + get[i]
 
+        headers = [i for i in range(len(get[0]))]
 
+        table = Table_Fisrt()
 
+        table.add(headers, get).set_global_opts(
 
+            title_opts=opts.ComponentTitleOpts(title=f"表格:{name}", subtitle="CoTan~机器学习:查看数据"))
 
+        table.render(Dic)
 
+        return Dic
 
+
 
+    def to_Html(self, name, Dic='', type_=0):
 
+        if Dic == '': Dic = f'{name}.html'
 
+        # 把要画的sheet放到第一个
 
+        Sheet_Dic = self.get_Form()
 
+        del Sheet_Dic[name]
 
+        Sheet_list = [name] + list(Sheet_Dic.keys())
 
+
 
+        class TAB_F:
 
+            def __init__(self, q):
 
+                self.tab = q  # 一个Tab
 
+
 
+            def render(self, Dic):
 
+                return self.tab.render(Dic)
 
+
 
+        # 生成一个显示页面
 
+        if type_ == 0:
 
+            class TAB(TAB_F):
 
+                def add(self, table, k, *f):
 
+                    self.tab.add(table, k)
 
+
 
+            tab = TAB(tab_First(page_title='CoTan:查看表格'))  # 一个Tab
 
+        elif type_ == 1:
 
+            class TAB(TAB_F):
 
+                def add(self, table, *k):
 
+                    self.tab.add(table)
 
+
 
+            tab = TAB(Page(page_title='CoTan:查看表格', layout=Page.DraggablePageLayout))
 
+        else:
 
+            class TAB(TAB_F):
 
+                def add(self, table, *k):
 
+                    self.tab.add(table)
 
+
 
+            tab = TAB(Page(page_title='CoTan:查看表格', layout=Page.SimplePageLayout))
 
+        # 迭代添加内容
 
+        for name in Sheet_list:
 
+            get = self.get_Sheet(name)
 
+            if get.ndim == 1: get = np.expand_dims(get, axis=1)
 
+            get = get.tolist()
 
+            for i in range(len(get)):
 
+                get[i] = [i+1] + get[i]
 
+            headers = [i for i in range(len(get[0]))]
 
+            table = Table_Fisrt()
 
+            table.add(headers, get).set_global_opts(
 
+                title_opts=opts.ComponentTitleOpts(title=f"表格:{name}", subtitle="CoTan~机器学习:查看数据"))
 
+            tab.add(table, f'表格:{name}')
 
+        tab.render(Dic)
 
+        return Dic
 
+
 
+    def Merge(self,name,axis=0):#aiis:0-横向合并(hstack),1-纵向合并(vstack)，2-深度合并
 
+        sheet_list = []
 
+        for i in name:
 
+            sheet_list.append(self.get_Sheet(i))
 
+        get = {0:np.hstack,1:np.vstack,2:np.dstack}[axis](sheet_list)
 
+        self.Add_Form(np.array(get),f'{name[0]}合成')
 
+
 
+    def Split(self,name,split=2,axis=0):#aiis:0-横向分割(hsplit),1-纵向分割(vsplit)
 
+        sheet = self.get_Sheet(name)
 
+        get = {0:np.hsplit,1:np.vsplit,2:np.dsplit}[axis](sheet,split)
 
+        for i in get:
 
+            self.Add_Form(i,f'{name[0]}分割')
 
+
 
+    def Two_Split(self,name,split,axis):#二分切割(0-横向，1-纵向)
 
+        sheet = self.get_Sheet(name)
 
+        try:
 
+            split = float(eval(split))
 
+            if split < 1:
 
+                split = int(split * len(sheet) if axis == 1 else len(sheet[0]))
 
+            else:
 
+                raise Exception
 
+        except:
 
+            split = int(split)
 
+        if axis == 0:
 
+            self.Add_Form(sheet[:,split:], f'{name[0]}分割')
 
+            self.Add_Form(sheet[:,:split], f'{name[0]}分割')
 
+
 
+    def Deep(self,sheet:np.ndarray):
 
+        return sheet.ravel()
 
+
 
+    def Down_Ndim(self,sheet:np.ndarray):#横向
 
+        down_list = []
 
+        for i in sheet:
 
+            down_list.append(i.ravel())
 
+        return np.array(down_list)
 
+
 
+    def LongitudinalDown_Ndim(self,sheet:np.ndarray):#纵向
 
+        down_list = []
 
+        for i in range(len(sheet[0])):
 
+            down_list.append(sheet[:,i].ravel())
 
+        return np.array(down_list).T
 
+
 
+    def Reval(self,name,axis):#axis:0-横向，1-纵向(带.T)，2-深度
 
+        sheet = self.get_Sheet(name)
 
+        self.Add_Form({0:self.Down_Ndim,1:self.LongitudinalDown_Ndim,2:self.Deep}[axis](sheet).copy(),f'{name}伸展')
 
+
 
+    def Del_Ndim(self,name):#删除无用维度
 
+        sheet = self.get_Sheet(name)
 
+        self.Add_Form(np.squeeze(sheet), f'{name}降维')
 
+
 
+    def T(self,name,Func:list):
 
+        sheet = self.get_Sheet(name)
 
+        if sheet.ndim <= 2:
 
+            self.Add_Form(sheet.T.copy(), f'{name}.T')
 
+        else:
 
+            self.Add_Form(np.transpose(sheet,Func).copy(), f'{name}.T')
 
+
 
+    def reShape(self,name,shape:list):
 
+        sheet = self.get_Sheet(name)
 
+        self.Add_Form(sheet.reshape(shape).copy(), f'{name}.r')
 
+
 
+    def Fucn_Add(self):
 
+        self.Func_Dic = {
 
+            'abs':lambda x,y:np.abs(x),
 
+            'sqrt':lambda x,y:np.sqrt(x),
 
+            'pow':lambda x,y:x**y,
 
+            'loge':lambda x,y:np.log(x),
 
+            'log10':lambda x,y:np.log10(x),
 
+            'ceil':lambda x,y:np.ceil(x),
 
+            'floor':lambda x,y:np.floor(x),
 
+            'rint':lambda x,y:np.rint(x),
 
+            'sin':lambda x,y:np.sin(x),
 
+            'cos':lambda x,y:np.cos(x),
 
+            'tan':lambda x,y:np.tan(x),
 
+            'tanh':lambda x,y:np.tanh(x),
 
+            'sinh':lambda x,y:np.sinh(x),
 
+            'cosh':lambda x,y:np.cosh(x),
 
+            'asin': lambda x, y: np.arcsin(x),
 
+            'acos': lambda x, y: np.arccos(x),
 
+            'atan': lambda x, y: np.arctan(x),
 
+            'atanh': lambda x, y: np.arctanh(x),
 
+            'asinh': lambda x, y: np.arcsinh(x),
 
+            'acosh': lambda x, y: np.arccosh(x),
 
+            'add': lambda x, y: x + y,#矩阵或元素
 
+            'sub': lambda x, y: x - y,#矩阵或元素
 
+            'mul': lambda x, y: np.multiply(x,y),#元素级别
 
+            'matmul': lambda x, y: np.matmul(x,y),#矩阵
 
+            'dot': lambda x, y: np.dot(x,y),#矩阵
 
+            'div': lambda x, y: x / y,
 
+            'div_floor': lambda x, y: np.floor_divide(x,y),
 
+            'power': lambda x, y: np.power(x,y),#元素级
 
+        }
 
+
 
+    def Cul_Numpy(self,data,data_type,Func):
 
+        if not 1 in data_type:raise Exception
 
+        func = self.Func_Dic.get(Func,lambda x,y:x)
 
+        args_data = []
 
+        for i in range(len(data)):
 
+            if data_type[i] == 0:
 
+                args_data.append(data[i])
 
+            else:
 
+                args_data.append(self.get_Sheet(data[i]))
 
+        get = func(*args_data)
 
+        self.Add_Form(get,f'{Func}({data[0]},{data[1]})')
 
+        return get
 
+
 
+class Study_MachineBase:
 
+    def __init__(self,*args,**kwargs):
 
+        self.Model = None
 
+        self.have_Fit = False
 
+        self.have_Predict = False
 
+        self.x_trainData = None
 
+        self.y_trainData = None
 
+        #有监督学习专有的testData
 
+        self.x_testData = None
 
+        self.y_testData = None
 
+        #记录这两个是为了克隆
 
+
 
+    def Fit(self,x_data,y_data,split=0.3,Increment=True,**kwargs):
 
+        y_data = y_data.ravel()
 
+        try:
 
+            if self.x_trainData is None or not Increment:raise Exception
 
+            self.x_trainData = np.vstack(x_data,self.x_trainData)
 
+            self.y_trainData = np.vstack(y_data,self.y_trainData)
 
+        except:
 
+            self.x_trainData = x_data.copy()
 
+            self.y_trainData = y_data.copy()
 
+        x_train,x_test,y_train,y_test = train_test_split(x_data,y_data,test_size=split)
 
+        try:#增量式训练
 
+            if not Increment:raise Exception
 
+            self.Model.partial_fit(x_data,y_data)
 
+        except:
 
+            self.Model.fit(self.x_trainData, self.y_trainData)
 
+        train_score = self.Model.score(x_train,y_train)
 
+        test_score = self.Model.score(x_test,y_test)
 
+        self.have_Fit = True
 
+        return train_score,test_score
 
+
 
+    def Score(self,x_data,y_data):
 
+        Score = self.Model.score(x_data,y_data)
 
+        return Score
 
+
 
+    def Class_Score(self,Dic,x_data:np.ndarray,y_Really:np.ndarray):
 
+        y_Really = y_Really.ravel()
 
+        y_Predict = self.Predict(x_data)[0]
 
+
 
+        Accuracy = self._Accuracy(y_Predict,y_Really)
 
+
 
+        Recall,class_ = self._Macro(y_Predict,y_Really)
 
+        Precision,class_ = self._Macro(y_Predict,y_Really,1)
 
+        F1,class_ = self._Macro(y_Predict,y_Really,2)
 
+
 
+        Confusion_matrix,class_ = self._Confusion_matrix(y_Predict,y_Really)
 
+        kappa = self._Kappa_score(y_Predict,y_Really)
 
+
 
+        tab = Tab()
 
+        def gauge_base(name:str,value:float) -> Gauge:
 
+            c = (
 
+                Gauge()
 
+                    .add("", [(name, round(value*100,2))],min_ = 0, max_ = 100)
 
+                    .set_global_opts(title_opts=opts.TitleOpts(title=name))
 
+            )
 
+            return c
 
+        tab.add(gauge_base('准确率',Accuracy),'准确率')
 
+        tab.add(gauge_base('kappa',kappa),'kappa')
 
+
 
+        def Bar_base(name,value) -> Bar:
 
+            c = (
 
+                Bar()
 
+                    .add_xaxis(class_)
 
+                    .add_yaxis(name, value, **Label_Set)
 
+                    .set_global_opts(title_opts=opts.TitleOpts(title=name), **global_Set)
 
+            )
 
+            return c
 
+        tab.add(Bar_base('精确率',Precision.tolist()),'精确率')
 
+        tab.add(Bar_base('召回率',Recall.tolist()),'召回率')
 
+        tab.add(Bar_base('F1',F1.tolist()),'F1')
 
+
 
+        def heatmap_base(name,value,max_,min_,show) -> HeatMap:
 
+            c = (
 
+                HeatMap()
 
+                    .add_xaxis(class_)
 
+                    .add_yaxis(name, class_, value, label_opts=opts.LabelOpts(is_show=show,position='inside'))
 
+                    .set_global_opts(title_opts=opts.TitleOpts(title=name), **global_Set,visualmap_opts=
 
+                opts.VisualMapOpts(max_=max_,min_=min_,pos_right='3%'))
 
+                )
 
+            return c
 
+
 
+        value = [[class_[i],class_[j],float(Confusion_matrix[i,j])] for i in range(len(class_)) for j in range(len(class_))]
 
+        tab.add(heatmap_base('混淆矩阵',value,float(Confusion_matrix.max()),float(Confusion_matrix.min()),len(class_)<7), '混淆矩阵')
 
+
 
+        desTo_CSV(Dic,'混淆矩阵',Confusion_matrix,class_,class_)
 
+        desTo_CSV(Dic,'评分',[Precision,Recall,F1],class_,['精确率','召回率','F1'])
 
+        save = Dic + r'/分类模型评估.HTML'
 
+        tab.render(save)
 
+        return save,
 
+
 
+    def _Accuracy(self,y_Predict,y_Really):#准确率
 
+        return accuracy_score(y_Really, y_Predict)
 
+
 
+    def _Macro(self,y_Predict,y_Really,func=0):
 
+        Func = [recall_score,precision_score,f1_score]#召回率，精确率和f1
 
+        class_ = np.unique(y_Really).tolist()
 
+        result = (Func[func](y_Really,y_Predict,class_,average=None))
 
+        return result,class_
 
+
 
+    def _Confusion_matrix(self,y_Predict,y_Really):#混淆矩阵
 
+        class_ = np.unique(y_Really).tolist()
 
+        return confusion_matrix(y_Really, y_Predict),class_
 
+
 
+    def _Kappa_score(self,y_Predict,y_Really):
 
+        return cohen_kappa_score(y_Really, y_Predict)
 
+
 
+    def Regression_Score(self,Dic,x_data:np.ndarray,y_Really:np.ndarray):
 
+        y_Really = y_Really.ravel()
 
+        y_Predict = self.Predict(x_data)[0]
 
+        tab = Tab()
 
+
 
+        MSE = self._MSE(y_Predict,y_Really)
 
+        MAE = self._MAE(y_Predict,y_Really)
 
+        r2_Score = self._R2_Score(y_Predict,y_Really)
 
+        RMSE = self._RMSE(y_Predict,y_Really)
 
+
 
+        tab.add(make_Tab(['MSE','MAE','RMSE','r2_Score'],[[MSE,MAE,RMSE,r2_Score]]), '评估数据')
 
+
 
+        save = Dic + r'/回归模型评估.HTML'
 
+        tab.render(save)
 
+        return save,
 
+
 
+    def Clusters_Score(self,Dic,x_data:np.ndarray,*args):
 
+        y_Predict = self.Predict(x_data)[0]
 
+        tab = Tab()
 
+        Coefficient,Coefficient_array = self._Coefficient_clustering(x_data,y_Predict)
 
+
 
+        def gauge_base(name:str,value:float) -> Gauge:
 
+            c = (
 
+                Gauge()
 
+                    .add("", [(name, round(value*100,2))],min_ = 0, max_ = 10**(judging_Digits(value*100)))
 
+                    .set_global_opts(title_opts=opts.TitleOpts(title=name))
 
+            )
 
+            return c
 
+        def Bar_base(name,value,xaxis) -> Bar:
 
+            c = (
 
+                Bar()
 
+                    .add_xaxis(xaxis)
 
+                    .add_yaxis(name, value, **Label_Set)
 
+                    .set_global_opts(title_opts=opts.TitleOpts(title=name), **global_Set)
 
+            )
 
+            return c
 
+
 
+        tab.add(gauge_base('平均轮廓系数', Coefficient),'平均轮廓系数')
 
+
 
+        def Bar_(Coefficient_array,name='数据轮廓系数'):
 
+            xaxis = [f'数据{i}' for i in range(len(Coefficient_array))]
 
+            value = Coefficient_array.tolist()
 
+            tab.add(Bar_base(name,value,xaxis),name)
 
+
 
+        n = 20
 
+        if len(Coefficient_array) <= n:
 
+            Bar_(Coefficient_array)
 
+        elif len(Coefficient_array) <= n**2:
 
+            a = 0
 
+            while a <= len(Coefficient_array):
 
+                b = a + n
 
+                if b >= len(Coefficient_array):b = len(Coefficient_array) + 1
 
+                Cofe_array = Coefficient_array[a:b]
 
+                Bar_(Cofe_array,f'{a}-{b}数据轮廓系数')
 
+                a += n
 
+        else:
 
+            split = np.hsplit(Coefficient_array,n)
 
+            a = 0
 
+            for Cofe_array in split:
 
+                Bar_(Cofe_array, f'{a}%-{a + n}%数据轮廓系数')
 
+                a += n
 
+
 
+        save = Dic + r'/聚类模型评估.HTML'
 
+        tab.render(save)
 
+        return save,
 
+
 
+    def _MSE(self,y_Predict,y_Really):#均方误差
 
+        return mean_squared_error(y_Really, y_Predict)
 
+
 
+    def _MAE(self,y_Predict,y_Really):#中值绝对误差
 
+        return median_absolute_error(y_Really, y_Predict)
 
+
 
+    def _R2_Score(self,y_Predict,y_Really):#中值绝对误差
 
+        return r2_score(y_Really, y_Predict)
 
+
 
+    def _RMSE(self,y_Predict,y_Really):#中值绝对误差
 
+        return self._MSE(y_Predict,y_Really) ** 0.5
 
+
 
+    def _Coefficient_clustering(self,x_data,y_Predict):
 
+        means_score = silhouette_score(x_data,y_Predict)
 
+        outline_score = silhouette_samples(x_data,y_Predict)
 
+        return means_score, outline_score
 
+
 
+    def Predict(self,x_data,*args,**kwargs):
 
+        self.x_testData = x_data.copy()
 
+        y_Predict = self.Model.predict(x_data)
 
+        self.y_testData = y_Predict.copy()
 
+        self.have_Predict = True
 
+        return y_Predict,'预测'
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        return (Dic,)
 
+
 
+class prep_Base(Study_MachineBase):#不允许第二次训练
 
+    def __init__(self,*args,**kwargs):
 
+        super(prep_Base, self).__init__(*args,**kwargs)
 
+        self.Model = None
 
+
 
+    def Fit(self, x_data,y_data,Increment=True, *args, **kwargs):
 
+        if not self.have_Predict:  # 不允许第二次训练
 
+            y_data = y_data.ravel()
 
+            try:
 
+                if self.x_trainData is None or not Increment: raise Exception
 
+                self.x_trainData = np.vstack(x_data, self.x_trainData)
 
+                self.y_trainData = np.vstack(y_data, self.y_trainData)
 
+            except:
 
+                self.x_trainData = x_data.copy()
 
+                self.y_trainData = y_data.copy()
 
+            try:  # 增量式训练
 
+                if not Increment: raise Exception
 
+                self.Model.partial_fit(x_data, y_data)
 
+            except:
 
+                self.Model.fit(self.x_trainData, self.y_trainData)
 
+        self.have_Fit = True
 
+        return 'None', 'None'
 
+
 
+    def Predict(self, x_data, *args, **kwargs):
 
+        self.x_testData = x_data.copy()
 
+        x_Predict = self.Model.transform(x_data)
 
+        self.y_testData = x_Predict.copy()
 
+        self.have_Predict = True
 
+        return x_Predict,'特征工程'
 
+
 
+    def Score(self, x_data, y_data):
 
+        return 'None' # 没有score
 
+
 
+class Unsupervised(prep_Base):#无监督，不允许第二次训练
 
+    def Fit(self, x_data,Increment=True, *args, **kwargs):
 
+        if not self.have_Predict:  # 不允许第二次训练
 
+            self.y_trainData = None
 
+            try:
 
+                if self.x_trainData is None or not Increment: raise Exception
 
+                self.x_trainData = np.vstack(x_data, self.x_trainData)
 
+            except:
 
+                self.x_trainData = x_data.copy()
 
+            try:  # 增量式训练
 
+                if not Increment: raise Exception
 
+                self.Model.partial_fit(x_data)
 
+            except:
 
+                self.Model.fit(self.x_trainData, self.y_trainData)
 
+        self.have_Fit = True
 
+        return 'None', 'None'
 
+
 
+class UnsupervisedModel(prep_Base):#无监督
 
+    def Fit(self, x_data, Increment=True,*args, **kwargs):
 
+        self.y_trainData = None
 
+        try:
 
+            if self.x_trainData is None or not Increment: raise Exception
 
+            self.x_trainData = np.vstack(x_data, self.x_trainData)
 
+        except:
 
+            self.x_trainData = x_data.copy()
 
+        try:  # 增量式训练
 
+            if not Increment: raise Exception
 
+            self.Model.partial_fit(x_data)
 
+        except:
 
+            self.Model.fit(self.x_trainData, self.y_trainData)
 
+        self.have_Fit = True
 
+        return 'None', 'None'
 
+
 
+class To_PyeBase(Study_MachineBase):
 
+    def __init__(self,args_use,model,*args,**kwargs):
 
+        super(To_PyeBase, self).__init__(*args,**kwargs)
 
+        self.Model = None
 
+
 
+        #记录这两个是为了克隆
 
+        self.k = {}
 
+        self.Model_Name = model
 
+
 
+    def Fit(self, x_data,y_data, *args, **kwargs):
 
+        self.x_trainData = x_data.copy()
 
+        self.y_trainData = y_data.ravel().copy()
 
+        self.have_Fit = True
 
+        return 'None', 'None'
 
+
 
+    def Predict(self, x_data, *args, **kwargs):
 
+        self.have_Predict = True
 
+        return np.array([]),'请使用训练'
 
+
 
+    def Score(self, x_data, y_data):
 
+        return 'None' # 没有score
 
+
 
+def num_str(num,f):
 
+    num = str(round(float(num),f))
 
+    if len(num.replace('.','')) == f:
 
+        return num
 
+    n = num.split('.')
 
+    if len(n) == 0:#无小数
 
+        return num + '.' + '0' * (f - len(num))
 
+    else:
 
+        return num + '0' * (f - len(num) + 1)#len(num)多算了一位小数点
 
+
 
+def desTo_CSV(Dic,name,data,columns=None,row=None):
 
+    Dic = Dic + '/' + name + '.csv'
 
+    DataFrame(data,columns=columns,index=row).to_csv(Dic,header=False if columns is None else True,
 
+                                                     index=False if row is None else True)
 
+    return data
 
+
 
+class Des(To_PyeBase):#数据分析
 
+    def Des(self, Dic, *args, **kwargs):
 
+        tab = Tab()
 
+
 
+        data = self.x_trainData
 
+        def Cumulative_calculation(data,func,name,tab):
 
+            sum_list = []
 
+            for i in range(len(data)):#按行迭代数据
 
+                sum_list.append([])
 
+                for a in range(len(data[i])):
 
+                    s = num_str(func(data[:i+1,a]),8)
 
+                    sum_list[-1].append(s)
 
+            desTo_CSV(Dic,f'{name}',sum_list)
 
+            tab.add(make_Tab([f'[{i}]' for i in range(len(sum_list[0]))],sum_list),f'{name}')
 
+
 
+        Geometric_mean = lambda x:np.power(np.prod(x),1/len(x))#几何平均数
 
+        Square_mean = lambda x:np.sqrt(np.sum(np.power(x,2)) / len(x))#平方平均数
 
+        Harmonic_mean = lambda x:len(x)/np.sum(np.power(x,-1))#调和平均数
 
+
 
+        Cumulative_calculation(data,np.sum,'累计求和',tab)
 
+        Cumulative_calculation(data,np.var,'累计方差',tab)
 
+        Cumulative_calculation(data,np.std,'累计标准差',tab)
 
+        Cumulative_calculation(data,np.mean,'累计算术平均值',tab)
 
+        Cumulative_calculation(data,Geometric_mean,'累计几何平均值',tab)
 
+        Cumulative_calculation(data,Square_mean,'累计平方平均值',tab)
 
+        Cumulative_calculation(data,Harmonic_mean,'累计调和平均值',tab)
 
+        Cumulative_calculation(data,np.median,'累计中位数',tab)
 
+        Cumulative_calculation(data,np.max,'累计最大值',tab)
 
+        Cumulative_calculation(data,np.min,'累计最小值',tab)
 
+
 
+        save = Dic + r'/数据分析.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class CORR(To_PyeBase):#相关性和协方差
 
+    def Des(self, Dic, *args, **kwargs):
 
+        tab = Tab()
 
+
 
+        data = DataFrame(self.x_trainData)
 
+        corr = data.corr().to_numpy()#相关性
 
+        cov = data.cov().to_numpy()#协方差
 
+
 
+        def HeatMAP(data,name:str,max_,min_):
 
+            x = [f'特征[{i}]' for i in range(len(data))]
 
+            y = [f'特征[{i}]' for i in range(len(data[0]))]
 
+            value = [(f'特征[{i}]', f'特征[{j}]', float(data[i][j])) for i in range(len(data)) for j in range(len(data[i]))]
 
+            c = (HeatMap()
 
+                 .add_xaxis(x)
 
+                 .add_yaxis(f'数据', y, value, label_opts=opts.LabelOpts(is_show= True if len(x) <= 10 else False,position='inside'))#如果特征太多则不显示标签
 
+                 .set_global_opts(title_opts=opts.TitleOpts(title='矩阵热力图'), **global_Leg,
 
+                                  yaxis_opts=opts.AxisOpts(is_scale=True, type_='category'),  # 'category'
 
+                                  xaxis_opts=opts.AxisOpts(is_scale=True, type_='category'),
 
+                                  visualmap_opts=opts.VisualMapOpts(is_show=True, max_=max_,min_=min_,pos_right='3%'))#显示
 
+                 )
 
+            tab.add(c,name)
 
+
 
+        HeatMAP(corr,'相关性热力图',1,-1)
 
+        HeatMAP(cov,'协方差热力图',float(cov.max()),float(cov.min()))
 
+
 
+        desTo_CSV(Dic, f'相关性矩阵', corr)
 
+        desTo_CSV(Dic, f'协方差矩阵', cov)
 
+        save = Dic + r'/数据相关性.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class View_data(To_PyeBase):#绘制预测型热力图
 
+    def __init__(self, args_use, Learner, *args, **kwargs):  # model表示当前选用的模型类型,Alpha针对正则化的参数
 
+        super(View_data, self).__init__(args_use,Learner,*args, **kwargs)
 
+
 
+        self.Model = Learner.Model
 
+        self.Select_Model = None
 
+        self.have_Fit = Learner.have_Fit
 
+        self.Model_Name = 'Select_Model'
 
+        self.Learner = Learner
 
+        self.Learner_name = Learner.Model_Name
 
+
 
+    def Fit(self,*args,**kwargs):
 
+        self.have_Fit = True
 
+        return 'None','None'
 
+
 
+    def Predict(self,x_data,Add_Func=None,*args, **kwargs):
 
+        x_trainData = self.Learner.x_trainData
 
+        y_trainData = self.Learner.y_trainData
 
+        x_name = self.Learner_name
 
+        if not x_trainData is None:
 
+            Add_Func(x_trainData, f'{x_name}:x训练数据')
 
+
 
+        try:
 
+            x_testData = self.x_testData
 
+            if not x_testData is None:
 
+                Add_Func(x_testData, f'{x_name}:x测试数据')
 
+        except:pass
 
+
 
+        try:
 
+            y_testData = self.y_testData.copy()
 
+            if not y_testData is None:
 
+                Add_Func(y_testData, f'{x_name}:y测试数据')
 
+        except:pass
 
+
 
+        self.have_Fit = True
 
+        if y_trainData is None:
 
+            return np.array([]), 'y训练数据'
 
+        return y_trainData,'y训练数据'
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        return Dic,
 
+
 
+class MatrixScatter(To_PyeBase):#矩阵散点图
 
+    def Des(self, Dic, *args, **kwargs):
 
+        tab = Tab()
 
+
 
+        data = self.x_trainData
 
+        if data.ndim <= 2:#维度为2
 
+            c = (Scatter()
 
+                 .add_xaxis([f'{i}' for i in range(data.shape[1])])
 
+                 .set_global_opts(title_opts=opts.TitleOpts(title=f'矩阵散点图'), **global_Leg)
 
+                 )
 
+            if data.ndim == 2:
 
+                for num in range(len(data)):
 
+                    i = data[num]
 
+                    c.add_yaxis(f'{num}',[[f'{num}',x] for x in i],color='#FFFFFF')
 
+            else:
 
+                c.add_yaxis(f'0', [[0,x]for x in data],color='#FFFFFF')
 
+            c.set_series_opts(label_opts=opts.LabelOpts(is_show=True,color='#000000',position='inside',
 
+            formatter=JsCode("function(params){return params.data[2];}"),
 
+            ))
 
+        elif data.ndim == 3:
 
+            c = (Scatter3D()
 
+                 .set_global_opts(title_opts=opts.TitleOpts(title=f'矩阵散点图'),**global_Leg)
 
+                 )
 
+            for num in range(len(data)):
 
+                i = data[num]
 
+                for s_num in range(len(i)):
 
+                    s = i[s_num]
 
+                    y_data = [[num,s_num,x,float(s[x])] for x in range(len(s))]
 
+                    c.add(f'{num}',y_data,zaxis3d_opts = opts.Axis3DOpts(type_="category"))
 
+            c.set_series_opts(label_opts=opts.LabelOpts(is_show=True,color='#000000',position='inside',
 
+                        formatter=JsCode("function(params){return params.data[3];}")))
 
+        else:
 
+            c = Scatter()
 
+        tab.add(c,'矩阵散点图')
 
+
 
+        save = Dic + r'/矩阵散点图.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Cluster_Tree(To_PyeBase):#聚类树状图
 
+    def Des(self, Dic, *args, **kwargs):
 
+        tab = Tab()
 
+        x_data = self.x_trainData
 
+        linkage_array = ward(x_data)#self.y_trainData是结果
 
+        dendrogram(linkage_array)
 
+        plt.savefig(Dic + r'/Cluster_graph.png')
 
+
 
+        image = Image()
 
+        image.add(src=Dic + r'/Cluster_graph.png',).set_global_opts(title_opts=opts.ComponentTitleOpts(title="聚类树状图"))
 
+        tab.add(image,'聚类树状图')
 
+
 
+        save = Dic + r'/聚类树状图.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Class_To_Bar(To_PyeBase):#类型柱状图
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        x_data = self.x_trainData.T
 
+        y_data = self.y_trainData
 
+        class_ = np.unique(y_data).tolist()#类型
 
+        class_list = []
 
+        for n_class in class_:  # 生成class_list(class是1,，也就是二维的，下面会压缩成一维)
 
+            class_list.append(y_data == n_class)
 
+        for num_i in range(len(x_data)):#迭代每一个特征
 
+            i = x_data[num_i]
 
+            i_con = is_continuous(i)
 
+            if i_con and len(i) >= 11:
 
+                c_list = [[0] * 10 for _ in class_list]  # 存放绘图数据，每一层列表是一个类(leg)，第二层是每个x_data
 
+                start = i.min()
 
+                end = i.max()
 
+                n = (end - start) / 10#生成10条柱子
 
+                x_axis = []#x轴
 
+                num_startEND = 0#迭代到第n个
 
+                while num_startEND <= 9:#把每个特征分为10类进行迭代
 
+                    x_axis.append(f'({num_startEND})[{round(start, 2)}-{round((start + n) if (start + n) <= end or not num_startEND == 9 else end, 2)}]')#x_axis添加数据
 
+                    try:
 
+                        if num_startEND == 9:raise Exception#执行到第10次时，直接获取剩下的所有
 
+                        s = (start <= i) == (i < end)#布尔索引
 
+                    except:#因为start + n有超出end的风险
 
+                        s = (start <= i) == (i <= end)#布尔索引
 
+                    # n_data = i[s]  # 取得现在的特征数据
 
+
 
+                    for num in range(len(class_list)):#根据类别进行迭代
 
+                        now_class = class_list[num]#取得布尔数组：y_data == n_class也就是输出值为指定类型的bool矩阵，用于切片
 
+                        bool_class = now_class[s].ravel()#切片成和n_data一样的位置一样的形状(now_class就是一个bool矩阵)
 
+                        c_list[num][num_startEND] = (int(np.sum(bool_class))) #用len计数 c_list = [[class1的数据],[class2的数据],[]]
 
+                    num_startEND += 1
 
+                    start += n
 
+            else :
 
+                iter_np = np.unique(i)
 
+                c_list = [[0] * len(iter_np) for _ in class_list]  # 存放绘图数据，每一层列表是一个类(leg)，第二层是每个x_data
 
+                x_axis = []  # 添加x轴数据
 
+                for i_num in range(len(iter_np)):#迭代每一个i(不重复)
 
+                    i_data = iter_np[i_num]
 
+                    # n_data= i[i == i_data]#取得现在特征数据
 
+                    x_axis.append(f'[{i_data}]')
 
+                    for num in range(len(class_list)):# 根据类别进行迭代
 
+                        now_class = class_list[num]#取得class_list的布尔数组
 
+                        bool_class = now_class[i == i_data]#切片成和n_data一样的位置一样的形状(now_class就是一个bool矩阵)
 
+                        c_list[num][i_num] = (int(np.sum(bool_class).tolist())) #用len计数 c_list = [[class1的数据],[class2的数据],[]]
 
+            c = (
 
+                Bar()
 
+                    .add_xaxis(x_axis)
 
+                    .set_global_opts(title_opts=opts.TitleOpts(title='类型-特征统计柱状图'), **global_Set,xaxis_opts=opts.AxisOpts(type_='category'),
 
+                                     yaxis_opts=opts.AxisOpts(type_='value')))
 
+            y_axis = []
 
+            for i in range(len(c_list)):
 
+                y_axis.append(f'{class_[i]}')
 
+                c.add_yaxis(f'{class_[i]}', c_list[i], **Label_Set)
 
+            desTo_CSV(Dic, f'类型-[{num_i}]特征统计柱状图', c_list, x_axis, y_axis)
 
+            tab.add(c, f'类型-[{num_i}]特征统计柱状图')
 
+
 
+        #未完成
 
+        save = Dic + r'/特征统计.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Numpy_To_HeatMap(To_PyeBase):#Numpy矩阵绘制热力图
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+
 
+        data = self.x_trainData
 
+        x = [f'横[{i}]' for i in range(len(data))]
 
+        y = [f'纵[{i}]' for i in range(len(data[0]))]
 
+        value = [(f'横[{i}]', f'纵[{j}]', float(data[i][j])) for i in range(len(data)) for j in range(len(data[i]))]
 
+        c = (HeatMap()
 
+             .add_xaxis(x)
 
+             .add_yaxis(f'数据', y, value, **Label_Set)  # value的第一个数值是x
 
+             .set_global_opts(title_opts=opts.TitleOpts(title='矩阵热力图'), **global_Leg,
 
+                              yaxis_opts=opts.AxisOpts(is_scale=True, type_='category'),  # 'category'
 
+                              xaxis_opts=opts.AxisOpts(is_scale=True, type_='category'),
 
+                              visualmap_opts=opts.VisualMapOpts(is_show=True, max_=float(data.max()),
 
+                                                                min_=float(data.min()),
 
+                                                                pos_right='3%'))#显示
 
+             )
 
+        tab.add(c,'矩阵热力图')
 
+        tab.add(make_Tab(x,data.T.tolist()),f'矩阵热力图:表格')
 
+
 
+        save = Dic + r'/矩阵热力图.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Predictive_HeatMap_Base(To_PyeBase):#绘制预测型热力图
 
+    def __init__(self, args_use, Learner, *args, **kwargs):  # model表示当前选用的模型类型,Alpha针对正则化的参数
 
+        super(Predictive_HeatMap_Base, self).__init__(args_use,Learner,*args, **kwargs)
 
+
 
+        self.Model = Learner.Model
 
+        self.Select_Model = None
 
+        self.have_Fit = Learner.have_Fit
 
+        self.Model_Name = 'Select_Model'
 
+        self.Learner = Learner
 
+        self.x_trainData = Learner.x_trainData.copy()
 
+        self.y_trainData = Learner.y_trainData.copy()
 
+        self.means = []
 
+
 
+    def Fit(self,x_data,*args,**kwargs):
 
+        try:
 
+            self.means = x_data.ravel()
 
+        except:
 
+            pass
 
+        self.have_Fit = True
 
+        return 'None','None'
 
+
 
+    def Des(self,Dic,Decision_boundary,Prediction_boundary,*args,**kwargs):
 
+        tab = Tab()
 
+        y = self.y_trainData
 
+        x_data = self.x_trainData
 
+        try:#如果没有class
 
+            class_ = self.Model.classes_.tolist()
 
+            class_heard = [f'类别[{i}]' for i in range(len(class_))]
 
+
 
+            #获取数据
 
+            get,x_means,x_range,Type = Training_visualization(x_data,class_,y)
 
+            #可使用自带的means，并且nan表示跳过
 
+            for i in range(min([len(x_means),len(self.means)])):
 
+                try:
 
+                    g = self.means[i]
 
+                    if g == np.nan:raise Exception
 
+                    x_means[i] = g
 
+                except:pass
 
+            get = Decision_boundary(x_range,x_means,self.Learner.Predict,class_,Type)
 
+            for i in range(len(get)):
 
+                tab.add(get[i], f'{i}预测热力图')
 
+
 
+            heard = class_heard + [f'普适预测第{i}特征' for i in range(len(x_means))]
 
+            data = class_ + [f'{i}' for i in x_means]
 
+            c = Table().add(headers=heard, rows=[data])
 
+            tab.add(c, '数据表')
 
+        except:
 
+            get, x_means, x_range,Type = regress_visualization(x_data, y)
 
+
 
+            get = Prediction_boundary(x_range, x_means, self.Learner.Predict, Type)
 
+            for i in range(len(get)):
 
+                tab.add(get[i], f'{i}预测热力图')
 
+
 
+            heard = [f'普适预测第{i}特征' for i in range(len(x_means))]
 
+            data = [f'{i}' for i in x_means]
 
+            c = Table().add(headers=heard, rows=[data])
 
+            tab.add(c, '数据表')
 
+
 
+        save = Dic + r'/预测热力图.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Predictive_HeatMap(Predictive_HeatMap_Base):#绘制预测型热力图
 
+    def Des(self,Dic,*args,**kwargs):
 
+        return super().Des(Dic,Decision_boundary,Prediction_boundary)
 
+
 
+class Predictive_HeatMap_More(Predictive_HeatMap_Base):#绘制预测型热力图_More
 
+    def Des(self,Dic,*args,**kwargs):
 
+        return super().Des(Dic,Decision_boundary_More,Prediction_boundary_More)
 
+
 
+class Near_feature_scatter_class_More(To_PyeBase):
 
+    def Des(self, Dic, *args, **kwargs):
 
+        tab = Tab()
 
+        x_data = self.x_trainData
 
+        y = self.y_trainData
 
+        class_ = np.unique(y).ravel().tolist()
 
+        class_heard = [f'簇[{i}]' for i in range(len(class_))]
 
+
 
+        get, x_means, x_range, Type = Training_visualization_More_NoCenter(x_data, class_, y)
 
+        for i in range(len(get)):
 
+            tab.add(get[i], f'{i}训练数据散点图')
 
+
 
+        heard = class_heard + [f'普适预测第{i}特征' for i in range(len(x_means))]
 
+        data = class_ + [f'{i}' for i in x_means]
 
+        c = Table().add(headers=heard, rows=[data])
 
+        tab.add(c, '数据表')
 
+
 
+        save = Dic + r'/数据特征散点图(分类).HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Near_feature_scatter_More(To_PyeBase):
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        x_data = self.x_trainData
 
+        x_means = make_Cat(x_data).get()[0]
 
+        get_y = Feature_visualization(x_data, '数据散点图')  # 转换
 
+        for i in range(len(get_y)):
 
+            tab.add(get_y[i], f'[{i}]数据x-x散点图')
 
+
 
+        heard = [f'普适预测第{i}特征' for i in range(len(x_means))]
 
+        data = [f'{i}' for i in x_means]
 
+        c = Table().add(headers=heard, rows=[data])
 
+        tab.add(c, '数据表')
 
+
 
+        save = Dic + r'/数据特征散点图.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Near_feature_scatter_class(To_PyeBase):#临近特征散点图：分类数据
 
+    def Des(self,Dic,*args,**kwargs):
 
+        #获取数据
 
+        class_ = np.unique(self.y_trainData).ravel().tolist()
 
+        class_heard = [f'类别[{i}]' for i in range(len(class_))]
 
+        tab = Tab()
 
+
 
+        y = self.y_trainData
 
+        x_data = self.x_trainData
 
+        get, x_means, x_range, Type = Training_visualization(x_data, class_, y)
 
+        for i in range(len(get)):
 
+            tab.add(get[i], f'{i}临近特征散点图')
 
+
 
+        heard = class_heard + [f'普适预测第{i}特征' for i in range(len(x_means))]
 
+        data = class_ + [f'{i}' for i in x_means]
 
+        c = Table().add(headers=heard, rows=[data])
 
+        tab.add(c, '数据表')
 
+
 
+        save = Dic + r'/临近数据特征散点图(分类).HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Near_feature_scatter(To_PyeBase):#临近特征散点图：连续数据
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        x_data = self.x_trainData.T
 
+        y = self.y_trainData
 
+
 
+        get, x_means, x_range,Type = Training_visualization_NoClass(x_data)
 
+        for i in range(len(get)):
 
+            tab.add(get[i], f'{i}临近特征散点图')
 
+
 
+        columns = [f'普适预测第{i}特征' for i in range(len(x_means))]
 
+        data = [f'{i}' for i in x_means]
 
+        tab.add(make_Tab(columns,[data]), '数据表')
 
+
 
+        save = Dic + r'/临近数据特征散点图.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Feature_scatter_YX(To_PyeBase):#y-x图
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        x_data = self.x_trainData
 
+        y = self.y_trainData
 
+
 
+        get, x_means, x_range,Type = regress_visualization(x_data,y)
 
+        for i in range(len(get)):
 
+            tab.add(get[i], f'{i}特征x-y散点图')
 
+
 
+        columns = [f'普适预测第{i}特征' for i in range(len(x_means))]
 
+        data = [f'{i}' for i in x_means]
 
+        tab.add(make_Tab(columns,[data]), '数据表')
 
+
 
+        save = Dic + r'/特征y-x图像.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Line_Model(Study_MachineBase):
 
+    def __init__(self,args_use,model,*args,**kwargs):#model表示当前选用的模型类型,Alpha针对正则化的参数
 
+        super(Line_Model, self).__init__(*args,**kwargs)
 
+        Model = {'Line':LinearRegression,'Ridge':Ridge,'Lasso':Lasso}[
 
+            model]
 
+        if model == 'Line':
 
+            self.Model = Model()
 
+            self.k = {}
 
+        else:
 
+            self.Model = Model(alpha=args_use['alpha'],max_iter=args_use['max_iter'])
 
+            self.k = {'alpha':args_use['alpha'],'max_iter':args_use['max_iter']}
 
+        #记录这两个是为了克隆
 
+        self.Alpha = args_use['alpha']
 
+        self.max_iter = args_use['max_iter']
 
+        self.Model_Name = model
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        x_data = self.x_trainData
 
+        y = self.y_trainData
 
+        w_list = self.Model.coef_.tolist()
 
+        w_heard = [f'系数w[{i}]' for i in range(len(w_list))]
 
+        b = self.Model.intercept_.tolist()
 
+
 
+        get, x_means, x_range,Type = regress_visualization(x_data, y)
 
+        get_Line = Regress_W(x_data, y, w_list, b, x_means.copy())
 
+        for i in range(len(get)):
 
+            tab.add(get[i].overlap(get_Line[i]), f'{i}预测类型图')
 
+
 
+        get = Prediction_boundary(x_range, x_means, self.Predict, Type)
 
+        for i in range(len(get)):
 
+            tab.add(get[i], f'{i}预测热力图')
 
+
 
+        tab.add(scatter(w_heard,w_list),'系数w散点图')
 
+        tab.add(bar(w_heard,self.Model.coef_),'系数柱状图')
 
+
 
+        columns = [f'普适预测第{i}特征' for i in range(len(x_means))] + w_heard + ['截距b']
 
+        data = [f'{i}' for i in x_means] + w_list + [b]
 
+        if self.Model_Name != 'Line':
 
+            columns += ['阿尔法','最大迭代次数']
 
+            data += [self.Model.alpha,self.Model.max_iter]
 
+        tab.add(make_Tab(columns,[data]), '数据表')
 
+
 
+        desTo_CSV(Dic, '系数表', [w_list] + [b], [f'系数W[{i}]' for i in range(len(w_list))] + ['截距'])
 
+        desTo_CSV(Dic, '预测表', [[f'{i}' for i in x_means]], [f'普适预测第{i}特征' for i in range(len(x_means))])
 
+
 
+        save = Dic + r'/线性回归模型.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class LogisticRegression_Model(Study_MachineBase):
 
+    def __init__(self,args_use,model,*args,**kwargs):#model表示当前选用的模型类型,Alpha针对正则化的参数
 
+        super(LogisticRegression_Model, self).__init__(*args,**kwargs)
 
+        self.Model = LogisticRegression(C=args_use['C'],max_iter=args_use['max_iter'])
 
+        #记录这两个是为了克隆
 
+        self.C = args_use['C']
 
+        self.max_iter = args_use['max_iter']
 
+        self.k = {'C':args_use['C'],'max_iter':args_use['max_iter']}
 
+        self.Model_Name = model
 
+
 
+    def Des(self,Dic='render.html',*args,**kwargs):
 
+        #获取数据
 
+        w_array = self.Model.coef_
 
+        w_list = w_array.tolist()  # 变为表格
 
+        b = self.Model.intercept_
 
+        c = self.Model.C
 
+        max_iter = self.Model.max_iter
 
+        class_ = self.Model.classes_.tolist()
 
+        class_heard = [f'类别[{i}]' for i in range(len(class_))]
 
+        tab = Tab()
 
+
 
+        y = self.y_trainData
 
+        x_data = self.x_trainData
 
+        get, x_means, x_range, Type = Training_visualization(x_data, class_, y)
 
+        get_Line = Training_W(x_data, class_, y, w_list, b, x_means.copy())
 
+        for i in range(len(get)):
 
+            tab.add(get[i].overlap(get_Line[i]), f'{i}决策边界散点图')
 
+
 
+        for i in range(len(w_list)):
 
+            w = w_list[i]
 
+            w_heard = [f'系数w[{i},{j}]' for j in range(len(w))]
 
+            tab.add(scatter(w_heard, w), f'系数w[{i}]散点图')
 
+            tab.add(bar(w_heard, w_array[i]), f'系数w[{i}]柱状图')
 
+
 
+        columns = class_heard + [f'截距{i}' for i in range(len(b))] + ['C', '最大迭代数']
 
+        data = class_ + b.tolist() + [c, max_iter]
 
+        c = Table().add(headers=columns, rows=[data])
 
+        tab.add(c, '数据表')
 
+        c = Table().add(headers=[f'系数W[{i}]' for i in range(len(w_list[0]))], rows=w_list)
 
+        tab.add(c, '系数数据表')
 
+
 
+        c = Table().add(headers=[f'普适预测第{i}特征' for i in range(len(x_means))], rows=[[f'{i}' for i in x_means]])
 
+        tab.add(c, '普适预测数据表')
 
+
 
+        desTo_CSV(Dic, '系数表', w_list, [f'系数W[{i}]' for i in range(len(w_list[0]))])
 
+        desTo_CSV(Dic, '截距表', [b], [f'截距{i}' for i in range(len(b))])
 
+        desTo_CSV(Dic, '预测表', [[f'{i}' for i in x_means]], [f'普适预测第{i}特征' for i in range(len(x_means))])
 
+
 
+        save = Dic + r'/逻辑回归.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Categorical_Data:#数据统计助手
 
+    def __init__(self):
 
+        self.x_means = []
 
+        self.x_range = []
 
+        self.Type = []
 
+
 
+    def __call__(self,x1, *args, **kwargs):
 
+        get = self.is_continuous(x1)
 
+        return get
 
+
 
+    def is_continuous(self,x1:np.array):
 
+        try:
 
+            x1_con = is_continuous(x1)
 
+            if x1_con:
 
+                self.x_means.append(np.mean(x1))
 
+                self.add_Range(x1)
 
+            else:
 
+                raise Exception
 
+            return x1_con
 
+        except:#找出出现次数最多的元素
 
+            new = np.unique(x1)#去除相同的元素
 
+            count_list = []
 
+            for i in new:
 
+                count_list.append(np.sum(x1 == i))
 
+            index = count_list.index(max(count_list))#找出最大值的索引
 
+            self.x_means.append(x1[index])
 
+            self.add_Range(x1,False)
 
+            return False
 
+
 
+    def add_Range(self,x1:np.array,range_=True):
 
+        try:
 
+            if not range_ : raise Exception
 
+            min_ = int(x1.min()) - 1
 
+            max_ = int(x1.max()) + 1
 
+            #不需要复制列表
 
+            self.x_range.append([min_,max_])
 
+            self.Type.append(1)
 
+        except:
 
+            self.x_range.append(list(set(x1.tolist())))#去除多余元素
 
+            self.Type.append(2)
 
+
 
+    def get(self):
 
+        return self.x_means,self.x_range,self.Type
 
+
 
+class Knn_Model(Study_MachineBase):
 
+    def __init__(self,args_use,model,*args,**kwargs):#model表示当前选用的模型类型,Alpha针对正则化的参数
 
+        super(Knn_Model, self).__init__(*args,**kwargs)
 
+        Model = {'Knn_class':KNeighborsClassifier,'Knn':KNeighborsRegressor}[model]
 
+        self.Model = Model(p=args_use['p'],n_neighbors=args_use['n_neighbors'])
 
+        #记录这两个是为了克隆
 
+        self.n_neighbors = args_use['n_neighbors']
 
+        self.p = args_use['p']
 
+        self.k = {'n_neighbors':args_use['n_neighbors'],'p':args_use['p']}
 
+        self.Model_Name = model
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        y = self.y_trainData
 
+        x_data = self.x_trainData
 
+        y_test = self.y_testData
 
+        x_test = self.x_testData
 
+        if self.Model_Name == 'Knn_class':
 
+            class_ = self.Model.classes_.tolist()
 
+            class_heard = [f'类别[{i}]' for i in range(len(class_))]
 
+
 
+            get,x_means,x_range,Type = Training_visualization(x_data,class_,y)
 
+            for i in range(len(get)):
 
+                tab.add(get[i],f'{i}训练数据散点图')
 
+
 
+            if not y_test is None:
 
+                get = Training_visualization(x_test,class_,y_test)[0]
 
+                for i in range(len(get)):
 
+                    tab.add(get[i],f'{i}测试数据散点图')
 
+
 
+            get = Decision_boundary(x_range,x_means,self.Predict,class_,Type)
 
+            for i in range(len(get)):
 
+                tab.add(get[i], f'{i}预测热力图')
 
+
 
+            heard = class_heard + [f'普适预测第{i}特征' for i in range(len(x_means))]
 
+            data = class_ + [f'{i}' for i in x_means]
 
+            c = Table().add(headers=heard, rows=[data])
 
+            tab.add(c, '数据表')
 
+        else:
 
+            get, x_means, x_range,Type = regress_visualization(x_data, y)
 
+            for i in range(len(get)):
 
+                tab.add(get[i], f'{i}训练数据散点图')
 
+
 
+            get = regress_visualization(x_test, y_test)[0]
 
+            for i in range(len(get)):
 
+                tab.add(get[i], f'{i}测试数据类型图')
 
+
 
+            get = Prediction_boundary(x_range, x_means, self.Predict, Type)
 
+            for i in range(len(get)):
 
+                tab.add(get[i], f'{i}预测热力图')
 
+
 
+            heard = [f'普适预测第{i}特征' for i in range(len(x_means))]
 
+            data = [f'{i}' for i in x_means]
 
+            c = Table().add(headers=heard, rows=[data])
 
+            tab.add(c, '数据表')
 
+        desTo_CSV(Dic, '预测表', [[f'{i}' for i in x_means]], [f'普适预测第{i}特征' for i in range(len(x_means))])
 
+        save = Dic + r'/K.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Tree_Model(Study_MachineBase):
 
+    def __init__(self,args_use,model,*args,**kwargs):#model表示当前选用的模型类型,Alpha针对正则化的参数
 
+        super(Tree_Model, self).__init__(*args,**kwargs)
 
+        Model = {'Tree_class':DecisionTreeClassifier,'Tree':DecisionTreeRegressor}[model]
 
+        self.Model = Model(criterion=args_use['criterion'],splitter=args_use['splitter'],max_features=args_use['max_features']
 
+                           ,max_depth=args_use['max_depth'],min_samples_split=args_use['min_samples_split'])
 
+        #记录这两个是为了克隆
 
+        self.criterion = args_use['criterion']
 
+        self.splitter = args_use['splitter']
 
+        self.max_features = args_use['max_features']
 
+        self.max_depth = args_use['max_depth']
 
+        self.min_samples_split = args_use['min_samples_split']
 
+        self.k = {'criterion':args_use['criterion'],'splitter':args_use['splitter'],'max_features':args_use['max_features'],
 
+                  'max_depth':args_use['max_depth'],'min_samples_split':args_use['min_samples_split']}
 
+        self.Model_Name = model
 
+
 
+    def Des(self, Dic, *args, **kwargs):
 
+        tab = Tab()
 
+        importance = self.Model.feature_importances_.tolist()
 
+
 
+        with open(Dic + r"\Tree_Gra.dot", 'w') as f:
 
+            export_graphviz(self.Model, out_file=f)
 
+
 
+        make_bar('特征重要性',importance,tab)
 
+        desTo_CSV(Dic, '特征重要性', [importance], [f'[{i}]特征' for i in range(len(importance))])
 
+        tab.add(SeeTree(Dic + r"\Tree_Gra.dot"),'决策树可视化')
 
+
 
+        y = self.y_trainData
 
+        x_data = self.x_trainData
 
+        y_test = self.y_testData
 
+        x_test = self.x_testData
 
+        if self.Model_Name == 'Tree_class':
 
+            class_ = self.Model.classes_.tolist()
 
+            class_heard = [f'类别[{i}]' for i in range(len(class_))]
 
+
 
+            get,x_means,x_range,Type = Training_visualization(x_data,class_,y)
 
+            for i in range(len(get)):
 
+                tab.add(get[i],f'{i}训练数据散点图')
 
+
 
+            get = Training_visualization(x_test, class_, y_test)[0]
 
+            for i in range(len(get)):
 
+                tab.add(get[i], f'{i}测试数据散点图')
 
+
 
+            get = Decision_boundary(x_range,x_means,self.Predict,class_,Type)
 
+            for i in range(len(get)):
 
+                tab.add(get[i], f'{i}预测热力图')
 
+
 
+            tab.add(make_Tab(class_heard + [f'普适预测第{i}特征' for i in range(len(x_means))] + [f'特征{i}重要性' for i in range(len(importance))],
 
+                             [class_ + [f'{i}' for i in x_means] + importance]), '数据表')
 
+        else:
 
+            get, x_means, x_range,Type = regress_visualization(x_data, y)
 
+            for i in range(len(get)):
 
+                tab.add(get[i], f'{i}训练数据散点图')
 
+
 
+            get = regress_visualization(x_test, y_test)[0]
 
+            for i in range(len(get)):
 
+                tab.add(get[i], f'{i}测试数据类型图')
 
+
 
+            get = Prediction_boundary(x_range, x_means, self.Predict, Type)
 
+            for i in range(len(get)):
 
+                tab.add(get[i], f'{i}预测热力图')
 
+
 
+            tab.add(make_Tab([f'普适预测第{i}特征' for i in range(len(x_means))] + [f'特征{i}重要性' for i in range(len(importance))],
 
+                             [[f'{i}' for i in x_means] + importance]), '数据表')
 
+        desTo_CSV(Dic, '预测表', [[f'{i}' for i in x_means]], [f'普适预测第{i}特征' for i in range(len(x_means))])
 
+        save = Dic + r'/决策树.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Forest_Model(Study_MachineBase):
 
+    def __init__(self,args_use,model,*args,**kwargs):#model表示当前选用的模型类型,Alpha针对正则化的参数
 
+        super(Forest_Model, self).__init__(*args,**kwargs)
 
+        Model = {'Forest_class':RandomForestClassifier,'Forest':RandomForestRegressor}[model]
 
+        self.Model = Model(n_estimators=args_use['n_Tree'],criterion=args_use['criterion'],max_features=args_use['max_features']
 
+                           ,max_depth=args_use['max_depth'],min_samples_split=args_use['min_samples_split'])
 
+        #记录这两个是为了克隆
 
+        self.n_estimators = args_use['n_Tree']
 
+        self.criterion = args_use['criterion']
 
+        self.max_features = args_use['max_features']
 
+        self.max_depth = args_use['max_depth']
 
+        self.min_samples_split = args_use['min_samples_split']
 
+        self.k = {'n_estimators':args_use['n_Tree'],'criterion':args_use['criterion'],'max_features':args_use['max_features'],
 
+                  'max_depth':args_use['max_depth'],'min_samples_split':args_use['min_samples_split']}
 
+        self.Model_Name = model
 
+
 
+    def Des(self, Dic, *args, **kwargs):
 
+        tab = Tab()
 
+        #多个决策树可视化
 
+        for i in range(len(self.Model.estimators_)):
 
+            with open(Dic + f"\Tree_Gra[{i}].dot", 'w') as f:
 
+                export_graphviz(self.Model.estimators_[i], out_file=f)
 
+
 
+            tab.add(SeeTree(Dic + f"\Tree_Gra[{i}].dot"),f'[{i}]决策树可视化')
 
+
 
+        y = self.y_trainData
 
+        x_data = self.x_trainData
 
+        if self.Model_Name == 'Forest_class':
 
+            class_ = self.Model.classes_.tolist()
 
+            class_heard = [f'类别[{i}]' for i in range(len(class_))]
 
+
 
+            get,x_means,x_range,Type = Training_visualization(x_data,class_,y)
 
+            for i in range(len(get)):
 
+                tab.add(get[i],f'{i}训练数据散点图')
 
+
 
+            get = Decision_boundary(x_range,x_means,self.Predict,class_,Type)
 
+            for i in range(len(get)):
 
+                tab.add(get[i], f'{i}预测热力图')
 
+
 
+            tab.add(make_Tab(class_heard + [f'普适预测第{i}特征' for i in range(len(x_means))],
 
+                             [class_ + [f'{i}' for i in x_means]]), '数据表')
 
+        else:
 
+            get, x_means, x_range,Type = regress_visualization(x_data, y)
 
+            for i in range(len(get)):
 
+                tab.add(get[i], f'{i}预测类型图')
 
+
 
+            get = Prediction_boundary(x_range, x_means, self.Predict, Type)
 
+            for i in range(len(get)):
 
+                tab.add(get[i], f'{i}预测热力图')
 
+
 
+            tab.add(make_Tab([f'普适预测第{i}特征' for i in range(len(x_means))],[[f'{i}' for i in x_means]]), '数据表')
 
+        desTo_CSV(Dic, '预测表', [[f'{i}' for i in x_means]], [f'普适预测第{i}特征' for i in range(len(x_means))])
 
+        save = Dic + r'/随机森林.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class GradientTree_Model(Study_MachineBase):#继承Tree_Model主要是继承Des
 
+    def __init__(self,args_use,model,*args,**kwargs):#model表示当前选用的模型类型,Alpha针对正则化的参数
 
+        super(GradientTree_Model, self).__init__(*args,**kwargs)#不需要执行Tree_Model的初始化
 
+        Model = {'GradientTree_class':GradientBoostingClassifier,'GradientTree':GradientBoostingRegressor}[model]
 
+        self.Model = Model(n_estimators=args_use['n_Tree'],max_features=args_use['max_features']
 
+                           ,max_depth=args_use['max_depth'],min_samples_split=args_use['min_samples_split'])
 
+        #记录这两个是为了克隆
 
+        self.criterion = args_use['criterion']
 
+        self.splitter = args_use['splitter']
 
+        self.max_features = args_use['max_features']
 
+        self.max_depth = args_use['max_depth']
 
+        self.min_samples_split = args_use['min_samples_split']
 
+        self.k = {'criterion':args_use['criterion'],'splitter':args_use['splitter'],'max_features':args_use['max_features'],
 
+                  'max_depth':args_use['max_depth'],'min_samples_split':args_use['min_samples_split']}
 
+        self.Model_Name = model
 
+
 
+    def Des(self, Dic, *args, **kwargs):
 
+        tab = Tab()
 
+        #多个决策树可视化
 
+        for a in range(len(self.Model.estimators_)):
 
+            for i in range(len(self.Model.estimators_[a])):
 
+                with open(Dic + f"\Tree_Gra[{a},{i}].dot", 'w') as f:
 
+                    export_graphviz(self.Model.estimators_[a][i], out_file=f)
 
+
 
+                tab.add(SeeTree(Dic + f"\Tree_Gra[{a},{i}].dot"),f'[{a},{i}]决策树可视化')
 
+
 
+        y = self.y_trainData
 
+        x_data = self.x_trainData
 
+        if self.Model_Name == 'Tree_class':
 
+            class_ = self.Model.classes_.tolist()
 
+            class_heard = [f'类别[{i}]' for i in range(len(class_))]
 
+
 
+            get,x_means,x_range,Type = Training_visualization(x_data,class_,y)
 
+            for i in range(len(get)):
 
+                tab.add(get[i],f'{i}训练数据散点图')
 
+
 
+            get = Decision_boundary(x_range,x_means,self.Predict,class_,Type)
 
+            for i in range(len(get)):
 
+                tab.add(get[i], f'{i}预测热力图')
 
+
 
+            tab.add(make_Tab(class_heard + [f'普适预测第{i}特征' for i in range(len(x_means))],
 
+                             [class_ + [f'{i}' for i in x_means]]), '数据表')
 
+        else:
 
+            get, x_means, x_range,Type = regress_visualization(x_data, y)
 
+            for i in range(len(get)):
 
+                tab.add(get[i], f'{i}预测类型图')
 
+
 
+            get = Prediction_boundary(x_range, x_means, self.Predict, Type)
 
+            for i in range(len(get)):
 
+                tab.add(get[i], f'{i}预测热力图')
 
+
 
+            tab.add(make_Tab([f'普适预测第{i}特征' for i in range(len(x_means))],[[f'{i}' for i in x_means]]), '数据表')
 
+        desTo_CSV(Dic, '预测表', [[f'{i}' for i in x_means]], [f'普适预测第{i}特征' for i in range(len(x_means))])
 
+        save = Dic + r'/梯度提升回归树.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class SVC_Model(Study_MachineBase):
 
+    def __init__(self,args_use,model,*args,**kwargs):#model表示当前选用的模型类型,Alpha针对正则化的参数
 
+        super(SVC_Model, self).__init__(*args,**kwargs)
 
+        self.Model = SVC(C=args_use['C'],gamma=args_use['gamma'],kernel=args_use['kernel'])
 
+        #记录这两个是为了克隆
 
+        self.C = args_use['C']
 
+        self.gamma = args_use['gamma']
 
+        self.kernel = args_use['kernel']
 
+        self.k = {'C':args_use['C'],'gamma':args_use['gamma'],'kernel':args_use['kernel']}
 
+        self.Model_Name = model
 
+
 
+    def Des(self, Dic, *args, **kwargs):
 
+        tab = Tab()
 
+        try:
 
+            w_list = self.Model.coef_.tolist()  # 未必有这个属性
 
+            b = self.Model.intercept_.tolist()
 
+            U = True
 
+        except:
 
+            U = False
 
+        class_ = self.Model.classes_.tolist()
 
+        class_heard = [f'类别[{i}]' for i in range(len(class_))]
 
+
 
+        y = self.y_trainData
 
+        x_data = self.x_trainData
 
+        get, x_means, x_range, Type = Training_visualization(x_data, class_, y)
 
+        if U:get_Line = Training_W(x_data, class_, y, w_list, b, x_means.copy())
 
+        for i in range(len(get)):
 
+            if U:tab.add(get[i].overlap(get_Line[i]), f'{i}决策边界散点图')
 
+            else:tab.add(get[i], f'{i}决策边界散点图')
 
+
 
+        get = Decision_boundary(x_range, x_means, self.Predict, class_, Type)
 
+        for i in range(len(get)):
 
+            tab.add(get[i], f'{i}预测热力图')
 
+
 
+        dic = {2:'离散',1:'连续'}
 
+        tab.add(make_Tab(class_heard + [f'普适预测第{i}特征:{dic[Type[i]]}' for i in range(len(x_means))],
 
+                         [class_ + [f'{i}' for i in x_means]]), '数据表')
 
+
 
+        if U:desTo_CSV(Dic, '系数表', w_list, [f'系数W[{i}]' for i in range(len(w_list[0]))])
 
+        if U:desTo_CSV(Dic, '截距表', [b], [f'截距{i}' for i in range(len(b))])
 
+        desTo_CSV(Dic, '预测表', [[f'{i}' for i in x_means]], [f'普适预测第{i}特征' for i in range(len(x_means))])
 
+
 
+        save = Dic + r'/支持向量机分类.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class SVR_Model(Study_MachineBase):
 
+    def __init__(self,args_use,model,*args,**kwargs):#model表示当前选用的模型类型,Alpha针对正则化的参数
 
+        super(SVR_Model, self).__init__(*args,**kwargs)
 
+        self.Model = SVR(C=args_use['C'],gamma=args_use['gamma'],kernel=args_use['kernel'])
 
+        #记录这两个是为了克隆
 
+        self.C = args_use['C']
 
+        self.gamma = args_use['gamma']
 
+        self.kernel = args_use['kernel']
 
+        self.k = {'C':args_use['C'],'gamma':args_use['gamma'],'kernel':args_use['kernel']}
 
+        self.Model_Name = model
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        x_data = self.x_trainData
 
+        y = self.y_trainData
 
+        try:
 
+            w_list = self.Model.coef_.tolist()#未必有这个属性
 
+            b = self.Model.intercept_.tolist()
 
+            U = True
 
+        except:
 
+            U = False
 
+
 
+        get, x_means, x_range,Type = regress_visualization(x_data, y)
 
+        if U:get_Line = Regress_W(x_data, y, w_list, b, x_means.copy())
 
+        for i in range(len(get)):
 
+            if U:tab.add(get[i].overlap(get_Line[i]), f'{i}预测类型图')
 
+            else:tab.add(get[i], f'{i}预测类型图')
 
+
 
+        get = Prediction_boundary(x_range, x_means, self.Predict, Type)
 
+        for i in range(len(get)):
 
+            tab.add(get[i], f'{i}预测热力图')
 
+
 
+        if U: desTo_CSV(Dic, '系数表', w_list, [f'系数W[{i}]' for i in range(len(w_list[0]))])
 
+        if U: desTo_CSV(Dic, '截距表', [b], [f'截距{i}' for i in range(len(b))])
 
+        desTo_CSV(Dic, '预测表', [[f'{i}' for i in x_means]], [f'普适预测第{i}特征' for i in range(len(x_means))])
 
+
 
+        tab.add(make_Tab([f'普适预测第{i}特征' for i in range(len(x_means))],[[f'{i}' for i in x_means]]), '数据表')
 
+        save = Dic + r'/支持向量机回归.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Variance_Model(Unsupervised):#无监督
 
+    def __init__(self,args_use,model,*args,**kwargs):#model表示当前选用的模型类型,Alpha针对正则化的参数
 
+        super(Variance_Model, self).__init__(*args,**kwargs)
 
+        self.Model = VarianceThreshold(threshold=(args_use['P'] * (1 - args_use['P'])))
 
+        #记录这两个是为了克隆
 
+        self.threshold = args_use['P']
 
+        self.k = {'threshold':args_use['P']}
 
+        self.Model_Name = model
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        var = self.Model.variances_#标准差
 
+        y_data = self.y_testData
 
+        if type(y_data) is np.ndarray:
 
+            get = Feature_visualization(self.y_testData)
 
+            for i in range(len(get)):
 
+                tab.add(get[i],f'[{i}]数据x-x散点图')
 
+
 
+        c = (
 
+            Bar()
 
+                .add_xaxis([f'[{i}]特征' for i in range(len(var))])
 
+                .add_yaxis('标准差', var.tolist(), **Label_Set)
 
+                .set_global_opts(title_opts=opts.TitleOpts(title='系数w柱状图'), **global_Set)
 
+        )
 
+        tab.add(c,'数据标准差')
 
+        save = Dic + r'/方差特征选择.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class SelectKBest_Model(prep_Base):#有监督
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(SelectKBest_Model, self).__init__(*args, **kwargs)
 
+        self.Model = SelectKBest(k=args_use['k'],score_func=args_use['score_func'])
 
+        # 记录这两个是为了克隆
 
+        self.k_ = args_use['k']
 
+        self.score_func=args_use['score_func']
 
+        self.k = {'k':args_use['k'],'score_func':args_use['score_func']}
 
+        self.Model_Name = model
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        score = self.Model.scores_.tolist()
 
+        support = self.Model.get_support()
 
+        y_data = self.y_trainData
 
+        x_data = self.x_trainData
 
+        if type(x_data) is np.ndarray:
 
+            get = Feature_visualization(x_data)
 
+            for i in range(len(get)):
 
+                tab.add(get[i],f'[{i}]训练数据x-x散点图')
 
+
 
+        if type(y_data) is np.ndarray:
 
+            get = Feature_visualization(y_data)
 
+            for i in range(len(get)):
 
+                tab.add(get[i],f'[{i}]保留训练数据x-x散点图')
 
+
 
+        y_data = self.y_testData
 
+        x_data = self.x_testData
 
+        if type(x_data) is np.ndarray:
 
+            get = Feature_visualization(x_data)
 
+            for i in range(len(get)):
 
+                tab.add(get[i],f'[{i}]数据x-x散点图')
 
+
 
+        if type(y_data) is np.ndarray:
 
+            get = Feature_visualization(y_data)
 
+            for i in range(len(get)):
 
+                tab.add(get[i],f'[{i}]保留数据x-x散点图')
 
+
 
+        Choose = []
 
+        UnChoose = []
 
+        for i in range(len(score)):
 
+            if support[i]:
 
+                Choose.append(score[i])
 
+                UnChoose.append(0)#占位
 
+            else:
 
+                UnChoose.append(score[i])
 
+                Choose.append(0)
 
+
 
+        c = (
 
+            Bar()
 
+                .add_xaxis([f'[{i}]特征' for i in range(len(score))])
 
+                .add_yaxis('选中特征', Choose, **Label_Set)
 
+                .add_yaxis('抛弃特征', UnChoose, **Label_Set)
 
+                .set_global_opts(title_opts=opts.TitleOpts(title='系数w柱状图'), **global_Set)
 
+        )
 
+        tab.add(c,'单变量重要程度')
 
+
 
+        save = Dic + r'/单一变量特征选择.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class SelectFrom_Model(prep_Base):#有监督
 
+    def __init__(self, args_use, Learner, *args, **kwargs):  # model表示当前选用的模型类型,Alpha针对正则化的参数
 
+        super(SelectFrom_Model, self).__init__(*args, **kwargs)
 
+
 
+        self.Model = Learner.Model
 
+        self.Select_Model = SelectFromModel(estimator=Learner.Model,max_features=args_use['k'],prefit=Learner.have_Fit)
 
+        self.max_features = args_use['k']
 
+        self.estimator=Learner.Model
 
+        self.k = {'max_features':args_use['k'],'estimator':Learner.Model,'have_Fit':Learner.have_Fit}
 
+        self.have_Fit = Learner.have_Fit
 
+        self.Model_Name = 'SelectFrom_Model'
 
+        self.Learner = Learner
 
+
 
+    def Fit(self, x_data,y_data,split=0.3, *args, **kwargs):
 
+        y_data = y_data.ravel()
 
+        if not self.have_Fit:  # 不允许第二次训练
 
+            self.Select_Model.fit(x_data, y_data)
 
+        self.have_Fit = True
 
+        return 'None','None'
 
+
 
+    def Predict(self, x_data, *args, **kwargs):
 
+        try:
 
+            self.x_testData = x_data.copy()
 
+            x_Predict = self.Select_Model.transform(x_data)
 
+            self.y_testData = x_Predict.copy()
 
+            self.have_Predict = True
 
+            return x_Predict,'模型特征工程'
 
+        except:
 
+            self.have_Predict = True
 
+            return np.array([]),'无结果工程'
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        support = self.Select_Model.get_support()
 
+        y_data = self.y_testData
 
+        x_data = self.x_testData
 
+        if type(x_data) is np.ndarray:
 
+            get = Feature_visualization(x_data)
 
+            for i in range(len(get)):
 
+                tab.add(get[i],f'[{i}]数据x-x散点图')
 
+
 
+        if type(y_data) is np.ndarray:
 
+            get = Feature_visualization(y_data)
 
+            for i in range(len(get)):
 
+                tab.add(get[i],f'[{i}]保留数据x-x散点图')
 
+
 
+        def make_Bar(score):
 
+            Choose = []
 
+            UnChoose = []
 
+            for i in range(len(score)):
 
+                if support[i]:
 
+                    Choose.append(abs(score[i]))
 
+                    UnChoose.append(0)  # 占位
 
+                else:
 
+                    UnChoose.append(abs(score[i]))
 
+                    Choose.append(0)
 
+            c = (
 
+                Bar()
 
+                    .add_xaxis([f'[{i}]特征' for i in range(len(score))])
 
+                    .add_yaxis('选中特征', Choose, **Label_Set)
 
+                    .add_yaxis('抛弃特征', UnChoose, **Label_Set)
 
+                    .set_global_opts(title_opts=opts.TitleOpts(title='系数w柱状图'), **global_Set)
 
+            )
 
+            tab.add(c,'单变量重要程度')
 
+
 
+        try:
 
+            make_Bar(self.Model.coef_)
 
+        except:
 
+            try:
 
+                make_Bar(self.Model.feature_importances_)
 
+            except:pass
 
+
 
+        save = Dic + r'/模型特征选择.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Standardization_Model(Unsupervised):#z-score标准化 无监督
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(Standardization_Model, self).__init__(*args, **kwargs)
 
+        self.Model = StandardScaler()
 
+
 
+        self.k = {}
 
+        self.Model_Name = 'StandardScaler'
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        y_data = self.y_testData
 
+        x_data = self.x_testData
 
+        var = self.Model.var_.tolist()
 
+        means = self.Model.mean_.tolist()
 
+        scale = self.Model.scale_.tolist()
 
+        Conversion_control(y_data,x_data,tab)
 
+
 
+        make_bar('标准差',var,tab)
 
+        make_bar('方差',means,tab)
 
+        make_bar('Scale',scale,tab)
 
+
 
+        save = Dic + r'/z-score标准化.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class MinMaxScaler_Model(Unsupervised):#离差标准化
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(MinMaxScaler_Model, self).__init__(*args, **kwargs)
 
+        self.Model = MinMaxScaler(feature_range=args_use['feature_range'])
 
+
 
+        self.k = {}
 
+        self.Model_Name = 'MinMaxScaler'
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        y_data = self.y_testData
 
+        x_data = self.x_testData
 
+        scale = self.Model.scale_.tolist()
 
+        max_ = self.Model.data_max_.tolist()
 
+        min_ = self.Model.data_min_.tolist()
 
+        Conversion_control(y_data,x_data,tab)
 
+        make_bar('Scale',scale,tab)
 
+        tab.add(make_Tab(heard= [f'[{i}]特征最大值' for i in range(len(max_))] + [f'[{i}]特征最小值' for i in range(len(min_))],
 
+                         row=[max_ + min_]), '数据表格')
 
+
 
+        save = Dic + r'/离差标准化.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class LogScaler_Model(prep_Base):#对数标准化
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(LogScaler_Model, self).__init__(*args, **kwargs)
 
+        self.Model = None
 
+
 
+        self.k = {}
 
+        self.Model_Name = 'LogScaler'
 
+
 
+    def Fit(self, x_data, *args, **kwargs):
 
+        if not self.have_Predict:  # 不允许第二次训练
 
+            self.max_logx = np.log(x_data.max())
 
+        self.have_Fit = True
 
+        return 'None', 'None'
 
+
 
+    def Predict(self, x_data, *args, **kwargs):
 
+        try:
 
+            max_logx = self.max_logx
 
+        except:
 
+            self.have_Fit = False
 
+            self.Fit(x_data)
 
+            max_logx = self.max_logx
 
+        self.x_testData = x_data.copy()
 
+        x_Predict = (np.log(x_data)/max_logx)
 
+        self.y_testData = x_Predict.copy()
 
+        self.have_Predict = True
 
+        return x_Predict,'对数变换'
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        y_data = self.y_testData
 
+        x_data = self.x_testData
 
+        Conversion_control(y_data,x_data,tab)
 
+        tab.add(make_Tab(heard=['最大对数值(自然对数)'],row=[[str(self.max_logx)]]),'数据表格')
 
+
 
+        save = Dic + r'/对数标准化.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class atanScaler_Model(prep_Base):#atan标准化
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(atanScaler_Model, self).__init__(*args, **kwargs)
 
+        self.Model = None
 
+
 
+        self.k = {}
 
+        self.Model_Name = 'atanScaler'
 
+
 
+    def Fit(self, x_data, *args, **kwargs):
 
+        self.have_Fit = True
 
+        return 'None', 'None'
 
+
 
+    def Predict(self, x_data, *args, **kwargs):
 
+        self.x_testData = x_data.copy()
 
+        x_Predict = (np.arctan(x_data)*(2/np.pi))
 
+        self.y_testData = x_Predict.copy()
 
+        self.have_Predict = True
 
+        return x_Predict,'atan变换'
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        y_data = self.y_testData
 
+        x_data = self.x_testData
 
+        Conversion_control(y_data,x_data,tab)
 
+
 
+        save = Dic + r'/反正切函数标准化.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class decimalScaler_Model(prep_Base):#小数定标准化
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(decimalScaler_Model, self).__init__(*args, **kwargs)
 
+        self.Model = None
 
+
 
+        self.k = {}
 
+        self.Model_Name = 'Decimal_normalization'
 
+
 
+    def Fit(self, x_data, *args, **kwargs):
 
+        if not self.have_Predict:  # 不允许第二次训练
 
+            self.j = max([judging_Digits(x_data.max()),judging_Digits(x_data.min())])
 
+        self.have_Fit = True
 
+        return 'None', 'None'
 
+
 
+    def Predict(self, x_data, *args, **kwargs):
 
+        self.x_testData = x_data.copy()
 
+        try:
 
+            j = self.j
 
+        except:
 
+            self.have_Fit = False
 
+            self.Fit(x_data)
 
+            j = self.j
 
+        x_Predict = (x_data/(10**j))
 
+        self.y_testData = x_Predict.copy()
 
+        self.have_Predict = True
 
+        return x_Predict,'小数定标标准化'
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        y_data = self.y_testData
 
+        x_data = self.x_testData
 
+        j = self.j
 
+        Conversion_control(y_data,x_data,tab)
 
+        tab.add(make_Tab(heard=['小数位数:j'], row=[[j]]), '数据表格')
 
+
 
+        save = Dic + r'/小数定标标准化.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Mapzoom_Model(prep_Base):#映射标准化
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(Mapzoom_Model, self).__init__(*args, **kwargs)
 
+        self.Model = None
 
+
 
+        self.feature_range = args_use['feature_range']
 
+        self.k = {}
 
+        self.Model_Name = 'Decimal_normalization'
 
+
 
+    def Fit(self, x_data, *args, **kwargs):
 
+        if not self.have_Predict:  # 不允许第二次训练
 
+            self.max = x_data.max()
 
+            self.min = x_data.min()
 
+        self.have_Fit = True
 
+        return 'None', 'None'
 
+
 
+    def Predict(self, x_data, *args, **kwargs):
 
+        self.x_testData = x_data.copy()
 
+        try:
 
+            max = self.max
 
+            min = self.min
 
+        except:
 
+            self.have_Fit = False
 
+            self.Fit(x_data)
 
+            max = self.max
 
+            min = self.min
 
+        x_Predict = (x_data * (self.feature_range[1] - self.feature_range[0])) / (max - min)
 
+        self.y_testData = x_Predict.copy()
 
+        self.have_Predict = True
 
+        return x_Predict,'映射标准化'
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        y_data = self.y_testData
 
+        x_data = self.x_testData
 
+        max = self.max
 
+        min = self.min
 
+        Conversion_control(y_data,x_data,tab)
 
+        tab.add(make_Tab(heard=['最大值','最小值'], row=[[max,min]]), '数据表格')
 
+
 
+        save = Dic + r'/映射标准化.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class sigmodScaler_Model(prep_Base):#sigmod变换
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(sigmodScaler_Model, self).__init__(*args, **kwargs)
 
+        self.Model = None
 
+
 
+        self.k = {}
 
+        self.Model_Name = 'sigmodScaler_Model'
 
+
 
+    def Fit(self, x_data, *args, **kwargs):
 
+        self.have_Fit = True
 
+        return 'None', 'None'
 
+
 
+    def Predict(self, x_data:np.array,*args,**kwargs):
 
+        self.x_testData = x_data.copy()
 
+        x_Predict = (1/(1+np.exp(-x_data)))
 
+        self.y_testData = x_Predict.copy()
 
+        self.have_Predict = True
 
+        return x_Predict,'Sigmod变换'
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        y_data = self.y_testData
 
+        x_data = self.x_testData
 
+        Conversion_control(y_data,x_data,tab)
 
+
 
+        save = Dic + r'/Sigmoid变换.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Fuzzy_quantization_Model(prep_Base):#模糊量化标准化
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(Fuzzy_quantization_Model, self).__init__(*args, **kwargs)
 
+        self.Model = None
 
+
 
+        self.feature_range = args_use['feature_range']
 
+        self.k = {}
 
+        self.Model_Name = 'Fuzzy_quantization'
 
+
 
+    def Fit(self, x_data, *args, **kwargs):
 
+        if not self.have_Predict:  # 不允许第二次训练
 
+            self.max = x_data.max()
 
+            self.min = x_data.min()
 
+        self.have_Fit = True
 
+        return 'None', 'None'
 
+
 
+    def Predict(self, x_data,*args,**kwargs):
 
+        self.x_testData = x_data.copy()
 
+        try:
 
+            max = self.max
 
+            min = self.min
 
+        except:
 
+            self.have_Fit = False
 
+            self.Fit(x_data)
 
+            max = self.max
 
+            min = self.min
 
+        x_Predict = 1 / 2 + (1 / 2) * np.sin(np.pi / (max - min) * (x_data - (max-min) / 2))
 
+        self.y_testData = x_Predict.copy()
 
+        self.have_Predict = True
 
+        return x_Predict,'模糊量化标准化'
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        y_data = self.y_trainData
 
+        x_data = self.x_trainData
 
+        max = self.max
 
+        min = self.min
 
+        Conversion_control(y_data,x_data,tab)
 
+        tab.add(make_Tab(heard=['最大值','最小值'], row=[[max,min]]), '数据表格')
 
+
 
+        save = Dic + r'/模糊量化标准化.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Regularization_Model(Unsupervised):#正则化
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(Regularization_Model, self).__init__(*args, **kwargs)
 
+        self.Model = Normalizer(norm=args_use['norm'])
 
+
 
+        self.k = {'norm':args_use['norm']}
 
+        self.Model_Name = 'Regularization'
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        y_data = self.y_testData.copy()
 
+        x_data = self.x_testData.copy()
 
+        Conversion_control(y_data,x_data,tab)
 
+
 
+        save = Dic + r'/正则化.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+#离散数据
 
+class Binarizer_Model(Unsupervised):#二值化
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(Binarizer_Model, self).__init__(*args, **kwargs)
 
+        self.Model = Binarizer(threshold=args_use['threshold'])
 
+
 
+        self.k = {}
 
+        self.Model_Name = 'Binarizer'
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        y_data = self.y_testData
 
+        x_data = self.x_testData
 
+        get_y = Discrete_Feature_visualization(y_data,'转换数据')#转换
 
+        for i in range(len(get_y)):
 
+            tab.add(get_y[i],f'[{i}]数据x-x离散散点图')
 
+
 
+        heard = [f'特征:{i}' for i in range(len(x_data[0]))]
 
+        tab.add(make_Tab(heard,x_data.tolist()),f'原数据')
 
+        tab.add(make_Tab(heard,y_data.tolist()), f'编码数据')
 
+        tab.add(make_Tab(heard,np.dstack((x_data,y_data)).tolist()), f'合成[原数据,编码]数据')
 
+
 
+        save = Dic + r'/二值离散化.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Discretization_Model(prep_Base):#n值离散
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(Discretization_Model, self).__init__(*args, **kwargs)
 
+        self.Model = None
 
+
 
+        range_ = args_use['split_range']
 
+        if range_ == []:raise Exception
 
+        elif len(range_) == 1:range_.append(range_[0])
 
+        self.range = range_
 
+        self.k = {}
 
+        self.Model_Name = 'Discretization'
 
+
 
+    def Fit(self,*args,**kwargs):
 
+        #t值在模型创建时已经保存
 
+        self.have_Fit = True
 
+        return 'None','None'
 
+
 
+    def Predict(self,x_data,*args,**kwargs):
 
+        self.x_testData = x_data.copy()
 
+        x_Predict = x_data.copy()#复制
 
+        range_ = self.range
 
+        bool_list = []
 
+        max_ = len(range_) - 1
 
+        o_t = None
 
+        for i in range(len(range_)):
 
+            try:
 
+                t = float(range_[i])
 
+            except:continue
 
+            if o_t == None:#第一个参数
 
+                bool_list.append(x_Predict <= t)
 
+            else:
 
+                bool_list.append((o_t <= x_Predict) == (x_Predict < t))
 
+                if i == max_:
 
+                    bool_list.append(t <= x_Predict)
 
+            o_t = t
 
+        for i in range(len(bool_list)):
 
+            x_Predict[bool_list[i]] = i
 
+        self.y_testData = x_Predict.copy()
 
+        self.have_Predict = True
 
+        return x_Predict,f'{len(bool_list)}值离散化'
 
+
 
+    def Des(self, Dic, *args, **kwargs):
 
+        tab = Tab()
 
+        y_data = self.y_testData
 
+        x_data = self.x_testData
 
+        get_y = Discrete_Feature_visualization(y_data, '转换数据')  # 转换
 
+        for i in range(len(get_y)):
 
+            tab.add(get_y[i], f'[{i}]数据x-x离散散点图')
 
+
 
+        heard = [f'特征:{i}' for i in range(len(x_data[0]))]
 
+        tab.add(make_Tab(heard,x_data.tolist()),f'原数据')
 
+        tab.add(make_Tab(heard,y_data.tolist()), f'编码数据')
 
+        tab.add(make_Tab(heard,np.dstack((x_data,y_data)).tolist()), f'合成[原数据,编码]数据')
 
+
 
+        save = Dic + r'/多值离散化.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Label_Model(prep_Base):#数字编码
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(Label_Model, self).__init__(*args, **kwargs)
 
+        self.Model = []
 
+        self.k = {}
 
+        self.Model_Name = 'LabelEncoder'
 
+
 
+    def Fit(self,x_data,*args, **kwargs):
 
+        if not self.have_Predict:  # 不允许第二次训练
 
+            self.Model = []
 
+            if x_data.ndim == 1:x_data = np.array([x_data])
 
+            for i in range(x_data.shape[1]):
 
+                self.Model.append(LabelEncoder().fit(np.ravel(x_data[:,i])))#训练机器(每个特征一个学习器)
 
+        self.have_Fit = True
 
+        return 'None', 'None'
 
+
 
+    def Predict(self, x_data, *args, **kwargs):
 
+        self.x_testData = x_data.copy()
 
+        x_Predict = x_data.copy()
 
+        if x_data.ndim == 1: x_data = np.array([x_data])
 
+        for i in range(x_data.shape[1]):
 
+            x_Predict[:,i] = self.Model[i].transform(x_data[:,i])
 
+        self.y_testData = x_Predict.copy()
 
+        self.have_Predict = True
 
+        return x_Predict,'数字编码'
 
+
 
+    def Des(self, Dic, *args, **kwargs):
 
+        tab = Tab()
 
+        x_data = self.x_testData
 
+        y_data = self.y_testData
 
+        get_y = Discrete_Feature_visualization(y_data, '转换数据')  # 转换
 
+        for i in range(len(get_y)):
 
+            tab.add(get_y[i], f'[{i}]数据x-x离散散点图')
 
+
 
+        heard = [f'特征:{i}' for i in range(len(x_data[0]))]
 
+        tab.add(make_Tab(heard,x_data.tolist()),f'原数据')
 
+        tab.add(make_Tab(heard,y_data.tolist()), f'编码数据')
 
+        tab.add(make_Tab(heard,np.dstack((x_data,y_data)).tolist()), f'合成[原数据,编码]数据')
 
+
 
+        save = Dic + r'/数字编码.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class OneHotEncoder_Model(prep_Base):#独热编码
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(OneHotEncoder_Model, self).__init__(*args, **kwargs)
 
+        self.Model = []
 
+
 
+        self.ndim_up = args_use['ndim_up']
 
+        self.k = {}
 
+        self.Model_Name = 'OneHotEncoder'
 
+        self.OneHot_Data = None#三维独热编码
 
+
 
+    def Fit(self,x_data,*args, **kwargs):
 
+        if not self.have_Predict:  # 不允许第二次训练
 
+            if x_data.ndim == 1:x_data = [x_data]
 
+            for i in range(x_data.shape[1]):
 
+                data = np.expand_dims(x_data[:,i], axis=1)#独热编码需要升维
 
+                self.Model.append(OneHotEncoder().fit(data))#训练机器
 
+        self.have_Fit = True
 
+        return 'None', 'None'
 
+
 
+    def Predict(self, x_data, *args, **kwargs):
 
+        self.x_testData = x_data.copy()
 
+        x_new = []
 
+        for i in range(x_data.shape[1]):
 
+            data = np.expand_dims(x_data[:, i], axis=1)  # 独热编码需要升维
 
+            oneHot = self.Model[i].transform(data).toarray().tolist()
 
+            x_new.append(oneHot)#添加到列表中
 
+        x_new = np.array(x_new)#新列表的行数据是原data列数据的独热码(只需要ndim=2，暂时没想到numpy的做法)
 
+        x_Predict = []
 
+        for i in range(x_new.shape[1]):
 
+            x_Predict.append(x_new[:,i])
 
+        x_Predict = np.array(x_Predict)#转换回array
 
+        self.OneHot_Data = x_Predict.copy()  # 保存未降维数据
 
+        if not self.ndim_up:#压缩操作
 
+            new_xPredict = []
 
+            for i in x_Predict:
 
+                new_list = []
 
+                list_ = i.tolist()
 
+                for a in list_:
 
+                    new_list += a
 
+                new = np.array(new_list)
 
+                new_xPredict.append(new)
 
+
 
+            self.y_testData = np.array(new_xPredict)
 
+            return self.y_testData.copy(),'独热编码'
 
+
 
+        self.y_testData = self.OneHot_Data
 
+        self.have_Predict = True
 
+        return x_Predict,'独热编码'
 
+
 
+    def Des(self, Dic, *args, **kwargs):
 
+        tab = Tab()
 
+        y_data = self.y_testData
 
+        x_data = self.x_testData
 
+        oh_data = self.OneHot_Data
 
+        if not self.ndim_up:
 
+            get_y = Discrete_Feature_visualization(y_data, '转换数据')  # 转换
 
+            for i in range(len(get_y)):
 
+                tab.add(get_y[i], f'[{i}]数据x-x离散散点图')
 
+
 
+        heard = [f'特征:{i}' for i in range(len(x_data[0]))]
 
+        tab.add(make_Tab(heard,x_data.tolist()),f'原数据')
 
+        tab.add(make_Tab(heard,oh_data.tolist()), f'编码数据')
 
+        tab.add(make_Tab(heard,np.dstack((oh_data,x_data)).tolist()), f'合成[原数据,编码]数据')
 
+        tab.add(make_Tab([f'编码:{i}' for i in range(len(y_data[0]))], y_data.tolist()), f'数据')
 
+        save = Dic + r'/独热编码.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Missed_Model(Unsupervised):#缺失数据补充
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(Missed_Model, self).__init__(*args, **kwargs)
 
+        self.Model = SimpleImputer(missing_values=args_use['miss_value'], strategy=args_use['fill_method'],
 
+                                   fill_value=args_use['fill_value'])
 
+
 
+        self.k = {}
 
+        self.Model_Name = 'Missed'
 
+
 
+    def Predict(self, x_data, *args, **kwargs):
 
+        self.x_testData = x_data.copy()
 
+        x_Predict = self.Model.transform(x_data)
 
+        self.y_testData = x_Predict.copy()
 
+        self.have_Predict = True
 
+        return x_Predict,'填充缺失'
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        y_data = self.y_testData
 
+        x_data = self.x_testData
 
+        statistics = self.Model.statistics_.tolist()
 
+        Conversion_control(y_data,x_data,tab)
 
+        tab.add(make_Tab([f'特征[{i}]' for i in range(len(statistics))],[statistics]),'填充值')
 
+        save = Dic + r'/缺失数据填充.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class PCA_Model(Unsupervised):
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(PCA_Model, self).__init__(*args, **kwargs)
 
+        self.Model = PCA(n_components=args_use['n_components'],whiten=args_use['white_PCA'])
 
+
 
+        self.whiten=args_use['white_PCA']
 
+        self.n_components = args_use['n_components']
 
+        self.k = {'n_components':args_use['n_components'],'whiten':args_use['white_PCA']}
 
+        self.Model_Name = 'PCA'
 
+
 
+    def Predict(self, x_data, *args, **kwargs):
 
+        self.x_testData = x_data.copy()
 
+        x_Predict = self.Model.transform(x_data)
 
+        self.y_testData = x_Predict.copy()
 
+        self.have_Predict = True
 
+        return x_Predict,'PCA'
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        y_data = self.y_testData
 
+        importance = self.Model.components_.tolist()
 
+        var = self.Model.explained_variance_.tolist()#方量差
 
+        Conversion_Separate_Format(y_data,tab)
 
+
 
+        x_data = [f'第{i+1}主成分' for i in range(len(importance))]#主成分
 
+        y_data = [f'特征[{i}]' for i in range(len(importance[0]))]#主成分
 
+        value = [(f'第{i+1}主成分',f'特征[{j}]',importance[i][j]) for i in range(len(importance)) for j in range(len(importance[i]))]
 
+        c = (HeatMap()
 
+             .add_xaxis(x_data)
 
+             .add_yaxis(f'', y_data, value, **Label_Set)  # value的第一个数值是x
 
+             .set_global_opts(title_opts=opts.TitleOpts(title='预测热力图'), **global_Leg,
 
+                              yaxis_opts=opts.AxisOpts(is_scale=True),  # 'category'
 
+                              xaxis_opts=opts.AxisOpts(is_scale=True),
 
+                              visualmap_opts=opts.VisualMapOpts(is_show=True, max_=int(self.Model.components_.max()) + 1,
 
+                                                                min_=int(self.Model.components_.min()),
 
+                                                                pos_right='3%'))  # 显示
 
+             )
 
+        tab.add(c,'成分热力图')
 
+        c = (
 
+            Bar()
 
+                .add_xaxis([f'第[{i}]主成分' for i in range(len(var))])
 
+                .add_yaxis('方量差', var, **Label_Set)
 
+                .set_global_opts(title_opts=opts.TitleOpts(title='方量差柱状图'), **global_Set)
 
+        )
 
+
 
+        desTo_CSV(Dic, '成分重要性', importance, [x_data],[y_data])
 
+        desTo_CSV(Dic, '方量差', [var], [f'第[{i}]主成分' for i in range(len(var))])
 
+
 
+        tab.add(c, '方量差柱状图')
 
+        save = Dic + r'/主成分分析.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class RPCA_Model(Unsupervised):
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(RPCA_Model, self).__init__(*args, **kwargs)
 
+        self.Model = IncrementalPCA(n_components=args_use['n_components'],whiten=args_use['white_PCA'])
 
+
 
+        self.n_components = args_use['n_components']
 
+        self.whiten=args_use['white_PCA']
 
+        self.k = {'n_components': args_use['n_components'],'whiten':args_use['white_PCA']}
 
+        self.Model_Name = 'RPCA'
 
+
 
+    def Predict(self, x_data, *args, **kwargs):
 
+        self.x_testData = x_data.copy()
 
+        x_Predict = self.Model.transform(x_data)
 
+        self.y_testData = x_Predict.copy()
 
+        self.have_Predict = True
 
+        return x_Predict,'RPCA'
 
+
 
+    def Des(self, Dic, *args, **kwargs):
 
+        tab = Tab()
 
+        y_data = self.y_trainData
 
+        importance = self.Model.components_.tolist()
 
+        var = self.Model.explained_variance_.tolist()  # 方量差
 
+        Conversion_Separate_Format(y_data, tab)
 
+
 
+        x_data = [f'第{i + 1}主成分' for i in range(len(importance))]  # 主成分
 
+        y_data = [f'特征[{i}]' for i in range(len(importance[0]))]  # 主成分
 
+        value = [(f'第{i + 1}主成分', f'特征[{j}]', importance[i][j]) for i in range(len(importance)) for j in
 
+                 range(len(importance[i]))]
 
+        c = (HeatMap()
 
+             .add_xaxis(x_data)
 
+             .add_yaxis(f'', y_data, value, **Label_Set)  # value的第一个数值是x
 
+             .set_global_opts(title_opts=opts.TitleOpts(title='预测热力图'), **global_Leg,
 
+                              yaxis_opts=opts.AxisOpts(is_scale=True),  # 'category'
 
+                              xaxis_opts=opts.AxisOpts(is_scale=True),
 
+                              visualmap_opts=opts.VisualMapOpts(is_show=True,
 
+                                                                max_=int(self.Model.components_.max()) + 1,
 
+                                                                min_=int(self.Model.components_.min()),
 
+                                                                pos_right='3%'))  # 显示
 
+             )
 
+        tab.add(c, '成分热力图')
 
+        c = (
 
+            Bar()
 
+                .add_xaxis([f'第[{i}]主成分' for i in range(len(var))])
 
+                .add_yaxis('放量差', var, **Label_Set)
 
+                .set_global_opts(title_opts=opts.TitleOpts(title='方量差柱状图'), **global_Set)
 
+        )
 
+        tab.add(c, '方量差柱状图')
 
+        desTo_CSV(Dic, '成分重要性', importance, [x_data],[y_data])
 
+        desTo_CSV(Dic, '方量差', [var], [f'第[{i}]主成分' for i in range(len(var))])
 
+        save = Dic + r'/RPCA(主成分分析).HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class KPCA_Model(Unsupervised):
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(KPCA_Model, self).__init__(*args, **kwargs)
 
+        self.Model = KernelPCA(n_components=args_use['n_components'], kernel=args_use['kernel'])
 
+        self.n_components = args_use['n_components']
 
+        self.kernel = args_use['kernel']
 
+        self.k = {'n_components': args_use['n_components'],'kernel':args_use['kernel']}
 
+        self.Model_Name = 'KPCA'
 
+
 
+    def Predict(self, x_data, *args, **kwargs):
 
+        self.x_testData = x_data.copy()
 
+        x_Predict = self.Model.transform(x_data)
 
+        self.y_testData = x_Predict.copy()
 
+        self.have_Predict = True
 
+        return x_Predict,'KPCA'
 
+
 
+    def Des(self, Dic, *args, **kwargs):
 
+        tab = Tab()
 
+        y_data = self.y_testData
 
+        Conversion_Separate_Format(y_data, tab)
 
+
 
+        save = Dic + r'/KPCA(主成分分析).HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class LDA_Model(prep_Base):#有监督学习
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(LDA_Model, self).__init__(*args, **kwargs)
 
+        self.Model = LDA(n_components=args_use['n_components'])
 
+        self.n_components = args_use['n_components']
 
+        self.k = {'n_components': args_use['n_components']}
 
+        self.Model_Name = 'LDA'
 
+
 
+    def Predict(self, x_data, *args, **kwargs):
 
+        self.x_testData = x_data.copy()
 
+        x_Predict = self.Model.transform(x_data)
 
+        self.y_testData = x_Predict.copy()
 
+        self.have_Predict = True
 
+        return x_Predict,'LDA'
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+
 
+        x_data = self.x_testData
 
+        y_data = self.y_testData
 
+        Conversion_Separate_Format(y_data,tab)
 
+
 
+        w_list = self.Model.coef_.tolist()  # 变为表格
 
+        b = self.Model.intercept_
 
+        tab = Tab()
 
+
 
+        x_means = make_Cat(x_data).get()[0]
 
+        get = Regress_W(x_data, None, w_list, b, x_means.copy())#回归的y是历史遗留问题 不用分类回归：因为得不到分类数据（predict结果是降维数据不是预测数据）
 
+        for i in range(len(get)):
 
+            tab.add(get[i].overlap(get[i]), f'类别:{i}LDA映射曲线')
 
+
 
+        save = Dic + r'/render.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class NMF_Model(Unsupervised):
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(NMF_Model, self).__init__(*args, **kwargs)
 
+        self.Model = NMF(n_components=args_use['n_components'])
 
+
 
+        self.n_components = args_use['n_components']
 
+        self.k = {'n_components':args_use['n_components']}
 
+        self.Model_Name = 'NFM'
 
+        self.h_testData = None
 
+        #x_trainData保存的是W，h_trainData和y_trainData是后来数据
 
+
 
+    def Predict(self, x_data,x_name='',Add_Func=None,*args, **kwargs):
 
+        self.x_testData = x_data.copy()
 
+        x_Predict = self.Model.transform(x_data)
 
+        self.y_testData = x_Predict.copy()
 
+        self.h_testData = self.Model.components_
 
+        if Add_Func != None and x_name != '':
 
+            Add_Func(self.h_testData, f'{x_name}:V->NMF[H]')
 
+        self.have_Predict = True
 
+        return x_Predict,'V->NMF[W]'
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        y_data = self.y_testData
 
+        x_data = self.x_testData
 
+        h_data = self.h_testData
 
+        Conversion_SeparateWH(y_data,h_data,tab)
 
+
 
+        wh_data = np.matmul(y_data, h_data)
 
+        difference_data = x_data - wh_data
 
+
 
+        def make_HeatMap(data,name,max_,min_):
 
+            x = [f'数据[{i}]' for i in range(len(data))]  # 主成分
 
+            y = [f'特征[{i}]' for i in range(len(data[0]))]  # 主成分
 
+            value = [(f'数据[{i}]', f'特征[{j}]', float(data[i][j])) for i in range(len(data)) for j in range(len(data[i]))]
 
+
 
+            c = (HeatMap()
 
+                 .add_xaxis(x)
 
+                 .add_yaxis(f'数据', y, value, **Label_Set)  # value的第一个数值是x
 
+                 .set_global_opts(title_opts=opts.TitleOpts(title='原始数据热力图'), **global_Leg,
 
+                                  yaxis_opts=opts.AxisOpts(is_scale=True, type_='category'),  # 'category'
 
+                                  xaxis_opts=opts.AxisOpts(is_scale=True, type_='category'),
 
+                                  visualmap_opts=opts.VisualMapOpts(is_show=True, max_=max_,
 
+                                                                    min_=min_,
 
+                                                                    pos_right='3%'))#显示
 
+                 )
 
+            tab.add(c,name)
 
+
 
+        max_ = max(int(x_data.max()),int(wh_data.max()),int(difference_data.max())) + 1
 
+        min_ = min(int(x_data.min()),int(wh_data.min()),int(difference_data.min()))
 
+
 
+        make_HeatMap(x_data,'原始数据热力图',max_,min_)
 
+        make_HeatMap(wh_data,'W * H数据热力图',max_,min_)
 
+        make_HeatMap(difference_data,'数据差热力图',max_,min_)
 
+
 
+        desTo_CSV(Dic, '权重矩阵', y_data)
 
+        desTo_CSV(Dic, '系数矩阵', h_data)
 
+        desTo_CSV(Dic, '系数*权重矩阵', wh_data)
 
+
 
+        save = Dic + r'/非负矩阵分解.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class TSNE_Model(Unsupervised):
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(TSNE_Model, self).__init__(*args, **kwargs)
 
+        self.Model = TSNE(n_components=args_use['n_components'])
 
+
 
+        self.n_components = args_use['n_components']
 
+        self.k = {'n_components':args_use['n_components']}
 
+        self.Model_Name = 't-SNE'
 
+
 
+    def Fit(self,*args, **kwargs):
 
+        self.have_Fit = True
 
+        return 'None', 'None'
 
+
 
+    def Predict(self, x_data, *args, **kwargs):
 
+        self.x_testData = x_data.copy()
 
+        x_Predict = self.Model.fit_transform(x_data)
 
+        self.y_testData = x_Predict.copy()
 
+        self.have_Predict = True
 
+        return x_Predict,'SNE'
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        y_data = self.y_testData
 
+        Conversion_Separate_Format(y_data,tab)
 
+
 
+        save = Dic + r'/T-SNE.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class MLP_Model(Study_MachineBase):#神经网络(多层感知机)，有监督学习
 
+    def __init__(self,args_use,model,*args,**kwargs):
 
+        super(MLP_Model, self).__init__(*args,**kwargs)
 
+        Model = {'MLP':MLPRegressor,'MLP_class':MLPClassifier}[model]
 
+        self.Model = Model(hidden_layer_sizes=args_use['hidden_size'],activation=args_use['activation'],
 
+                           solver=args_use['solver'],alpha=args_use['alpha'],max_iter=args_use['max_iter'])
 
+        #记录这两个是为了克隆
 
+        self.hidden_layer_sizes = args_use['hidden_size']
 
+        self.activation = args_use['activation']
 
+        self.max_iter = args_use['max_iter']
 
+        self.solver = args_use['solver']
 
+        self.alpha = args_use['alpha']
 
+        self.k = {'hidden_layer_sizes':args_use['hidden_size'],'activation':args_use['activation'],'max_iter':args_use['max_iter'],
 
+                  'solver':args_use['solver'],'alpha':args_use['alpha']}
 
+        self.Model_Name = model
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+
 
+        x_data = self.x_testData
 
+        y_data = self.y_testData
 
+        coefs = self.Model.coefs_
 
+        class_ = self.Model.classes_
 
+        n_layers_ = self.Model.n_layers_
 
+        def make_HeatMap(data,name):
 
+            x = [f'特征(节点)[{i}]' for i in range(len(data))]
 
+            y = [f'节点[{i}]' for i in range(len(data[0]))]
 
+            value = [(f'特征(节点)[{i}]', f'节点[{j}]', float(data[i][j])) for i in range(len(data)) for j in range(len(data[i]))]
 
+
 
+            c = (HeatMap()
 
+                 .add_xaxis(x)
 
+                 .add_yaxis(f'数据', y, value, **Label_Set)  # value的第一个数值是x
 
+                 .set_global_opts(title_opts=opts.TitleOpts(title=name), **global_Leg,
 
+                                  yaxis_opts=opts.AxisOpts(is_scale=True, type_='category'),  # 'category'
 
+                                  xaxis_opts=opts.AxisOpts(is_scale=True, type_='category'),
 
+                                  visualmap_opts=opts.VisualMapOpts(is_show=True, max_=float(data.max()),
 
+                                                                    min_=float(data.min()),
 
+                                                                    pos_right='3%'))#显示
 
+                 )
 
+            tab.add(c,name)
 
+            tab.add(make_Tab(x,data.T.tolist()),f'{name}:表格')
 
+            desTo_CSV(Dic,f'{name}:表格',data.T.tolist(),x,y)
 
+
 
+        get, x_means, x_range, Type = regress_visualization(x_data, y_data)
 
+        for i in range(len(get)):
 
+            tab.add(get[i], f'{i}训练数据散点图')
 
+
 
+        get = Prediction_boundary(x_range, x_means, self.Predict, Type)
 
+        for i in range(len(get)):
 
+            tab.add(get[i], f'{i}预测热力图')
 
+
 
+        heard = ['神经网络层数']
 
+        data = [n_layers_]
 
+        for i in range(len(coefs)):
 
+            make_HeatMap(coefs[i],f'{i}层权重矩阵')
 
+            heard.append(f'第{i}层节点数')
 
+            data.append(len(coefs[i][0]))
 
+
 
+        if self.Model_Name == 'MLP_class':
 
+            heard += [f'[{i}]类型' for i in range(len(class_))]
 
+            data += class_.tolist()
 
+
 
+        tab.add(make_Tab(heard,[data]),'数据表')
 
+
 
+        save = Dic + r'/多层感知机.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class kmeans_Model(UnsupervisedModel):
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(kmeans_Model, self).__init__(*args, **kwargs)
 
+        self.Model = KMeans(n_clusters=args_use['n_clusters'])
 
+
 
+        self.class_ = []
 
+        self.n_clusters = args_use['n_clusters']
 
+        self.k = {'n_clusters':args_use['n_clusters']}
 
+        self.Model_Name = 'k-means'
 
+
 
+    def Fit(self, x_data, *args, **kwargs):
 
+        re = super().Fit(x_data,*args,**kwargs)
 
+        self.class_ = list(set(self.Model.labels_.tolist()))
 
+        self.have_Fit = True
 
+        return re
 
+
 
+    def Predict(self, x_data, *args, **kwargs):
 
+        self.x_testData = x_data.copy()
 
+        y_Predict = self.Model.predict(x_data)
 
+        self.y_testData = y_Predict.copy()
 
+        self.have_Predict = True
 
+        return y_Predict,'k-means'
 
+
 
+    def Des(self,Dic,*args,**kwargs):
 
+        tab = Tab()
 
+        y = self.y_testData
 
+        x_data = self.x_testData
 
+        class_ = self.class_
 
+        center = self.Model.cluster_centers_
 
+        class_heard = [f'簇[{i}]' for i in range(len(class_))]
 
+
 
+        Func = Training_visualization_More if More_Global else Training_visualization_Center
 
+        get,x_means,x_range,Type = Func(x_data,class_,y,center)
 
+        for i in range(len(get)):
 
+            tab.add(get[i],f'{i}数据散点图')
 
+
 
+        get = Decision_boundary(x_range, x_means, self.Predict, class_, Type)
 
+        for i in range(len(get)):
 
+            tab.add(get[i], f'{i}预测热力图')
 
+
 
+        heard = class_heard + [f'普适预测第{i}特征' for i in range(len(x_means))]
 
+        data = class_ + [f'{i}' for i in x_means]
 
+        c = Table().add(headers=heard, rows=[data])
 
+        tab.add(c, '数据表')
 
+        desTo_CSV(Dic, '预测表', [[f'{i}' for i in x_means]], [f'普适预测第{i}特征' for i in range(len(x_means))])
 
+        save = Dic + r'/k-means聚类.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Agglomerative_Model(UnsupervisedModel):
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(Agglomerative_Model, self).__init__(*args, **kwargs)
 
+        self.Model = AgglomerativeClustering(n_clusters=args_use['n_clusters'])#默认为2，不同于k-means
 
+
 
+        self.class_ = []
 
+        self.n_clusters = args_use['n_clusters']
 
+        self.k = {'n_clusters':args_use['n_clusters']}
 
+        self.Model_Name = 'Agglomerative'
 
+
 
+    def Fit(self, x_data, *args, **kwargs):
 
+        re = super().Fit(x_data,*args,**kwargs)
 
+        self.class_ = list(set(self.Model.labels_.tolist()))
 
+        self.have_Fit = True
 
+        return re
 
+
 
+    def Predict(self, x_data, *args, **kwargs):
 
+        self.x_testData = x_data.copy()
 
+        y_Predict = self.Model.fit_predict(x_data)
 
+        self.y_trainData = y_Predict.copy()
 
+        self.have_Predict = True
 
+        return y_Predict,'Agglomerative'
 
+
 
+    def Des(self, Dic, *args, **kwargs):
 
+        tab = Tab()
 
+        y = self.y_testData
 
+        x_data = self.x_testData
 
+        class_ = self.class_
 
+        class_heard = [f'簇[{i}]' for i in range(len(class_))]
 
+
 
+        Func = Training_visualization_More_NoCenter if More_Global else Training_visualization
 
+        get, x_means, x_range, Type = Func(x_data, class_, y)
 
+        for i in range(len(get)):
 
+            tab.add(get[i], f'{i}训练数据散点图')
 
+
 
+        get = Decision_boundary(x_range, x_means, self.Predict, class_, Type)
 
+        for i in range(len(get)):
 
+            tab.add(get[i], f'{i}预测热力图')
 
+
 
+        linkage_array = ward(self.x_trainData)#self.y_trainData是结果
 
+        dendrogram(linkage_array)
 
+        plt.savefig(Dic + r'/Cluster_graph.png')
 
+
 
+        image = Image()
 
+        image.add(
 
+            src=Dic + r'/Cluster_graph.png',
 
+        ).set_global_opts(
 
+            title_opts=opts.ComponentTitleOpts(title="聚类树状图")
 
+        )
 
+
 
+        tab.add(image,'聚类树状图')
 
+
 
+        heard = class_heard + [f'普适预测第{i}特征' for i in range(len(x_means))]
 
+        data = class_ + [f'{i}' for i in x_means]
 
+        c = Table().add(headers=heard, rows=[data])
 
+        tab.add(c, '数据表')
 
+
 
+        desTo_CSV(Dic, '预测表', [[f'{i}' for i in x_means]], [f'普适预测第{i}特征' for i in range(len(x_means))])
 
+        save = Dic + r'/层次聚类.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class DBSCAN_Model(UnsupervisedModel):
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(DBSCAN_Model, self).__init__(*args, **kwargs)
 
+        self.Model = DBSCAN(eps = args_use['eps'], min_samples = args_use['min_samples'])
 
+        #eps是距离(0.5)，min_samples(5)是簇与噪音分界线(每个簇最小元素数)
 
+        # min_samples
 
+        self.eps = args_use['eps']
 
+        self.min_samples = args_use['min_samples']
 
+        self.k = {'min_samples':args_use['min_samples'],'eps':args_use['eps']}
 
+        self.class_ = []
 
+        self.Model_Name = 'DBSCAN'
 
+
 
+    def Fit(self, x_data, *args, **kwargs):
 
+        re = super().Fit(x_data,*args,**kwargs)
 
+        self.class_ = list(set(self.Model.labels_.tolist()))
 
+        self.have_Fit = True
 
+        return re
 
+
 
+    def Predict(self, x_data, *args, **kwargs):
 
+        self.x_testData = x_data.copy()
 
+        y_Predict = self.Model.fit_predict(x_data)
 
+        self.y_testData = y_Predict.copy()
 
+        self.have_Predict = True
 
+        return y_Predict,'DBSCAN'
 
+
 
+    def Des(self, Dic, *args, **kwargs):
 
+        #DBSCAN没有预测的必要
 
+        tab = Tab()
 
+        y = self.y_testData.copy()
 
+        x_data = self.x_testData.copy()
 
+        class_ = self.class_
 
+        class_heard = [f'簇[{i}]' for i in range(len(class_))]
 
+
 
+        Func = Training_visualization_More_NoCenter if More_Global else Training_visualization
 
+        get, x_means, x_range, Type = Func(x_data, class_, y)
 
+        for i in range(len(get)):
 
+            tab.add(get[i], f'{i}训练数据散点图')
 
+
 
+        heard = class_heard + [f'普适预测第{i}特征' for i in range(len(x_means))]
 
+        data = class_ + [f'{i}' for i in x_means]
 
+        c = Table().add(headers=heard, rows=[data])
 
+        tab.add(c, '数据表')
 
+
 
+        desTo_CSV(Dic, '预测表', [[f'{i}' for i in x_means]], [f'普适预测第{i}特征' for i in range(len(x_means))])
 
+        save = Dic + r'/密度聚类.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Fast_Fourier(Study_MachineBase):#快速傅里叶变换
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(Fast_Fourier, self).__init__(*args, **kwargs)
 
+        self.Model = None
 
+        self.Fourier = None#fft复数
 
+        self.Frequency = None#频率range
 
+        self.angular_Frequency = None#角频率range
 
+        self.Phase = None#相位range
 
+        self.Breadth = None#震幅range
 
+        self.N = None#样本数
 
+
 
+    def Fit(self, y_data, *args, **kwargs):
 
+        y_data = y_data.ravel()  # 扯平为一维数组
 
+        try:
 
+            if self.y_trainData is None:raise Exception
 
+            self.y_trainData = np.hstack(y_data,self.x_trainData)
 
+        except:
 
+            self.y_trainData = y_data.copy()
 
+        Fourier = fft(y_data)
 
+        self.N = len(y_data)
 
+        self.Frequency = np.linspace(0,1,self.N)#频率N_range
 
+        self.angular_Frequency = self.Frequency / ( np.pi * 2 )#角频率w
 
+        self.Phase = np.angle(Fourier)
 
+        self.Breadth = np.abs(Fourier)
 
+        self.Fourier = Fourier
 
+        self.have_Fit = True
 
+        return 'None','None'
 
+
 
+    def Predict(self, x_data, *args, **kwargs):
 
+        return np.array([]),''
 
+
 
+    def Des(self, Dic, *args, **kwargs):
 
+        #DBSCAN没有预测的必要
 
+        tab = Tab()
 
+        y = self.y_trainData.copy()
 
+        N = self.N
 
+        Phase = self.Phase#相位range
 
+        Breadth = self.Breadth#震幅range
 
+        normalization_Breadth = Breadth/N
 
+        def line(name,value,s=slice(0,None)) -> Line:
 
+            c = (
 
+                Line()
 
+                    .add_xaxis(self.Frequency[s].tolist())
 
+                    .add_yaxis('', value,**Label_Set,symbol='none' if self.N >= 500 else None)
 
+                    .set_global_opts(title_opts=opts.TitleOpts(title=name),**global_Leg,
 
+                                     xaxis_opts=opts.AxisOpts(type_='value'),
 
+                                     yaxis_opts=opts.AxisOpts(type_='value'))
 
+            )
 
+            return c
 
+
 
+        tab.add(line('原始数据',y.tolist()),'原始数据')
 
+        tab.add(line('双边振幅谱',Breadth.tolist()),'双边振幅谱')
 
+        tab.add(line('双边振幅谱(归一化)',normalization_Breadth.tolist()),'双边振幅谱(归一化)')
 
+        tab.add(line('单边相位谱',Breadth[:int(N/2)].tolist(),slice(0,int(N/2))),'单边相位谱')
 
+        tab.add(line('单边相位谱(归一化)',normalization_Breadth[:int(N/2)].tolist(),slice(0,int(N/2))),'单边相位谱(归一化)')
 
+        tab.add(line('双边相位谱', Phase.tolist()), '双边相位谱')
 
+        tab.add(line('单边相位谱', Phase[:int(N/2)].tolist(),slice(0,int(N/2))), '单边相位谱')
 
+
 
+        tab.add(make_Tab(self.Frequency.tolist(),[Breadth.tolist()]),'双边振幅谱')
 
+        tab.add(make_Tab(self.Frequency.tolist(),[Phase.tolist()]),'双边相位谱')
 
+        tab.add(make_Tab(self.Frequency.tolist(),[self.Fourier.tolist()]),'快速傅里叶变换')
 
+
 
+        save = Dic + r'/快速傅里叶.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Reverse_Fast_Fourier(Study_MachineBase):#快速傅里叶变换
 
+    def __init__(self, args_use, model, *args, **kwargs):
 
+        super(Reverse_Fast_Fourier, self).__init__(*args, **kwargs)
 
+        self.Model = None
 
+        self.N = None
 
+        self.y_testData_real = None
 
+        self.Phase = None
 
+        self.Breadth = None
 
+
 
+    def Fit(self, y_data, *args, **kwargs):
 
+        return 'None','None'
 
+
 
+    def Predict(self, x_data,x_name='', Add_Func=None, *args, **kwargs):
 
+        self.x_testData = x_data.ravel().astype(np.complex_)
 
+        Fourier = ifft(self.x_testData)
 
+        self.y_testData = Fourier.copy()
 
+        self.y_testData_real = np.real(Fourier)
 
+        self.N = len(self.y_testData_real)
 
+        self.Phase = np.angle(self.x_testData)
 
+        self.Breadth = np.abs(self.x_testData)
 
+        Add_Func(self.y_testData_real.copy(), f'{x_name}:逆向快速傅里叶变换[实数]')
 
+        return Fourier,'逆向快速傅里叶变换'
 
+
 
+    def Des(self, Dic, *args, **kwargs):
 
+        #DBSCAN没有预测的必要
 
+        tab = Tab()
 
+        y = self.y_testData_real.copy()
 
+        y_data = self.y_testData.copy()
 
+        N = self.N
 
+        range_N = np.linspace(0,1,N).tolist()
 
+        Phase = self.Phase#相位range
 
+        Breadth = self.Breadth#震幅range
 
+
 
+        def line(name,value,s=slice(0,None)) -> Line:
 
+            c = (
 
+                Line()
 
+                    .add_xaxis(range_N[s])
 
+                    .add_yaxis('', value,**Label_Set,symbol='none' if N >= 500 else None)
 
+                    .set_global_opts(title_opts=opts.TitleOpts(title=name),**global_Leg,
 
+                                     xaxis_opts=opts.AxisOpts(type_='value'),
 
+                                     yaxis_opts=opts.AxisOpts(type_='value'))
 
+            )
 
+            return c
 
+
 
+        tab.add(line('逆向傅里叶变换', y.tolist()), '逆向傅里叶变换[实数]')
 
+        tab.add(make_Tab(range_N,[y_data.tolist()]),'逆向傅里叶变换数据')
 
+        tab.add(make_Tab(range_N,[y.tolist()]),'逆向傅里叶变换数据[实数]')
 
+        tab.add(line('双边振幅谱',Breadth.tolist()),'双边振幅谱')
 
+        tab.add(line('单边相位谱',Breadth[:int(N/2)].tolist(),slice(0,int(N/2))),'单边相位谱')
 
+        tab.add(line('双边相位谱', Phase.tolist()), '双边相位谱')
 
+        tab.add(line('单边相位谱', Phase[:int(N/2)].tolist(),slice(0,int(N/2))), '单边相位谱')
 
+
 
+        save = Dic + r'/快速傅里叶.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Reverse_Fast_Fourier_TwoNumpy(Reverse_Fast_Fourier):#2快速傅里叶变换
 
+    def Fit(self, x_data,y_data=None,x_name='', Add_Func=None, *args, **kwargs):
 
+        r = np.multiply(np.cos(x_data),y_data)
 
+        j = np.multiply(np.sin(x_data),y_data) * 1j
 
+        super(Reverse_Fast_Fourier_TwoNumpy, self).Predict(r + j,x_name=x_name, Add_Func=Add_Func, *args, **kwargs)
 
+        return 'None','None'
 
+
 
+class Curve_fitting(Study_MachineBase):#曲线拟合
 
+    def __init__(self,Name, str_, model, *args, **kwargs):
 
+        super(Curve_fitting, self).__init__(*args, **kwargs)
 
+        def ndimDown(data:np.ndarray):
 
+            if data.ndim == 1:return data
 
+            new_data = []
 
+            for i in data:
 
+                new_data.append(np.sum(i))
 
+            return np.array(new_data)
 
+        NAME = {'np':np,'Func':model,'ndimDown':ndimDown}
 
+        DEF = f'''
 
+def FUNC({",".join(model.__code__.co_varnames)}):
 
+    answer = Func({",".join(model.__code__.co_varnames)})
 
+    return ndimDown(answer)
 
+'''
 
+        exec(DEF,NAME)
 
+        self.Func = NAME['FUNC']
 
+        self.Fit_data = None
 
+        self.Name = Name
 
+        self.Func_Str = str_
 
+
 
+    def Fit(self, x_data:np.ndarray,y_data:np.ndarray, *args, **kwargs):
 
+        y_data = y_data.ravel()
 
+        x_data = x_data.astype(np.float64)
 
+        try:
 
+            if self.x_trainData is None:raise Exception
 
+            self.x_trainData = np.vstack(x_data,self.x_trainData)
 
+            self.y_trainData = np.vstack(y_data,self.y_trainData)
 
+        except:
 
+            self.x_trainData = x_data.copy()
 
+            self.y_trainData = y_data.copy()
 
+        self.Fit_data = optimize.curve_fit(self.Func,self.x_trainData,self.y_trainData)
 
+        self.Model = self.Fit_data[0].copy()
 
+        return 'None','None'
 
+
 
+    def Predict(self, x_data, *args, **kwargs):
 
+        self.x_testData = x_data.copy()
 
+        Predict = self.Func(x_data,*self.Model)
 
+        y_Predict = []
 
+        for i in Predict:
 
+            y_Predict.append(np.sum(i))
 
+        y_Predict = np.array(y_Predict)
 
+        self.y_testData = y_Predict.copy()
 
+        self.have_Predict = True
 
+        return y_Predict,self.Name
 
+
 
+    def Des(self, Dic, *args, **kwargs):
 
+        #DBSCAN没有预测的必要
 
+        tab = Tab()
 
+        y = self.y_testData.copy()
 
+        x_data = self.x_testData.copy()
 
+
 
+        get, x_means, x_range,Type = regress_visualization(x_data, y)
 
+        for i in range(len(get)):
 
+            tab.add(get[i], f'{i}预测类型图')
 
+
 
+        get = Prediction_boundary(x_range, x_means, self.Predict, Type)
 
+        for i in range(len(get)):
 
+            tab.add(get[i], f'{i}预测热力图')
 
+
 
+        tab.add(make_Tab([f'普适预测第{i}特征' for i in range(len(x_means))], [[f'{i}' for i in x_means]]),'普适预测特征数据')
 
+        tab.add(make_Tab([f'参数[{i}]' for i in range(len(self.Model))], [[f'{i}' for i in self.Model]]), '拟合参数')
 
+
 
+        save = Dic + r'/曲线拟合.HTML'
 
+        tab.render(save)  # 生成HTML
 
+        return save,
 
+
 
+class Machine_Learner(Learner):#数据处理者
 
+    def __init__(self,*args, **kwargs):
 
+        super().__init__(*args, **kwargs)
 
+        self.Learner = {}#记录机器
 
+        self.Learn_Dic = {'Line':Line_Model,
 
+                          'Ridge':Line_Model,
 
+                          'Lasso':Line_Model,
 
+                          'LogisticRegression':LogisticRegression_Model,
 
+                          'Knn_class':Knn_Model,
 
+                          'Knn': Knn_Model,
 
+                          'Tree_class': Tree_Model,
 
+                          'Tree': Tree_Model,
 
+                          'Forest':Forest_Model,
 
+                          'Forest_class': Forest_Model,
 
+                          'GradientTree_class':GradientTree_Model,
 
+                          'GradientTree': GradientTree_Model,
 
+                          'Variance':Variance_Model,
 
+                          'SelectKBest':SelectKBest_Model,
 
+                          'Z-Score':Standardization_Model,
 
+                          'MinMaxScaler':MinMaxScaler_Model,
 
+                          'LogScaler':LogScaler_Model,
 
+                          'atanScaler':atanScaler_Model,
 
+                          'decimalScaler':decimalScaler_Model,
 
+                          'sigmodScaler':sigmodScaler_Model,
 
+                          'Mapzoom':Mapzoom_Model,
 
+                          'Fuzzy_quantization':Fuzzy_quantization_Model,
 
+                          'Regularization':Regularization_Model,
 
+                          'Binarizer':Binarizer_Model,
 
+                          'Discretization':Discretization_Model,
 
+                          'Label':Label_Model,
 
+                          'OneHotEncoder':OneHotEncoder_Model,
 
+                          'Missed':Missed_Model,
 
+                          'PCA':PCA_Model,
 
+                          'RPCA':RPCA_Model,
 
+                          'KPCA':KPCA_Model,
 
+                          'LDA':LDA_Model,
 
+                          'SVC':SVC_Model,
 
+                          'SVR':SVR_Model,
 
+                          'MLP':MLP_Model,
 
+                          'MLP_class': MLP_Model,
 
+                          'NMF':NMF_Model,
 
+                          't-SNE':TSNE_Model,
 
+                          'k-means':kmeans_Model,
 
+                          'Agglomerative':Agglomerative_Model,
 
+                          'DBSCAN':DBSCAN_Model,
 
+                          'ClassBar':Class_To_Bar,
 
+                          'FeatureScatter':Near_feature_scatter,
 
+                          'FeatureScatterClass': Near_feature_scatter_class,
 
+                          'FeatureScatter_all':Near_feature_scatter_More,
 
+                          'FeatureScatterClass_all':Near_feature_scatter_class_More,
 
+                          'HeatMap':Numpy_To_HeatMap,
 
+                          'FeatureY-X':Feature_scatter_YX,
 
+                          'ClusterTree':Cluster_Tree,
 
+                          'MatrixScatter':MatrixScatter,
 
+                          'Correlation':CORR,
 
+                          'Statistics':Des,
 
+                          'Fast_Fourier':Fast_Fourier,
 
+                          'Reverse_Fast_Fourier':Reverse_Fast_Fourier,
 
+                          '[2]Reverse_Fast_Fourier':Reverse_Fast_Fourier_TwoNumpy,
 
+                          }
 
+        self.Learner_Type = {}#记录机器的类型
 
+
 
+    def p_Args(self,Text,Type):#解析参数
 
+        args = {}
 
+        args_use = {}
 
+        #输入数据
 
+        exec(Text,args)
 
+        #处理数据
 
+        if Type in ('MLP','MLP_class'):
 
+            args_use['alpha'] = float(args.get('alpha', 0.0001))  # MLP正则化用
 
+        else:
 
+            args_use['alpha'] = float(args.get('alpha',1.0))#L1和L2正则化用
 
+        args_use['C'] = float(args.get('C', 1.0))  # L1和L2正则化用
 
+        if Type in ('MLP','MLP_class'):
 
+            args_use['max_iter'] = int(args.get('max_iter', 200))  # L1和L2正则化用
 
+        else:
 
+            args_use['max_iter'] = int(args.get('max_iter', 1000))  # L1和L2正则化用
 
+        args_use['n_neighbors'] = int(args.get('K_knn', 5))#knn邻居数 (命名不同)
 
+        args_use['p'] = int(args.get('p', 2))  # 距离计算方式
 
+        args_use['nDim_2'] = bool(args.get('nDim_2', True))  # 数据是否降维
 
+
 
+        if Type in ('Tree','Forest','GradientTree'):
 
+            args_use['criterion'] = 'mse' if bool(args.get('is_MSE', True)) else 'mae'  # 是否使用基尼不纯度
 
+        else:
 
+            args_use['criterion'] = 'gini' if bool(args.get('is_Gini', True)) else 'entropy'  # 是否使用基尼不纯度
 
+        args_use['splitter'] = 'random' if bool(args.get('is_random', False)) else 'best' # 决策树节点是否随机选用最优
 
+        args_use['max_features'] = args.get('max_features', None) # 选用最多特征数
 
+        args_use['max_depth'] = args.get('max_depth', None)  # 最大深度
 
+        args_use['min_samples_split'] = int(args.get('min_samples_split', 2))  # 是否继续划分（容易造成过拟合）
 
+
 
+        args_use['P'] = float(args.get('min_samples_split', 0.8))
 
+        args_use['k'] = args.get('k',1)
 
+        args_use['score_func'] = ({'chi2':chi2,'f_classif':f_classif,'mutual_info_classif':mutual_info_classif,
 
+                                   'f_regression':f_regression,'mutual_info_regression':mutual_info_regression}.
 
+                                  get(args.get('score_func','f_classif'),f_classif))
 
+
 
+        args_use['feature_range'] = tuple(args.get('feature_range',(0,1)))
 
+        args_use['norm'] = args.get('norm','l2')#正则化的方式L1或者L2
 
+
 
+        args_use['threshold'] = float(args.get('threshold', 0.0))  # 二值化特征
 
+
 
+        args_use['split_range'] = list(args.get('split_range', [0]))  # 二值化特征
 
+
 
+        args_use['ndim_up'] = bool(args.get('ndim_up', False))
 
+        args_use['miss_value'] = args.get('miss_value',np.nan)
 
+        args_use['fill_method'] = args.get('fill_method','mean')
 
+        args_use['fill_value'] = args.get('fill_value',None)
 
+
 
+        args_use['n_components'] = args.get('n_components',1)
 
+        args_use['kernel'] = args.get('kernel','rbf' if Type in ('SVR','SVC') else 'linear')
 
+
 
+        args_use['n_Tree'] = args.get('n_Tree',100)
 
+        args_use['gamma'] = args.get('gamma',1)
 
+        args_use['hidden_size'] = tuple(args.get('hidden_size',(100,)))
 
+        args_use['activation'] = str(args.get('activation','relu'))
 
+        args_use['solver'] = str(args.get('solver','adam'))
 
+        if Type in ('k-means',):
 
+            args_use['n_clusters'] = int(args.get('n_clusters',8))
 
+        else:
 
+            args_use['n_clusters'] = int(args.get('n_clusters', 2))
 
+        args_use['eps'] = float(args.get('n_clusters', 0.5))
 
+        args_use['min_samples'] = int(args.get('n_clusters', 5))
 
+        args_use['white_PCA'] = bool(args.get('white_PCA', False))
 
+        return args_use
 
+
 
+    def Add_Learner(self,Learner,Text=''):
 
+        get = self.Learn_Dic[Learner]
 
+        name = f'Le[{len(self.Learner)}]{Learner}'
 
+        #参数调节
 
+        args_use = self.p_Args(Text,Learner)
 
+        #生成学习器
 
+        self.Learner[name] = get(model=Learner,args_use=args_use)
 
+        self.Learner_Type[name] = Learner
 
+
 
+    def Add_Curve_Fitting(self,Learner_text,Text=''):
 
+        NAME = {}
 
+        exec(Learner_text,NAME)
 
+        name = f'Le[{len(self.Learner)}]{NAME.get("name","SELF")}'
 
+        func = NAME.get('f',lambda x,k,b:k * x + b)
 
+        self.Learner[name] = Curve_fitting(name,Learner_text,func)
 
+        self.Learner_Type[name] = 'Curve_fitting'
 
+
 
+    def Add_SelectFrom_Model(self,Learner,Text=''):#Learner代表选中的学习器
 
+        model = self.get_Learner(Learner)
 
+        name = f'Le[{len(self.Learner)}]SelectFrom_Model:{Learner}'
 
+        #参数调节
 
+        args_use = self.p_Args(Text,'SelectFrom_Model')
 
+        #生成学习器
 
+        self.Learner[name] = SelectFrom_Model(Learner=model,args_use=args_use,Dic=self.Learn_Dic)
 
+        self.Learner_Type[name] = 'SelectFrom_Model'
 
+
 
+    def Add_Predictive_HeatMap(self,Learner,Text=''):#Learner代表选中的学习器
 
+        model = self.get_Learner(Learner)
 
+        name = f'Le[{len(self.Learner)}]Predictive_HeatMap:{Learner}'
 
+        #生成学习器
 
+        args_use = self.p_Args(Text, 'Predictive_HeatMap')
 
+        self.Learner[name] = Predictive_HeatMap(Learner=model,args_use=args_use)
 
+        self.Learner_Type[name] = 'Predictive_HeatMap'
 
+
 
+    def Add_Predictive_HeatMap_More(self,Learner,Text=''):#Learner代表选中的学习器
 
+        model = self.get_Learner(Learner)
 
+        name = f'Le[{len(self.Learner)}]Predictive_HeatMap_More:{Learner}'
 
+        #生成学习器
 
+        args_use = self.p_Args(Text, 'Predictive_HeatMap_More')
 
+        self.Learner[name] = Predictive_HeatMap_More(Learner=model,args_use=args_use)
 
+        self.Learner_Type[name] = 'Predictive_HeatMap_More'
 
+
 
+    def Add_View_data(self,Learner,Text=''):#Learner代表选中的学习器
 
+        model = self.get_Learner(Learner)
 
+        name = f'Le[{len(self.Learner)}]View_data:{Learner}'
 
+        #生成学习器
 
+        args_use = self.p_Args(Text, 'View_data')
 
+        self.Learner[name] = View_data(Learner=model,args_use=args_use)
 
+        self.Learner_Type[name] = 'View_data'
 
+
 
+    def Return_Learner(self):
 
+        return self.Learner.copy()
 
+
 
+    def get_Learner(self,name):
 
+        return self.Learner[name]
 
+
 
+    def get_Learner_Type(self,name):
 
+        return self.Learner_Type[name]
 
+
 
+    def Fit(self,x_name,y_name,Learner,split=0.3,*args,**kwargs):
 
+        x_data = self.get_Sheet(x_name)
 
+        y_data = self.get_Sheet(y_name)
 
+        model = self.get_Learner(Learner)
 
+        return model.Fit(x_data,y_data,split = split, x_name=x_name, Add_Func=self.Add_Form)
 
+
 
+    def Predict(self,x_name,Learner,Text='',**kwargs):
 
+        x_data = self.get_Sheet(x_name)
 
+        model = self.get_Learner(Learner)
 
+        y_data,name = model.Predict(x_data, x_name=x_name, Add_Func=self.Add_Form)
 
+        self.Add_Form(y_data,f'{x_name}:{name}')
 
+        return y_data
 
+
 
+    def Score(self,name_x,name_y,Learner):#Score_Only表示仅评分 Fit_Simp 是普遍类操作
 
+        model = self.get_Learner(Learner)
 
+        x = self.get_Sheet(name_x)
 
+        y = self.get_Sheet(name_y)
 
+        return model.Score(x,y)
 
+
 
+    def Show_Score(self,Learner,Dic,name_x,name_y,Func=0):#显示参数
 
+        x = self.get_Sheet(name_x)
 
+        y = self.get_Sheet(name_y)
 
+        if NEW_Global:
 
+            dic = Dic + f'/{Learner}分类评分[CoTan]'
 
+            new_dic = dic
 
+            a = 0
 
+            while exists(new_dic):#直到他不存在 —— False
 
+                new_dic = dic + f'[{a}]'
 
+                a += 1
 
+            mkdir(new_dic)
 
+        else:
 
+            new_dic = Dic
 
+        model = self.get_Learner(Learner)
 
+        #打包
 
+        func = [model.Class_Score, model.Regression_Score, model.Clusters_Score][Func]
 
+        save = func(new_dic,x,y)[0]
 
+        if TAR_Global:make_targz(f'{new_dic}.tar.gz',new_dic)
 
+        return save,new_dic
 
+
 
+    def Show_Args(self,Learner,Dic):#显示参数
 
+        if NEW_Global:
 
+            dic = Dic + f'/{Learner}数据[CoTan]'
 
+            new_dic = dic
 
+            a = 0
 
+            while exists(new_dic):#直到他不存在 —— False
 
+                new_dic = dic + f'[{a}]'
 
+                a += 1
 
+            mkdir(new_dic)
 
+        else:
 
+            new_dic = Dic
 
+        model = self.get_Learner(Learner)
 
+        if (not(model.Model is None) or not(model.Model is list)) and CLF_Global:
 
+            joblib.dump(model.Model,new_dic + '/MODEL.model')#保存模型
 
+        # pickle.dump(model,new_dic + f'/{Learner}.pkl')#保存学习器
 
+        #打包
 
+        save = model.Des(new_dic)[0]
 
+        if TAR_Global:make_targz(f'{new_dic}.tar.gz',new_dic)
 
+        return save,new_dic
 
+
 
+    def Del_Leaner(self,Leaner):
 
+        del self.Learner[Leaner]
 
+        del self.Learner_Type[Leaner]
 
+
 
+def make_targz(output_filename, source_dir):
 
+    with tarfile.open(output_filename, "w:gz") as tar:
 
+        tar.add(source_dir, arcname=basename(source_dir))
 
+    return output_filename
 
+
 
+def set_Global(More=More_Global,All=All_Global,CSV=CSV_Global,CLF=CLF_Global,TAR=TAR_Global,NEW=NEW_Global):
 
+    global More_Global,All_Global,CSV_Global,CLF_Global,TAR_Global,NEW_Global
 
+    More_Global = More  # 是否使用全部特征绘图
 
+    All_Global = All  # 是否导出charts
 
+    CSV_Global = CSV  # 是否导出CSV
 
+    CLF_Global = CLF  # 是否导出模型
 
+    TAR_Global = TAR  # 是否打包tar
 
+    NEW_Global = NEW  # 是否新建目录

+ 1001 - 0
Machine_learning_analysis/Machine_learning.py
مشاهده فایل 

@@ -0,0 +1,1001 @@
 
+import tkinter
 
+import webbrowser
 
+from tkinter.filedialog import askopenfilename, asksaveasfilename,askdirectory
 
+import tkinter.messagebox
 
+import os
 
+import chardet
 
+from tkinter.scrolledtext import ScrolledText
 
+from Machine_learning_analysis import Learn_Numpy
 
+print('Start')
 
+
 
+def Main():
 
+    global top,ML,Form_List,PATH,bg,bbg,fg,Merge_list,Cul_list,Cul_Type
 
+    Cul_list = []
 
+    Cul_Type = []
 
+    PATH = os.getcwd()
 
+    Form_List = []
 
+    Merge_list = []
 
+    ML = Learn_Numpy.Machine_Learner()
 
+
 
+    top = tkinter.Tk()
 
+    bg = '#FFFAFA'  # 主颜色
 
+    bbg = '#FFFAFA'  # 按钮颜色
 
+    fg = '#000000'  # 文字颜色
 
+    top["bg"] = bg
 
+    FONT = ('黑体', 11)  # 设置字体
 
+    top.title('CoTan机器学习')
 
+    top.resizable(width=False, height=False)
 
+    top.geometry('+10+10')  # 设置所在位置
 
+
 
+    width_B = 13  # 标准宽度
 
+    height_B = 2
 
+    a_y = 0
 
+    a_x = 0
 
+
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='导入CSV', command=Add_CSV, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='导入Py', command=Add_Python, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 1, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='导出CSV', command=to_CSV, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 2, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    global name_Input
 
+    a_y += 1
 
+    tkinter.Label(top, text='表格名称:', bg=bg, fg=fg, font=FONT, width=width_B, height=height_B).grid(column=a_x,
 
+                                                                                                   row=a_y)  # 设置说明
 
+    name_Input = tkinter.Entry(top, width=width_B)
 
+    name_Input.grid(column=a_x + 1, row=a_y, columnspan=2, sticky=tkinter.E + tkinter.W)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='删除表格', font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='查看表格', command=Show, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 1, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='查看单一表格', command=Show_One, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 2, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    global Form_BOX, Index_BOX, Column_BOX, to_HTML_Type, Seq_Input, Code_Input,str_must
 
+    a_y += 1
 
+    to_HTML_Type = tkinter.IntVar()  # 正，负，0
 
+    lable = ['选项卡型', '可移动型', '自适应型']  # 复选框
 
+    for i in range(3):
 
+        tkinter.Radiobutton(top, bg=bg, fg=fg, activebackground=bg, activeforeground=fg, selectcolor=bg, text=lable[i],
 
+                            variable=to_HTML_Type,
 
+                            value=i).grid(column=a_x + i, row=a_y, sticky=tkinter.W)
 
+
 
+    str_must = tkinter.IntVar()
 
+    a_y += 1
 
+    tkinter.Label(top, text='编码方式:', bg=bg, fg=fg, font=FONT, width=width_B, height=height_B).grid(column=a_x,
 
+                                                                                                   row=a_y)  # 设置说明
 
+    Code_Input = tkinter.Entry(top, width=width_B)
 
+    Code_Input.grid(column=a_x + 1, row=a_y, sticky=tkinter.E + tkinter.W)
 
+    buttom = tkinter.Checkbutton(top, bg=bg, fg=fg, activebackground=bg, activeforeground=fg, selectcolor=bg,
 
+                                 text='字符串类型',
 
+                                 variable=str_must)
 
+    buttom.grid(column=a_x + 2, row=a_y, sticky=tkinter.W)
 
+
 
+    a_y += 1
 
+    tkinter.Label(top, text='CSV分隔符:', bg=bg, fg=fg, font=FONT, width=width_B, height=height_B).grid(column=a_x,
 
+                                                                                                     row=a_y)  # 设置说明
 
+    Seq_Input = tkinter.Entry(top, width=width_B)
 
+    Seq_Input.grid(column=a_x + 1,columnspan=2, row=a_y, sticky=tkinter.E + tkinter.W)
 
+
 
+    a_y += 1
 
+    Form_BOX = tkinter.Listbox(top, width=width_B * 3, height=height_B * 10)  # 显示符号
 
+    Form_BOX.grid(column=a_x, row=a_y, columnspan=3, rowspan=10, sticky=tkinter.E + tkinter.W + tkinter.S + tkinter.N)
 
+    #422
 
+    a_y += 10
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='添加数据', command=Merge_Add, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='删除数据', command=Merge_Del, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 1, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='组合数据', command=Merge, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 2, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    global Processing_type, Shape_Input, Merge_BOX
 
+    a_y += 1
 
+    Merge_BOX = tkinter.Listbox(top, width=width_B * 3, height=height_B * 3)  # 显示符号
 
+    Merge_BOX.grid(column=a_x, row=a_y, columnspan=3, rowspan=3, sticky=tkinter.E + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 3
 
+    Processing_type = tkinter.IntVar()#正，负，0
 
+    lable = ['横向处理','纵向处理','深度处理']#复选框
 
+    for i in range(3):
 
+        tkinter.Radiobutton(top,bg = bg,fg = fg,activebackground=bg,activeforeground=fg,selectcolor=bg,text=lable[i],
 
+                            variable=Processing_type, value=i).grid(column=a_x+i, row=a_y, sticky=tkinter.W)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='数据切片', command=Two_Split, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='数据分割', command=Split, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 1, row=a_y,columnspan=2,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Label(top, text='重塑形状:', bg=bg, fg=fg, font=FONT, width=width_B, height=height_B).grid(column=a_x,row=a_y)
 
+    Shape_Input = tkinter.Entry(top, width=width_B)
 
+    Shape_Input.grid(column=a_x + 1, row=a_y, sticky=tkinter.E + tkinter.W)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='矩阵重塑', command=Reshape, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 2, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='矩阵伸展', command=Reval, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='矩阵降维', command=Del_Ndim, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 1, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='矩阵转置', command=T, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 2, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_x += 3
 
+    tkinter.Label(top, text='', bg=bg, fg=fg, font=FONT, width=1).grid(column=a_x, row=a_y)  # 设置说明
 
+    a_x += 1
 
+    a_y = 0
 
+
 
+    tkinter.Label(top, text='【机器学习】', bg=bg, fg=fg, font=FONT, width=width_B * 3, height=height_B).grid(column=a_x,
 
+                                                                                                        columnspan=3,
 
+                                                                                                        row=a_y,
 
+                                                                                                        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)  # 设置说明
 
+
 
+    global ML_BOX, ML_OUT,X_OUT,Y_OUT
 
+    a_y += 1
 
+    X_OUT = tkinter.StringVar()
 
+    Put = tkinter.Entry(top, width=width_B * 2, textvariable=X_OUT)
 
+    Put.grid(column=a_x, row=a_y, columnspan=2, sticky=tkinter.E + tkinter.W)
 
+    Put['state'] = 'readonly'
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='选用X集', command=set_Feature, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 2, row=a_y, sticky=tkinter.E + tkinter.W)
 
+
 
+    Y_OUT = tkinter.StringVar()
 
+    a_y += 1
 
+    Put = tkinter.Entry(top, width=width_B * 2, textvariable=Y_OUT)
 
+    Put.grid(column=a_x, row=a_y, columnspan=2, sticky=tkinter.E + tkinter.W)
 
+    Put['state'] = 'readonly'
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='选用Y集', command=set_Label, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 2, row=a_y, sticky=tkinter.E + tkinter.W)
 
+
 
+    ML_OUT = tkinter.StringVar()
 
+    a_y += 1
 
+    Put = tkinter.Entry(top, width=width_B * 2, textvariable=ML_OUT)
 
+    Put.grid(column=a_x, row=a_y, columnspan=2, sticky=tkinter.E + tkinter.W)
 
+    Put['state'] = 'readonly'
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='选用学习器', command=set_Learner, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 2, row=a_y, sticky=tkinter.E + tkinter.W)
 
+
 
+    global Split_Input
 
+    a_y += 1
 
+    tkinter.Label(top, text='测试数据分割:', bg=bg, fg=fg, font=FONT, width=width_B, height=height_B).grid(column=a_x,
 
+                                                                                                     row=a_y)
 
+    Split_Input = tkinter.Entry(top, width=width_B * 2)
 
+    Split_Input.grid(column=a_x + 1, row=a_y, columnspan=2, sticky=tkinter.E + tkinter.W)
 
+
 
+    a_y += 1
 
+    ML_BOX = tkinter.Listbox(top, width=width_B * 3, height=height_B * 5)
 
+    ML_BOX.grid(column=a_x, row=a_y, columnspan=3, rowspan=5, sticky=tkinter.E + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 5
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='导入学习器', font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='查看数据', command=Show_Args, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 1, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='删除学习器', command=Del_Leaner, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 2, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='训练机器', command=Fit_Learner, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='测试机器', command=Score_Learner, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 1, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='数据预测', command=Predict_Learner, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 2, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='单一变量特征选择', command=Add_SelectKBest, font=FONT, width=width_B, height=height_B).grid(column=a_x, row=a_y,columnspan=2,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='映射标准化', command=Add_Mapzoom, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x+2, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='方差特征选择',command=Add_Variance, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='使用学习器筛选', command=Add_SelectFrom_Model, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 1, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='模糊量化标准化', command=Add_Fuzzy_quantization, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 2, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='Z-score',command=Add_Z_Score, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='离差标准化', command=Add_MinMaxScaler, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 1, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='Log变换', command=Add_LogScaler, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 2, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='atan变换',command=Add_atanScaler, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='小数定标准化', command=Add_decimalScaler, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 1, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='Sigmod变换', command=Add_sigmodScaler, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 2, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='正则化',command=Add_Regularization, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='二值离散', command=Add_Binarizer, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 1, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='多值离散', command=Add_Discretization, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 2, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='独热编码',command=Add_OneHotEncoder, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='数字编码', command=Add_Label, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 1, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='缺失填充', command=Add_Missed, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 2, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='PCA降维',command=Add_PCA, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='RPCA降维', command=Add_RPCA, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 1, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='KPCA升维', command=Add_KPCA, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 2, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='LDA降维', command=Add_LDA, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='NMF降维', command=Add_NMF, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x+1, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='t-SNE', command=Add_TSNE, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x+2, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='线性回归', command=Add_Line, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='岭回归', command=Add_Ridge, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 1, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='Lasso', command=Add_Lasso, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 2, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='逻辑回归', command=Add_LogisticRegression, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='K邻近预测', command=Add_Knn, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 1, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='K邻近分类', command=Add_Knn_Class, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 2, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='梯度回归树回归', command=Add_GradientTree, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='决策树回归',command=Add_Tree, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 1, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='决策树分类',command=Add_Tree_Class, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 2, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='梯度回归树分类', command=Add_GradientTree_class, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='随机森林回归', command=Add_Forest, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x+1, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='随机森林分类',command=Add_Forest_class, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 2, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_x += 3
 
+    tkinter.Label(top, text='', bg=bg, fg=fg, font=FONT, width=1).grid(column=a_x, row=a_y)  # 设置说明
 
+    a_x += 1
 
+    a_y = 0
 
+
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='多层感知机回归', command=Add_MLP, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='多层感知机分类', command=Add_MLP_class, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x+1, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='随机森林分类',command=Add_Forest_class, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 2, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='支持向量机分类:SVC', command=Add_SVC, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x, row=a_y,columnspan=2,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='回归:SVR', command=Add_SVR, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 2, row=a_y,
 
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='k-means', command=Add_KMeans, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='凝聚聚类', command=Add_Agglomerative, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x+1, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='DBSCAN',command=Add_DBSCAN, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 2, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='特征分类图', command=Add_ClassBar, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='临近特征回归图', command=Add_FeatureScatter, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x+1, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='临近特征分类图',command=Add_FeatureScatterClass, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 2, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='所有特征回归图', command=Add_FeatureScatter_all, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='所有特征分类图', command=Add_FeatureScatterClass_all, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x+1, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='临近特征预测图',command=Add_Predictive_HeatMap, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 2, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='所有特征预测图', command=Add_Predictive_HeatMap_More, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='矩阵热力图', command=Add_Numpy_To_HeatMap, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x+1, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='数据y-x散点图',command=Add_FeatureY_X, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 2, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='聚类树状图', command=Add_ClusterTree, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='获取数据', command=Add_View_data, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x+1, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='矩阵散点图',command=Add_MatrixScatter, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 2, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='特征相关性', command=Add_corr, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='曲线拟合', command=Curve_fitting, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x+1, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='快速傅里叶',command=Add_Fast_Fourier, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 2, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='数据统计', command=Add_Des, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='双逆向傅里叶',command=Add_Reverse_Fast_Fourier2, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x+1, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='逆向傅里叶',command=Add_Reverse_Fast_Fourier, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 2, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='分类模型评估', command=Show_Class_Score, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='回归模型评估', command=Show_Regression_Score, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x+1, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='聚类模型评估', command=Show_Clustering_Score, font=FONT, width=width_B, height=height_B).grid(
 
+        column=a_x + 2, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y += 1
 
+    tkinter.Label(top, text='【学习器配置】', bg=bg, fg=fg, font=FONT, width=width_B * 3, height=height_B).grid(column=a_x,
 
+                                                                                                        columnspan=3,
 
+                                                                                                        row=a_y,
 
+                                                                                                        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)  # 设置说明
 
+
 
+    global Global_Type
 
+    a_y += 1
 
+    Global_Type = []
 
+    lable = ['聚类仅邻近特征','导出单独页面','导出表格CSV']#复选框
 
+    for i in range(3):
 
+        Global_Type.append(tkinter.IntVar())
 
+        tkinter.Checkbutton(top,bg = bg,fg = fg,activebackground=bg,activeforeground=fg,selectcolor=bg, text=lable[i],
 
+                            variable=Global_Type[-1],command=globalSeeting).grid(column=a_x+i, row=a_y, sticky=tkinter.W)
 
+
 
+    a_y += 1
 
+    lable = ['导出模型','压缩为tar.gz','创建新目录']#复选框
 
+    for i in range(3):
 
+        Global_Type.append(tkinter.IntVar())
 
+        tkinter.Checkbutton(top,bg = bg,fg = fg,activebackground=bg,activeforeground=fg,selectcolor=bg, text=lable[i],
 
+                            variable=Global_Type[-1],command=globalSeeting).grid(column=a_x+i, row=a_y, sticky=tkinter.W)
 
+
 
+    for i in Global_Type[1:]:i.set(1)
 
+    globalSeeting()
 
+
 
+    global Args_Learner
 
+    a_y += 1
 
+    Args_Learner = tkinter.Text(top, width=width_B * 3, height=height_B * 6)
 
+    Args_Learner.grid(column=a_x, row=a_y, columnspan=3, rowspan=6,
 
+                      sticky=tkinter.E + tkinter.W + tkinter.N + tkinter.S)
 
+
 
+    a_y += 6
 
+    tkinter.Label(top, text='【矩阵运算】', bg=bg, fg=fg, font=FONT, width=width_B * 3, height=height_B).grid(column=a_x,
 
+                                                                                                        columnspan=3,
 
+                                                                                                        row=a_y,
 
+                                                                                                        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)  # 设置说明
 
+
 
+    global Cul_Box,CulType_Input,Value_Input
 
+    a_y += 1
 
+    Cul_Box = tkinter.Listbox(top, width=width_B * 3, height=height_B * 1)
 
+    Cul_Box.grid(column=a_x, row=a_y, columnspan=3, rowspan=1, sticky=tkinter.E + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    a_y +=1
 
+    tkinter.Label(top, text='运算类型:', bg=bg, fg=fg, font=FONT, width=width_B, height=height_B).grid(column=a_x,row=a_y)
 
+    CulType_Input = tkinter.Entry(top, width=width_B * 2)
 
+    CulType_Input.grid(column=a_x + 1, row=a_y, columnspan=2, sticky=tkinter.E + tkinter.W)
 
+
 
+    a_y +=1
 
+    tkinter.Label(top, text='键入参数:', bg=bg, fg=fg, font=FONT, width=width_B, height=height_B).grid(column=a_x,row=a_y)
 
+    Value_Input = tkinter.Entry(top, width=width_B * 2)
 
+    Value_Input.grid(column=a_x + 1, row=a_y, columnspan=2, sticky=tkinter.E + tkinter.W)
 
+
 
+    a_y += 1
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='选择参数', command=Cul_Add, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='键入参数', command=Cul_Input, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 1, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+    tkinter.Button(top, bg=bbg, fg=fg, text='矩阵运算', command=Cul_Numpy, font=FONT, width=width_B,
 
+                   height=height_B).grid(column=a_x + 2, row=a_y,
 
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
 
+
 
+    top.mainloop()
 
+
 
+def Add_Reverse_Fast_Fourier2():  # 添加Lenear的核心
 
+    Add_leaner('[2]Reverse_Fast_Fourier')
 
+
 
+def Add_Reverse_Fast_Fourier():  # 添加Lenear的核心
 
+    Add_leaner('Reverse_Fast_Fourier')
 
+
 
+def Add_Fast_Fourier():  # 添加Lenear的核心
 
+    Add_leaner('Fast_Fourier')
 
+
 
+def Curve_fitting():
 
+    Dic = askopenfilename(title='导入参数')
 
+    with open(Dic,'r') as f:
 
+        ML.Add_Curve_Fitting(f.read(),Text=get_Args_Learner())
 
+        Update_Leaner()
 
+
 
+def Show_Clustering_Score():
 
+    Show_Score(2)
 
+
 
+def Show_Regression_Score():
 
+    Show_Score(1)
 
+
 
+def Show_Class_Score():
 
+    Show_Score(0)
 
+
 
+def Show_Score(func):
 
+    learner = get_Learner(True)
 
+    Dic = askdirectory(title='选择保存位置')
 
+    data = ML.Show_Score(learner,Dic,get_Name(False,True),get_Name(False,False),func)
 
+    webbrowser.open(data[0])
 
+    webbrowser.open(data[1])#还可以打开文件管理器
 
+    Update_BOX()
 
+
 
+def Cul_Numpy():
 
+    global Cul_list, Cul_Type, CulType_Input
 
+    func = CulType_Input.get()
 
+    if len(Cul_list) == 2 and 1 in Cul_Type:
 
+        ML.Cul_Numpy(Cul_list,Cul_Type,func)
 
+    Update_BOX()
 
+
 
+def Add_Cul(num,type_):
 
+    global Cul_list, Cul_Type
 
+    if len(Cul_list) == 2:
 
+        del Cul_list[0]
 
+        del Cul_Type[0]
 
+    Cul_list.append(num)
 
+    Cul_Type.append(type_)
 
+
 
+def Update_Cul():
 
+    global Cul_list,Cul_Type,Cul_Box
 
+    Cul_Box.delete(0, tkinter.END)
 
+    a = ['第一参数','第二参数']
 
+    b = ['参数','矩阵']
 
+    Cul_Box.insert(tkinter.END, *[f'{a[i]} {Cul_list[i]} {b[Cul_Type[i]]}' for i in range(len(Cul_list))])
 
+
 
+def Cul_Input():
 
+    global Cul_Box,CulType_Input,Value_Input
 
+    num = eval(Value_Input.get(),{})
 
+    Add_Cul(num,0)
 
+    Update_Cul()
 
+
 
+def Cul_Add():
 
+    name = get_Name()
 
+    Add_Cul(name,1)
 
+    Update_Cul()
 
+
 
+def Del_Leaner():
 
+    Learn = get_Learner(True)
 
+    set_Learne = get_Learner(False)  # 获取学习器Learner
 
+    if set_Learne != Learn:
 
+        ML.Del_Leaner(Learn)
 
+    Update_Leaner()
 
+
 
+def globalSeeting():
 
+    global Global_Type
 
+    args = [bool(i.get()) for i in Global_Type]
 
+    Learn_Numpy.set_Global(*args)
 
+
 
+def Reshape():
 
+    global ML,Processing_type
 
+    shape = eval(f'[{Shape_Input.get()}]')[0]
 
+    ML.reShape(get_Name(),shape)
 
+    Update_BOX()
 
+
 
+def T():
 
+    global ML,Processing_type,Shape_Input
 
+    try:
 
+        Func = eval(f'[{Shape_Input.get()}]')
 
+    except:
 
+        Func = None
 
+    ML.T(get_Name(),Func)
 
+    Update_BOX()
 
+
 
+def Del_Ndim():
 
+    global ML
 
+    ML.Del_Ndim(get_Name())
 
+    Update_BOX()
 
+
 
+def Reval():
 
+    global ML,Processing_type
 
+    Type = Processing_type.get()
 
+    ML.Reval(get_Name(),Type)
 
+    Update_BOX()
 
+
 
+def Two_Split():
 
+    global ML,Processing_type,Shape_Input
 
+    Type = Processing_type.get()
 
+    ML.Two_Split(get_Name(),Shape_Input.get(),Type)
 
+    Update_BOX()
 
+
 
+def Split():
 
+    global ML,Processing_type,Shape_Input
 
+    Type = Processing_type.get()
 
+    try:
 
+        Split = eval(f'[{Shape_Input.get()}]')[0]
 
+    except:
 
+        Split = 2
 
+    ML.Split(get_Name(),Split,Type)
 
+    Update_BOX()
 
+
 
+def Merge():
 
+    global Merge_list,ML,Processing_type
 
+    if len(Merge_list) < 1:
 
+        return False
 
+    Type = Processing_type.get()
 
+    ML.Merge(Merge_list,Type)
 
+    Update_BOX()
 
+
 
+def Update_Merge():
 
+    global Merge_list,Merge_BOX
 
+    Merge_BOX.delete(0, tkinter.END)
 
+    Merge_BOX.insert(tkinter.END, *Merge_list)
 
+
 
+def Merge_Del():
 
+    global Merge_list,Merge_BOX
 
+    del Merge_list[Merge_BOX.curselection()[0]]
 
+    Update_Merge()
 
+
 
+def Merge_Add():
 
+    global Merge_list
 
+    name = get_Name()
 
+    Merge_list.append(name)
 
+    Update_Merge()
 
+
 
+
 
+def Show_Args():
 
+    learner = get_Learner(True)
 
+    Dic = askdirectory(title='选择保存位置')
 
+    data = ML.Show_Args(learner,Dic)
 
+    webbrowser.open(data[0])
 
+    webbrowser.open(data[1])#还可以打开文件管理器
 
+    Update_BOX()
 
+
 
+
 
+def get_Args_Learner():
 
+    global Args_Learner
 
+    return Args_Learner.get('0.0', tkinter.END)
 
+
 
+
 
+def Score_Learner():
 
+    learner = get_Learner()
 
+    socore = ML.Score(get_Name(False,True),get_Name(False,False), learner)
 
+    tkinter.messagebox.showinfo('测试完成', f'针对测试数据评分结果为:{socore}')
 
+
 
+
 
+def Predict_Learner():
 
+    learner = get_Learner()
 
+    Data = ML.Predict(get_Name(False,True),learner)
 
+    title = f'CoTan数据处理 学习器:{learner}'
 
+    Creat_TextSheet(Data, title)
 
+    Update_BOX()
 
+
 
+
 
+def Fit_Learner():
 
+    learner = get_Learner()
 
+    try:
 
+        split = float(Split_Input.get())
 
+        if split < 0 or 1 < split: raise Exception
 
+    except:
 
+        split = 0.3
 
+    socore = ML.Fit(get_Name(False,True),get_Name(False,False), learner, Text=get_Args_Learner(), split=split)
 
+    tkinter.messagebox.showinfo('训练完成', f'针对训练数据({(1 - split) * 100}%)评分结果为:{socore[0]}\n'
 
+                                        f'针对测试数据评分({split * 100}%)结果为:{socore[1]}')
 
+
 
+def set_Feature():
 
+    global X_OUT
 
+    X_OUT.set(get_Name())
 
+
 
+
 
+def set_Label():
 
+    global Y_OUT
 
+    Y_OUT.set(get_Name())
 
+
 
+
 
+def set_Learner():
 
+    global ML_OUT
 
+    ML_OUT.set(get_Learner(True))
 
+
 
+
 
+def get_Learner(Type=False):
 
+    global Learn_Dic, ML_BOX, ML_OUT
 
+    if Type:
 
+        try:
 
+            return list(Learn_Dic.keys())[ML_BOX.curselection()[0]]
 
+        except:
 
+            try:
 
+                return list(Learn_Dic.keys)[0]
 
+            except:
 
+                return get_Learner(False)
 
+    else:
 
+        try:
 
+            return ML_OUT.get()
 
+        except:
 
+            return None
 
+
 
+def Add_Des():  # 添加Lenear的核心
 
+    Add_leaner('Statistics')
 
+
 
+def Add_corr():
 
+    Add_leaner('Correlation')
 
+
 
+def Add_MatrixScatter():
 
+    Add_leaner('MatrixScatter')
 
+
 
+def Add_View_data():
 
+    ML.Add_View_data(get_Learner(), Text=get_Args_Learner())
 
+    Update_Leaner()
 
+
 
+def Add_ClusterTree():
 
+    Add_leaner('ClusterTree')
 
+
 
+def Add_FeatureY_X():
 
+    Add_leaner('FeatureY-X')
 
+
 
+def Add_Numpy_To_HeatMap():
 
+    Add_leaner('HeatMap')
 
+
 
+def Add_Predictive_HeatMap_More():  # 添加Lenear的核心
 
+    ML.Add_Predictive_HeatMap_More(get_Learner(), Text=get_Args_Learner())
 
+    Update_Leaner()
 
+
 
+def Add_Predictive_HeatMap():  # 添加Lenear的核心
 
+    ML.Add_Predictive_HeatMap(get_Learner(), Text=get_Args_Learner())
 
+    Update_Leaner()
 
+
 
+def Add_FeatureScatterClass_all():
 
+    Add_leaner('FeatureScatterClass_all')
 
+
 
+def Add_FeatureScatter_all():
 
+    Add_leaner('FeatureScatter_all')
 
+
 
+def Add_FeatureScatterClass():
 
+    Add_leaner('FeatureScatterClass')
 
+
 
+def Add_FeatureScatter():
 
+    Add_leaner('FeatureScatter')
 
+
 
+def Add_ClassBar():
 
+    Add_leaner('ClassBar')
 
+
 
+def Add_DBSCAN():
 
+    Add_leaner('DBSCAN')
 
+
 
+def Add_Agglomerative():
 
+    Add_leaner('Agglomerative')
 
+
 
+def Add_KMeans():
 
+    Add_leaner('k-means')
 
+
 
+def Add_MLP_class():
 
+    Add_leaner('MLP_class')
 
+
 
+def Add_MLP():
 
+    Add_leaner('MLP')
 
+
 
+def Add_SVR():
 
+    Add_leaner('SVR')
 
+
 
+def Add_SVC():
 
+    Add_leaner('SVC')
 
+
 
+def Add_GradientTree():
 
+    Add_leaner('GradientTree')
 
+
 
+def Add_GradientTree_class():
 
+    Add_leaner('GradientTree_class')
 
+
 
+def Add_TSNE():
 
+    Add_leaner('t-SNE')
 
+
 
+def Add_NMF():
 
+    Add_leaner('NMF')
 
+
 
+def Add_LDA():
 
+    Add_leaner('LDA')
 
+
 
+def Add_KPCA():
 
+    Add_leaner('KPCA')
 
+
 
+def Add_RPCA():
 
+    Add_leaner('RPCA')
 
+
 
+def Add_PCA():
 
+    Add_leaner('PCA')
 
+
 
+def Add_Missed():
 
+    Add_leaner('Missed')
 
+
 
+def Add_Label():
 
+    Add_leaner('Label')
 
+
 
+def Add_OneHotEncoder():
 
+    Add_leaner('OneHotEncoder')
 
+
 
+def Add_Discretization():
 
+    Add_leaner('Discretization')
 
+
 
+def Add_Binarizer():
 
+    Add_leaner('Binarizer')
 
+
 
+def Add_Regularization():
 
+    Add_leaner('Regularization')
 
+
 
+def Add_Fuzzy_quantization():
 
+    Add_leaner('Fuzzy_quantization')
 
+
 
+def Add_Mapzoom():
 
+    Add_leaner('Mapzoom')
 
+
 
+def Add_sigmodScaler():
 
+    Add_leaner('sigmodScaler')
 
+
 
+def Add_decimalScaler():
 
+    Add_leaner('decimalScaler')
 
+
 
+def Add_atanScaler():
 
+    Add_leaner('atanScaler')
 
+
 
+def Add_LogScaler():
 
+    Add_leaner('LogScaler')
 
+
 
+def Add_MinMaxScaler():
 
+    Add_leaner('MinMaxScaler')
 
+
 
+def Add_Z_Score():
 
+    Add_leaner('Z-Score')
 
+
 
+def Add_Forest():
 
+    Add_leaner('Forest')
 
+
 
+def Add_Forest_class():
 
+    Add_leaner('Forest_class')
 
+
 
+def Add_Tree_Class():
 
+    Add_leaner('Tree_class')
 
+
 
+def Add_Tree():
 
+    Add_leaner('Tree')
 
+
 
+
 
+def Add_SelectKBest():
 
+    Add_leaner('SelectKBest')
 
+
 
+
 
+def Add_Knn_Class():
 
+    Add_leaner('Knn_class')
 
+
 
+
 
+def Add_LogisticRegression():
 
+    Add_leaner('LogisticRegression')
 
+
 
+
 
+def Add_Lasso():
 
+    Add_leaner('Lasso')
 
+
 
+
 
+def Add_Variance():
 
+    Add_leaner('Variance')
 
+
 
+
 
+def Add_Knn():
 
+    Add_leaner('Knn')
 
+
 
+
 
+def Add_Ridge():
 
+    Add_leaner('Ridge')
 
+
 
+
 
+def Add_Line():
 
+    Add_leaner('Line')
 
+
 
+
 
+def Add_SelectFrom_Model():  # 添加Lenear的核心
 
+    ML.Add_SelectFrom_Model(get_Learner(), Text=get_Args_Learner())
 
+    Update_Leaner()
 
+
 
+
 
+def Add_leaner(Type):  # 添加Lenear的核心
 
+    ML.Add_Learner(Type, Text=get_Args_Learner())
 
+    Update_Leaner()
 
+
 
+
 
+def Update_Leaner():
 
+    global Learn_Dic, ML_BOX
 
+    Learn_Dic = ML.Return_Learner()
 
+    ML_BOX.delete(0, tkinter.END)
 
+    ML_BOX.insert(tkinter.END, *Learn_Dic.keys())
 
+
 
+
 
+def Show_One():
 
+    global PATH, to_HTML_Type
 
+    Dic = f'{PATH}/$Show_Sheet.html'
 
+    try:
 
+        name = get_Name()
 
+        if name == None: raise Exception
 
+        ML.to_Html_One(name, Dic)
 
+        webbrowser.open(Dic)
 
+    except:
 
+        # pass
 
+        raise
 
+
 
+
 
+def Show():
 
+    global PATH, to_HTML_Type
 
+    Dic = f'{PATH}/$Show_Sheet.html'
 
+    try:
 
+        name = get_Name()
 
+        if name == None: raise Exception
 
+        ML.to_Html(name, Dic, to_HTML_Type.get())
 
+        webbrowser.open(Dic)
 
+    except:
 
+        pass
 
+
 
+
 
+def to_CSV():
 
+    global top, Seq_Input, Code_Input, str_must, Index_must
 
+    Dic = asksaveasfilename(title='选择保存的CSV', filetypes=[("CSV", ".csv")])
 
+    Seq = Seq_Input.get()
 
+    name = get_Name()
 
+    ML.to_CSV(Dic, name, Seq)
 
+    Update_BOX()
 
+
 
+
 
+def Add_CSV():
 
+    global top, Seq_Input, Code_Input, str_must, name_Input
 
+    Dic = askopenfilename(title='选择载入的CSV', filetypes=[("CSV", ".csv")])
 
+    if Dic == '': return False
 
+    Seq = Seq_Input.get()
 
+    Codeing = Code_Input.get()
 
+    str_ = bool(str_must.get())
 
+    name = name_Input.get().replace(' ', '')
 
+    if name == '':
 
+        name = os.path.splitext(os.path.split(Dic)[1])[0]
 
+        print(name)
 
+    if Codeing == '':
 
+        with open(Dic, 'rb') as f:
 
+            Codeing = chardet.detect(f.read())['encoding']
 
+    if Seq == '': Seq = ','
 
+    ML.read_csv(Dic, name, Codeing, str_, Seq,)
 
+    Update_BOX()
 
+
 
+
 
+def Add_Python():
 
+    global top, Seq_Input, Code_Input, str_must, Index_must
 
+    Dic = askopenfilename(title='选择载入的py', filetypes=[("Python", ".py"), ("Txt", ".txt")])
 
+    name = name_Input.get().replace(' ', '')
 
+    if name == '':
 
+        name = os.path.splitext(os.path.split(Dic)[1])[0]
 
+    with open(Dic, 'r') as f:
 
+        ML.Add_Python(f.read(), name)
 
+    Update_BOX()
 
+
 
+def get_Name(Type=True, x=True):  # 获得名字统一接口
 
+    global Form_List, Form_BOX, X_OUT
 
+    if Type:
 
+        try:
 
+            return Form_List[Form_BOX.curselection()[0]]
 
+        except:
 
+            try:
 
+                return Form_List[0]
 
+            except:
 
+                return None
 
+    else:
 
+        try:
 
+            if x:
 
+                return X_OUT.get()
 
+            else:
 
+                return Y_OUT.get()
 
+        except:
 
+            return None
 
+
 
+
 
+def Update_BOX():
 
+    global top, Form_BOX, Form_List
 
+    Form_List = list(ML.get_Form().keys())
 
+    Form_BOX.delete(0, tkinter.END)
 
+    Form_BOX.insert(tkinter.END, *Form_List)
 
+
 
+def Creat_TextSheet(data, name):
 
+    global bg
 
+    new_top = tkinter.Toplevel(bg=bg)
 
+    new_top.title(name)
 
+    new_top.geometry('+10+10')  # 设置所在位置
 
+    text = ScrolledText(new_top, font=('黑体', 13), height=50)
 
+    text.pack(fill=tkinter.BOTH)
 
+    text.insert('0.0', data)
 
+    text.config(state=tkinter.DISABLED)
 
+    new_top.resizable(width=False, height=False)

+ 2 - 0
Machine_learning_analysis/__init__.py
مشاهده فایل 

@@ -0,0 +1,2 @@
 
+from Machine_learning_analysis.Machine_learning import Main
 
+print('机器学习分析加载完毕...')

BIN
Machine_learning_analysis/__pycache__/Learn_Numpy.cpython-37.pyc
مشاهده فایل


BIN
Machine_learning_analysis/__pycache__/Machine_learning.cpython-37.pyc
مشاهده فایل


BIN
Machine_learning_analysis/__pycache__/__init__.cpython-37.pyc
مشاهده فایل


+ 0 - 35
Test2.py
مشاهده فایل 

@@ -1,35 +0,0 @@
 
-import json
 
-import os
 
-
 
-from pyecharts import options as opts
 
-from pyecharts.charts import Page, TreeMap
 
-
 
-
 
-def treemap_base() -> TreeMap:
 
-    data = [
 
-        {"value": 40, "name": "我是A"},
 
-        {
 
-            "name": "我是B",
 
-            "children": [
 
-                {
 
-                    "value": 76,
 
-                    "name": "我是B.children",
 
-                    "children": [
 
-                        {"value": 12, "name": "我是B.children.a"},
 
-                        {"value": 28, "name": "我是B.children.b"},
 
-                        {"value": 20, "name": "我是B.children.c"},
 
-                        {"value": 16, "name": "我是B.children.d"},
 
-                    ],
 
-                }
 
-            ],
 
-        },
 
-    ]
 
-    print(data)
 
-    c = (
 
-        TreeMap()
 
-        .add("演示数据", data,label_opts=opts.LabelOpts(is_show=True,position='inside'))
 
-        .set_global_opts(title_opts=opts.TitleOpts(title="TreeMap-基本示例"))
 
-    )
 
-    return c
 
-
 
-treemap_base().render()

+ 16 - 0
Text.py
مشاهده فایل 

@@ -0,0 +1,16 @@
 
+# import subprocess
 
+#
 
+# a = subprocess.Popen('dir;dir',shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE,
 
+#                      cwd=r'C:\Users\宋子桓\Desktop\Git_Test')
 
+# # print(a.stdout())
 
+# a.wait()
 
+# print(a.returncode)
 
+# while True:
 
+#     i = a.stdout.readline().decode('utf-8')
 
+#     if i == '':break
 
+#     print(i,end='')
 
+
 
+a = '&&'
 
+# b = ['1','2','3']
 
+b = ['1']
 
+print(a.join(b))

BIN
__pycache__/Git_Ctrl.cpython-37.pyc
مشاهده فایل


BIN
__pycache__/Learn.cpython-37.pyc
مشاهده فایل


+ 347 - 0
assets/ResizeSensor.js
مشاهده فایل 

@@ -0,0 +1,347 @@
 
+'use strict';
 
+
 
+/**
 
+ * Copyright Marc J. Schmidt. See the LICENSE file at the top-level
 
+ * directory of this distribution and at
 
+ * https://github.com/marcj/css-element-queries/blob/master/LICENSE.
 
+ */
 
+(function (root, factory) {
 
+    if (typeof define === "function" && define.amd) {
 
+        define(factory);
 
+    } else if (typeof exports === "object") {
 
+        module.exports = factory();
 
+    } else {
 
+        root.ResizeSensor = factory();
 
+    }
 
+}(typeof window !== 'undefined' ? window : this, function () {
 
+
 
+    // Make sure it does not throw in a SSR (Server Side Rendering) situation
 
+    if (typeof window === "undefined") {
 
+        return null;
 
+    }
 
+    // https://github.com/Semantic-Org/Semantic-UI/issues/3855
 
+    // https://github.com/marcj/css-element-queries/issues/257
 
+    var globalWindow = typeof window != 'undefined' && window.Math == Math
 
+        ? window
 
+        : typeof self != 'undefined' && self.Math == Math
 
+            ? self
 
+            : Function('return this')();
 
+    // Only used for the dirty checking, so the event callback count is limited to max 1 call per fps per sensor.
 
+    // In combination with the event based resize sensor this saves cpu time, because the sensor is too fast and
 
+    // would generate too many unnecessary events.
 
+    var requestAnimationFrame = globalWindow.requestAnimationFrame ||
 
+        globalWindow.mozRequestAnimationFrame ||
 
+        globalWindow.webkitRequestAnimationFrame ||
 
+        function (fn) {
 
+            return globalWindow.setTimeout(fn, 20);
 
+        };
 
+
 
+    /**
 
+     * Iterate over each of the provided element(s).
 
+     *
 
+     * @param {HTMLElement|HTMLElement[]} elements
 
+     * @param {Function}                  callback
 
+     */
 
+    function forEachElement(elements, callback){
 
+        var elementsType = Object.prototype.toString.call(elements);
 
+        var isCollectionTyped = ('[object Array]' === elementsType
 
+            || ('[object NodeList]' === elementsType)
 
+            || ('[object HTMLCollection]' === elementsType)
 
+            || ('[object Object]' === elementsType)
 
+            || ('undefined' !== typeof jQuery && elements instanceof jQuery) //jquery
 
+            || ('undefined' !== typeof Elements && elements instanceof Elements) //mootools
 
+        );
 
+        var i = 0, j = elements.length;
 
+        if (isCollectionTyped) {
 
+            for (; i < j; i++) {
 
+                callback(elements[i]);
 
+            }
 
+        } else {
 
+            callback(elements);
 
+        }
 
+    }
 
+
 
+    /**
 
+    * Get element size
 
+    * @param {HTMLElement} element
 
+    * @returns {Object} {width, height}
 
+    */
 
+    function getElementSize(element) {
 
+        if (!element.getBoundingClientRect) {
 
+            return {
 
+                width: element.offsetWidth,
 
+                height: element.offsetHeight
 
+            }
 
+        }
 
+
 
+        var rect = element.getBoundingClientRect();
 
+        return {
 
+            width: Math.round(rect.width),
 
+            height: Math.round(rect.height)
 
+        }
 
+    }
 
+
 
+    /**
 
+     * Apply CSS styles to element.
 
+     *
 
+     * @param {HTMLElement} element
 
+     * @param {Object} style
 
+     */
 
+    function setStyle(element, style) {
 
+        Object.keys(style).forEach(function(key) {
 
+            element.style[key] = style[key];
 
+        });
 
+    }
 
+
 
+    /**
 
+     * Class for dimension change detection.
 
+     *
 
+     * @param {Element|Element[]|Elements|jQuery} element
 
+     * @param {Function} callback
 
+     *
 
+     * @constructor
 
+     */
 
+    var ResizeSensor = function(element, callback) {
 
+        /**
 
+         *
 
+         * @constructor
 
+         */
 
+        function EventQueue() {
 
+            var q = [];
 
+            this.add = function(ev) {
 
+                q.push(ev);
 
+            };
 
+
 
+            var i, j;
 
+            this.call = function(sizeInfo) {
 
+                for (i = 0, j = q.length; i < j; i++) {
 
+                    q[i].call(this, sizeInfo);
 
+                }
 
+            };
 
+
 
+            this.remove = function(ev) {
 
+                var newQueue = [];
 
+                for(i = 0, j = q.length; i < j; i++) {
 
+                    if(q[i] !== ev) newQueue.push(q[i]);
 
+                }
 
+                q = newQueue;
 
+            };
 
+
 
+            this.length = function() {
 
+                return q.length;
 
+            }
 
+        }
 
+
 
+        /**
 
+         *
 
+         * @param {HTMLElement} element
 
+         * @param {Function}    resized
 
+         */
 
+        function attachResizeEvent(element, resized) {
 
+            if (!element) return;
 
+            if (element.resizedAttached) {
 
+                element.resizedAttached.add(resized);
 
+                return;
 
+            }
 
+
 
+            element.resizedAttached = new EventQueue();
 
+            element.resizedAttached.add(resized);
 
+
 
+            element.resizeSensor = document.createElement('div');
 
+            element.resizeSensor.dir = 'ltr';
 
+            element.resizeSensor.className = 'resize-sensor';
 
+
 
+            var style = {
 
+                pointerEvents: 'none',
 
+                position: 'absolute',
 
+                left: '0px',
 
+                top: '0px',
 
+                right: '0px',
 
+                bottom: '0px',
 
+                overflow: 'hidden',
 
+                zIndex: '-1',
 
+                visibility: 'hidden',
 
+                maxWidth: '100%'
 
+            };
 
+            var styleChild = {
 
+                position: 'absolute',
 
+                left: '0px',
 
+                top: '0px',
 
+                transition: '0s',
 
+            };
 
+
 
+            setStyle(element.resizeSensor, style);
 
+
 
+            var expand = document.createElement('div');
 
+            expand.className = 'resize-sensor-expand';
 
+            setStyle(expand, style);
 
+
 
+            var expandChild = document.createElement('div');
 
+            setStyle(expandChild, styleChild);
 
+            expand.appendChild(expandChild);
 
+
 
+            var shrink = document.createElement('div');
 
+            shrink.className = 'resize-sensor-shrink';
 
+            setStyle(shrink, style);
 
+
 
+            var shrinkChild = document.createElement('div');
 
+            setStyle(shrinkChild, styleChild);
 
+            setStyle(shrinkChild, { width: '200%', height: '200%' });
 
+            shrink.appendChild(shrinkChild);
 
+
 
+            element.resizeSensor.appendChild(expand);
 
+            element.resizeSensor.appendChild(shrink);
 
+            element.appendChild(element.resizeSensor);
 
+
 
+            var computedStyle = window.getComputedStyle(element);
 
+            var position = computedStyle ? computedStyle.getPropertyValue('position') : null;
 
+            if ('absolute' !== position && 'relative' !== position && 'fixed' !== position) {
 
+                element.style.position = 'relative';
 
+            }
 
+
 
+            var dirty, rafId;
 
+            var size = getElementSize(element);
 
+            var lastWidth = 0;
 
+            var lastHeight = 0;
 
+            var initialHiddenCheck = true;
 
+            var lastAnimationFrame = 0;
 
+
 
+            var resetExpandShrink = function () {
 
+                var width = element.offsetWidth;
 
+                var height = element.offsetHeight;
 
+
 
+                expandChild.style.width = (width + 10) + 'px';
 
+                expandChild.style.height = (height + 10) + 'px';
 
+
 
+                expand.scrollLeft = width + 10;
 
+                expand.scrollTop = height + 10;
 
+
 
+                shrink.scrollLeft = width + 10;
 
+                shrink.scrollTop = height + 10;
 
+            };
 
+
 
+            var reset = function() {
 
+                // Check if element is hidden
 
+                if (initialHiddenCheck) {
 
+                    var invisible = element.offsetWidth === 0 && element.offsetHeight === 0;
 
+                    if (invisible) {
 
+                        // Check in next frame
 
+                        if (!lastAnimationFrame){
 
+                            lastAnimationFrame = requestAnimationFrame(function(){
 
+                                lastAnimationFrame = 0;
 
+
 
+                                reset();
 
+                            });
 
+                        }
 
+
 
+                        return;
 
+                    } else {
 
+                        // Stop checking
 
+                        initialHiddenCheck = false;
 
+                    }
 
+                }
 
+
 
+                resetExpandShrink();
 
+            };
 
+            element.resizeSensor.resetSensor = reset;
 
+
 
+            var onResized = function() {
 
+                rafId = 0;
 
+
 
+                if (!dirty) return;
 
+
 
+                lastWidth = size.width;
 
+                lastHeight = size.height;
 
+
 
+                if (element.resizedAttached) {
 
+                    element.resizedAttached.call(size);
 
+                }
 
+            };
 
+
 
+            var onScroll = function() {
 
+                size = getElementSize(element);
 
+                dirty = size.width !== lastWidth || size.height !== lastHeight;
 
+
 
+                if (dirty && !rafId) {
 
+                    rafId = requestAnimationFrame(onResized);
 
+                }
 
+
 
+                reset();
 
+            };
 
+
 
+            var addEvent = function(el, name, cb) {
 
+                if (el.attachEvent) {
 
+                    el.attachEvent('on' + name, cb);
 
+                } else {
 
+                    el.addEventListener(name, cb);
 
+                }
 
+            };
 
+
 
+            addEvent(expand, 'scroll', onScroll);
 
+            addEvent(shrink, 'scroll', onScroll);
 
+
 
+            // Fix for custom Elements
 
+            requestAnimationFrame(reset);
 
+        }
 
+
 
+        forEachElement(element, function(elem){
 
+            attachResizeEvent(elem, callback);
 
+        });
 
+
 
+        this.detach = function(ev) {
 
+            ResizeSensor.detach(element, ev);
 
+        };
 
+
 
+        this.reset = function() {
 
+            element.resizeSensor.resetSensor();
 
+        };
 
+    };
 
+
 
+    ResizeSensor.reset = function(element) {
 
+        forEachElement(element, function(elem){
 
+            elem.resizeSensor.resetSensor();
 
+        });
 
+    };
 
+
 
+    ResizeSensor.detach = function(element, ev) {
 
+        forEachElement(element, function(elem){
 
+            if (!elem) return;
 
+            if(elem.resizedAttached && typeof ev === "function"){
 
+                elem.resizedAttached.remove(ev);
 
+                if(elem.resizedAttached.length()) return;
 
+            }
 
+            if (elem.resizeSensor) {
 
+                if (elem.contains(elem.resizeSensor)) {
 
+                    elem.removeChild(elem.resizeSensor);
 
+                }
 
+                delete elem.resizeSensor;
 
+                delete elem.resizedAttached;
 
+            }
 
+        });
 
+    };
 
+
 
+    if (typeof MutationObserver !== "undefined") {
 
+        var observer = new MutationObserver(function (mutations) {
 
+            for (var i in mutations) {
 
+                if (mutations.hasOwnProperty(i)) {
 
+                    var items = mutations[i].addedNodes;
 
+                    for (var j = 0; j < items.length; j++) {
 
+                        if (items[j].resizeSensor) {
 
+                            ResizeSensor.reset(items[j]);
 
+                        }
 
+                    }
 
+                }
 
+            }
 
+        });
 
+
 
+        document.addEventListener("DOMContentLoaded", function (event) {
 
+            observer.observe(document.body, {
 
+                childList: true,
 
+                subtree: true,
 
+            });
 
+        });
 
+    }
 
+
 
+    return ResizeSensor;
 
+
 
+}));

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+ 21 - 0
assets/bmap.min.js


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assets/bulma.min.css


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+ 0 - 0
assets/echarts-gl.min.js


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+ 0 - 0
assets/echarts-liquidfill.min.js


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+ 0 - 0
assets/echarts-wordcloud.min.js


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+ 21 - 0
assets/echarts.min.js


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+ 664 - 0
assets/jquery-ui.css


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+ 5 - 0
assets/jquery-ui.min.js


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assets/jquery.min.js


+ 8 - 0
assets/main.js
مشاهده فایل 

@@ -0,0 +1,8 @@
 
+define(["require", "exports"], function (require, exports) {
 
+    "use strict";
 
+    Object.defineProperty(exports, "__esModule", { value: true });
 
+    function load_ipython_extension() {
 
+        console.log("pyecharts-assert extension has been loaded");
 
+    }
 
+    exports.load_ipython_extension = load_ipython_extension;
 
+});

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assets/maps/an1_hui1_an1_qing4.js


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assets/maps/an1_hui1_bang4_bu4.js


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assets/maps/an1_hui1_bo2_zhou1.js


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assets/maps/an1_hui1_chi2_zhou1.js


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assets/maps/an1_hui1_chu2_zhou1.js


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assets/maps/an1_hui1_fu4_yang2.js


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assets/maps/an1_hui1_he2_fei2.js


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assets/maps/an1_hui1_huai2_bei3.js


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assets/maps/an1_hui1_huai2_nan2.js


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assets/maps/an1_hui1_huang2_shan1.js


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assets/maps/an1_hui1_liu4_an1.js


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assets/maps/an1_hui1_ma3_an1_shan1.js


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assets/maps/an1_hui1_su4_zhou1.js


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assets/maps/an1_hui1_tong2_ling2.js


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+ 1 - 0
assets/maps/an1_hui1_wu2_hu2.js


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+ 1 - 0
assets/maps/an1_hui1_xuan1_cheng2.js


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+ 0 - 0
assets/maps/anhui.js


+ 2 - 0
assets/maps/aomen.js
مشاهده فایل 

@@ -0,0 +1,2 @@
 
+!function(e,o){"function"==typeof define&&define.amd?define(["exports","echarts"],o):"object"==typeof exports&&"string"!=typeof exports.nodeName?o(exports,require("echarts")):o({},e.echarts)}(this,function(e,o){var t=function(e){"undefined"!=typeof console&&console&&console.error&&console.error(e)}
 
+return o?o.registerMap?void o.registerMap("澳门",{type:"FeatureCollection",features:[{id:"820001",type:"Feature",geometry:{type:"MultiPolygon",coordinates:[["@@LADC^umZ@DONWE@DALBBF@H@DFBBTC"],["@@P@LC@AGM@OECMBABBTCD@DDH"]],encodeOffsets:[[[116285,22746]],[[116303,22746]]]},properties:{cp:[113.552965,22.207882],name:"花地玛堂区",childNum:2}},{id:"820002",type:"Feature",geometry:{type:"Polygon",coordinates:["@@MK@CA@AAGDEB@NVFJG"],encodeOffsets:[[116281,22734]]},properties:{cp:[113.549052,22.199175],name:"花王堂区",childNum:1}},{id:"820003",type:"Feature",geometry:{type:"Polygon",coordinates:["@@EGOB@DNLHE@C"],encodeOffsets:[[116285,22729]]},properties:{cp:[113.550252,22.193791],name:"望德堂区",childNum:1}},{id:"820004",type:"Feature",geometry:{type:"Polygon",coordinates:["@@ŸYMVAN@BFCBBDAFHDBBFDHIJJEFDPCHHlYJQ"],encodeOffsets:[[116313,22707]]},properties:{cp:[113.55374,22.188119],name:"大堂区",childNum:1}},{id:"820005",type:"Feature",geometry:{type:"Polygon",coordinates:["@@JICGAECACGEBAAEDBFNXB@"],encodeOffsets:[[116266,22728]]},properties:{cp:[113.54167,22.187778],name:"风顺堂区",childNum:1}},{id:"820006",type:"Feature",geometry:{type:"Polygon",coordinates:["@@ ZNWRquZCBCC@AEA@@ADCDCAACEAGBQ@INEL"],encodeOffsets:[[116265,22694]]},properties:{cp:[113.558783,22.154124],name:"嘉模堂区",childNum:1}},{id:"820007",type:"Feature",geometry:{type:"Polygon",coordinates:["@@MOIAIEI@@GE@AAUCBdCFIFR@HAFBBDDBDCBC@@FB@BDDDA\\M"],encodeOffsets:[[116316,22676]]},properties:{cp:[113.56925,22.136546],name:"路凼填海区",childNum:1}},{id:"820008",type:"Feature",geometry:{type:"Polygon",coordinates:["@@DKMMa_GC_COD@dVDBBF@@HJ@JFJBNPZK"],encodeOffsets:[[116329,22670]]},properties:{cp:[113.559954,22.124049],name:"圣方济各堂区",childNum:1}}],UTF8Encoding:!0}):void t("ECharts Map is not loaded"):void t("ECharts is not Loaded")})

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assets/maps/beijing.js


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assets/maps/china.js


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assets/maps/chongqing.js


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assets/maps/fu2_jian4_fu2_zhou1.js


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assets/maps/fu2_jian4_fu3_tian2.js


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assets/maps/fu2_jian4_long2_yan2.js


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assets/maps/fu2_jian4_nan2_ping2.js


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assets/maps/fu2_jian4_ning2_de2.js


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assets/maps/fu2_jian4_quan2_zhou1.js


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assets/maps/fu2_jian4_san1_ming2.js


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assets/maps/fu2_jian4_sha4_men2.js


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assets/maps/fu2_jian4_zhang1_zhou1.js


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assets/maps/fujian.js


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assets/maps/gan1_su4_bai2_yin2.js


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assets/maps/gan1_su4_ding4_xi1.js


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assets/maps/gan1_su4_jia1_yu4_guan1.js


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assets/maps/gan1_su4_jin1_chang1.js


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assets/maps/gan1_su4_jiu3_quan2.js


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assets/maps/gan1_su4_lan2_zhou1.js


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assets/maps/gan1_su4_lin2_xia4_hui2_zu2_zi4_zhi4_zhou1.js


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assets/maps/gan1_su4_long3_nan2.js


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assets/maps/gan1_su4_ping2_liang2.js


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assets/maps/gan1_su4_qing4_yang2.js


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assets/maps/gan1_su4_tian1_shui3.js


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assets/maps/gan1_su4_wu3_wei1.js


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assets/maps/gan1_su4_zhang1_ye4.js


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+ 0 - 0
assets/maps/gansu.js


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assets/maps/guang3_dong1_chao2_zhou1.js


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assets/maps/guang3_dong1_dong1_guan1.js


+ 2 - 0
assets/maps/guang3_dong1_dong1_sha1_qun2_dao3.js
مشاهده فایل 

@@ -0,0 +1,2 @@
 
+!function(e,o){"function"==typeof define&&define.amd?define(["exports","echarts"],o):"object"==typeof exports&&"string"!=typeof exports.nodeName?o(exports,require("echarts")):o({},e.echarts)}(this,function(e,o){var t=function(e){"undefined"!=typeof console&&console&&console.error&&console.error(e)}
 
+return o?o.registerMap?void o.registerMap("东沙群岛",{type:"FeatureCollection",features:[{type:"Feature",id:"442100",properties:{name:"东沙群岛",cp:[116.887613,20.617825],childNum:4},geometry:{type:"MultiPolygon",coordinates:[["@@N\\`ZrDVBrCf]\\mRmBqWY]OoFyZe^a\\SfA^"],["@@JVZNT@B@TSno\\cB_ES@AWCaAaR]dOfId"],["@@IBGBIBEBID@@AB@BB@@@BBA@BD@@DBDBB@B@B@DAFCFAD@B@DCB@B@@ADGBCA@E@"],["@@tJrHtC^BvGj[JK\\{R§CS]{Ycc_q]‹K{AuLUJSPCJBLFHNF„KŠNlRtn`‚C€MhQJ]BuDUCKCIBIHOFK@[AQAKDCJDHHBFFCN@HDF"]],encodeOffsets:[[[118726,21604]],[[118709,21486]],[[119538,21192]],[[119573,21271]]]}}],UTF8Encoding:!0}):void t("ECharts Map is not loaded"):void t("ECharts is not Loaded")})

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assets/maps/guang3_dong1_fo2_shan1.js


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assets/maps/guang3_dong1_guang3_zhou1.js


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assets/maps/guang3_dong1_he2_yuan2.js


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assets/maps/guang3_dong1_hui4_zhou1.js


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assets/maps/guang3_dong1_jiang1_men2.js


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assets/maps/guang3_dong1_jie1_yang2.js


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assets/maps/guang3_dong1_mao4_ming2.js


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assets/maps/guang3_dong1_mei2_zhou1.js


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assets/maps/guang3_dong1_qing1_yuan3.js


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برخی فایل ها در این مقایسه diff نمایش داده نمی شوند زیرا تعداد فایل ها بسیار زیاد است