From d253953af4780654b357687e324cc48c07c83479 Mon Sep 17 00:00:00 2001
From: ALINA_KURBANOVA <alinakurbanova2004@gmail.com>
Date: Thu, 13 Feb 2025 23:26:33 +0400
Subject: [PATCH] lab7 is done

---
 lab_7/lab_7.ipynb | 2598 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 2598 insertions(+)
 create mode 100644 lab_7/lab_7.ipynb

diff --git a/lab_7/lab_7.ipynb b/lab_7/lab_7.ipynb
new file mode 100644
index 00000000..7672d912
--- /dev/null
+++ b/lab_7/lab_7.ipynb
@@ -0,0 +1,2598 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Начало лабы №7"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Загрузка даанных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume',\n",
+      "       'SP_open', 'SP_high', 'SP_low', 'SP_close', 'SP_Ajclose', 'SP_volume',\n",
+      "       'DJ_open', 'DJ_high', 'DJ_low', 'DJ_close', 'DJ_Ajclose', 'DJ_volume',\n",
+      "       'EG_open', 'EG_high', 'EG_low', 'EG_close', 'EG_Ajclose', 'EG_volume',\n",
+      "       'EU_Price', 'EU_open', 'EU_high', 'EU_low', 'EU_Trend', 'OF_Price',\n",
+      "       'OF_Open', 'OF_High', 'OF_Low', 'OF_Volume', 'OF_Trend', 'OS_Price',\n",
+      "       'OS_Open', 'OS_High', 'OS_Low', 'OS_Trend', 'SF_Price', 'SF_Open',\n",
+      "       'SF_High', 'SF_Low', 'SF_Volume', 'SF_Trend', 'USB_Price', 'USB_Open',\n",
+      "       'USB_High', 'USB_Low', 'USB_Trend', 'PLT_Price', 'PLT_Open', 'PLT_High',\n",
+      "       'PLT_Low', 'PLT_Trend', 'PLD_Price', 'PLD_Open', 'PLD_High', 'PLD_Low',\n",
+      "       'PLD_Trend', 'RHO_PRICE', 'USDI_Price', 'USDI_Open', 'USDI_High',\n",
+      "       'USDI_Low', 'USDI_Volume', 'USDI_Trend', 'GDX_Open', 'GDX_High',\n",
+      "       'GDX_Low', 'GDX_Close', 'GDX_Adj Close', 'GDX_Volume', 'USO_Open',\n",
+      "       'USO_High', 'USO_Low', 'USO_Close', 'USO_Adj Close', 'USO_Volume'],\n",
+      "      dtype='object')\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Date",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Open",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "High",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Low",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Close",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Adj Close",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Volume",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "SP_open",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "SP_high",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "SP_low",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "SP_close",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "SP_Ajclose",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "SP_volume",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "DJ_open",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "DJ_high",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "DJ_low",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "DJ_close",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "DJ_Ajclose",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "DJ_volume",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "EG_open",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "EG_high",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "EG_low",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "EG_close",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "EG_Ajclose",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "EG_volume",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "EU_Price",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "EU_open",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "EU_high",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "EU_low",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "EU_Trend",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "OF_Price",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "OF_Open",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "OF_High",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "OF_Low",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "OF_Volume",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "OF_Trend",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "OS_Price",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "OS_Open",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "OS_High",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "OS_Low",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "OS_Trend",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "SF_Price",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "SF_Open",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "SF_High",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "SF_Low",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "SF_Volume",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "SF_Trend",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "USB_Price",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "USB_Open",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "USB_High",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "USB_Low",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "USB_Trend",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "PLT_Price",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PLT_Open",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PLT_High",
+         "rawType": "float64",
+         "type": "float"
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+        {
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+        {
+         "name": "PLD_Price",
+         "rawType": "float64",
+         "type": "float"
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+        {
+         "name": "PLD_Open",
+         "rawType": "float64",
+         "type": "float"
+        },
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+         "name": "PLD_High",
+         "rawType": "float64",
+         "type": "float"
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+         "name": "PLD_Trend",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "RHO_PRICE",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "USDI_Price",
+         "rawType": "float64",
+         "type": "float"
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+        {
+         "name": "USDI_Open",
+         "rawType": "float64",
+         "type": "float"
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+        {
+         "name": "USDI_High",
+         "rawType": "float64",
+         "type": "float"
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+         "name": "USDI_Low",
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+         "type": "float"
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+         "type": "float"
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+         "name": "GDX_Adj Close",
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+        {
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+         "107.71",
+         "107.15",
+         "108.17",
+         "106.16",
+         "145090",
+         "1",
+         "99.01",
+         "97.54",
+         "99.26",
+         "96.81",
+         "1",
+         "53148",
+         "53519",
+         "54184",
+         "52937",
+         "75950",
+         "0",
+         "1.97",
+         "1.97",
+         "1.97",
+         "1.97",
+         "1",
+         "1429.05",
+         "1434.4",
+         "1453.75",
+         "1417.65",
+         "0",
+         "635.9",
+         "625.7",
+         "641.5",
+         "623.8",
+         "1",
+         "1400",
+         "80.35",
+         "80.105",
+         "80.445",
+         "79.55",
+         "24140",
+         "1",
+         "53.150002",
+         "53.43",
+         "52.419998",
+         "52.959999",
+         "50.186852",
+         "8724300",
+         "37.669998",
+         "38.240002",
+         "37.52",
+         "38.110001",
+         "38.110001",
+         "10728000"
+        ]
+       ],
+       "shape": {
+        "columns": 81,
+        "rows": 5
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>SP_open</th>\n",
+       "      <th>SP_high</th>\n",
+       "      <th>SP_low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>GDX_Low</th>\n",
+       "      <th>GDX_Close</th>\n",
+       "      <th>GDX_Adj Close</th>\n",
+       "      <th>GDX_Volume</th>\n",
+       "      <th>USO_Open</th>\n",
+       "      <th>USO_High</th>\n",
+       "      <th>USO_Low</th>\n",
+       "      <th>USO_Close</th>\n",
+       "      <th>USO_Adj Close</th>\n",
+       "      <th>USO_Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2011-12-15</td>\n",
+       "      <td>154.740005</td>\n",
+       "      <td>154.949997</td>\n",
+       "      <td>151.710007</td>\n",
+       "      <td>152.330002</td>\n",
+       "      <td>152.330002</td>\n",
+       "      <td>21521900</td>\n",
+       "      <td>123.029999</td>\n",
+       "      <td>123.199997</td>\n",
+       "      <td>121.989998</td>\n",
+       "      <td>...</td>\n",
+       "      <td>51.570000</td>\n",
+       "      <td>51.680000</td>\n",
+       "      <td>48.973877</td>\n",
+       "      <td>20605600</td>\n",
+       "      <td>36.900002</td>\n",
+       "      <td>36.939999</td>\n",
+       "      <td>36.049999</td>\n",
+       "      <td>36.130001</td>\n",
+       "      <td>36.130001</td>\n",
+       "      <td>12616700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2011-12-16</td>\n",
+       "      <td>154.309998</td>\n",
+       "      <td>155.369995</td>\n",
+       "      <td>153.899994</td>\n",
+       "      <td>155.229996</td>\n",
+       "      <td>155.229996</td>\n",
+       "      <td>18124300</td>\n",
+       "      <td>122.230003</td>\n",
+       "      <td>122.949997</td>\n",
+       "      <td>121.300003</td>\n",
+       "      <td>...</td>\n",
+       "      <td>52.040001</td>\n",
+       "      <td>52.680000</td>\n",
+       "      <td>49.921513</td>\n",
+       "      <td>16285400</td>\n",
+       "      <td>36.180000</td>\n",
+       "      <td>36.500000</td>\n",
+       "      <td>35.730000</td>\n",
+       "      <td>36.270000</td>\n",
+       "      <td>36.270000</td>\n",
+       "      <td>12578800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2011-12-19</td>\n",
+       "      <td>155.479996</td>\n",
+       "      <td>155.860001</td>\n",
+       "      <td>154.360001</td>\n",
+       "      <td>154.869995</td>\n",
+       "      <td>154.869995</td>\n",
+       "      <td>12547200</td>\n",
+       "      <td>122.059998</td>\n",
+       "      <td>122.320000</td>\n",
+       "      <td>120.029999</td>\n",
+       "      <td>...</td>\n",
+       "      <td>51.029999</td>\n",
+       "      <td>51.169998</td>\n",
+       "      <td>48.490578</td>\n",
+       "      <td>15120200</td>\n",
+       "      <td>36.389999</td>\n",
+       "      <td>36.450001</td>\n",
+       "      <td>35.930000</td>\n",
+       "      <td>36.200001</td>\n",
+       "      <td>36.200001</td>\n",
+       "      <td>7418200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2011-12-20</td>\n",
+       "      <td>156.820007</td>\n",
+       "      <td>157.429993</td>\n",
+       "      <td>156.580002</td>\n",
+       "      <td>156.979996</td>\n",
+       "      <td>156.979996</td>\n",
+       "      <td>9136300</td>\n",
+       "      <td>122.180000</td>\n",
+       "      <td>124.139999</td>\n",
+       "      <td>120.370003</td>\n",
+       "      <td>...</td>\n",
+       "      <td>52.369999</td>\n",
+       "      <td>52.990002</td>\n",
+       "      <td>50.215282</td>\n",
+       "      <td>11644900</td>\n",
+       "      <td>37.299999</td>\n",
+       "      <td>37.610001</td>\n",
+       "      <td>37.220001</td>\n",
+       "      <td>37.560001</td>\n",
+       "      <td>37.560001</td>\n",
+       "      <td>10041600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2011-12-21</td>\n",
+       "      <td>156.979996</td>\n",
+       "      <td>157.529999</td>\n",
+       "      <td>156.130005</td>\n",
+       "      <td>157.160004</td>\n",
+       "      <td>157.160004</td>\n",
+       "      <td>11996100</td>\n",
+       "      <td>123.930000</td>\n",
+       "      <td>124.360001</td>\n",
+       "      <td>122.750000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>52.419998</td>\n",
+       "      <td>52.959999</td>\n",
+       "      <td>50.186852</td>\n",
+       "      <td>8724300</td>\n",
+       "      <td>37.669998</td>\n",
+       "      <td>38.240002</td>\n",
+       "      <td>37.520000</td>\n",
+       "      <td>38.110001</td>\n",
+       "      <td>38.110001</td>\n",
+       "      <td>10728000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 81 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Date        Open        High         Low       Close   Adj Close  \\\n",
+       "0  2011-12-15  154.740005  154.949997  151.710007  152.330002  152.330002   \n",
+       "1  2011-12-16  154.309998  155.369995  153.899994  155.229996  155.229996   \n",
+       "2  2011-12-19  155.479996  155.860001  154.360001  154.869995  154.869995   \n",
+       "3  2011-12-20  156.820007  157.429993  156.580002  156.979996  156.979996   \n",
+       "4  2011-12-21  156.979996  157.529999  156.130005  157.160004  157.160004   \n",
+       "\n",
+       "     Volume     SP_open     SP_high      SP_low  ...    GDX_Low  GDX_Close  \\\n",
+       "0  21521900  123.029999  123.199997  121.989998  ...  51.570000  51.680000   \n",
+       "1  18124300  122.230003  122.949997  121.300003  ...  52.040001  52.680000   \n",
+       "2  12547200  122.059998  122.320000  120.029999  ...  51.029999  51.169998   \n",
+       "3   9136300  122.180000  124.139999  120.370003  ...  52.369999  52.990002   \n",
+       "4  11996100  123.930000  124.360001  122.750000  ...  52.419998  52.959999   \n",
+       "\n",
+       "   GDX_Adj Close  GDX_Volume   USO_Open   USO_High    USO_Low  USO_Close  \\\n",
+       "0      48.973877    20605600  36.900002  36.939999  36.049999  36.130001   \n",
+       "1      49.921513    16285400  36.180000  36.500000  35.730000  36.270000   \n",
+       "2      48.490578    15120200  36.389999  36.450001  35.930000  36.200001   \n",
+       "3      50.215282    11644900  37.299999  37.610001  37.220001  37.560001   \n",
+       "4      50.186852     8724300  37.669998  38.240002  37.520000  38.110001   \n",
+       "\n",
+       "   USO_Adj Close  USO_Volume  \n",
+       "0      36.130001    12616700  \n",
+       "1      36.270000    12578800  \n",
+       "2      36.200001     7418200  \n",
+       "3      37.560001    10041600  \n",
+       "4      38.110001    10728000  \n",
+       "\n",
+       "[5 rows x 81 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df = pd.read_csv(\"..//static//csv//FINAL_USO.csv\")\n",
+    "print(df.columns)\n",
+    "display(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Создание лингвистических переменных**\n",
+    "\n",
+    "Входные переменные: OF_Price (цены на нефть) и SF_Price (цена на серебро) . \\\n",
+    "Выходная переменная: Adj Close (цена)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from skfuzzy import control as ctrl\n",
+    "\n",
+    "\n",
+    "# Инициализация лингвистических переменных\n",
+    "oil_price = ctrl.Antecedent(np.arange(df['OF_Price'].min(), df['OF_Price'].max(), 10), \"oil_price\")\n",
+    "silver_price = ctrl.Antecedent(np.arange(df['SF_Price'].min(), df['SF_Price'].max(), 1000), \"silver_price\")\n",
+    "adj_close = ctrl.Consequent(np.arange(df['Adj Close'].min(), df['Adj Close'].max(), 10), \"adj_close\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Формирование нечетких переменных для лингвистических переменных и их визуализация**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\skfuzzy\\control\\fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "  fig.show()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import skfuzzy as fuzz\n",
+    "\n",
+    "oil_price['low'] = fuzz.zmf(oil_price.universe, 40, 50)\n",
+    "oil_price['average'] = fuzz.trapmf(oil_price.universe, [60, 70, 80, 90])\n",
+    "oil_price['high'] = fuzz.smf(oil_price.universe, 100, 120)\n",
+    "\n",
+    "silver_price['low'] = fuzz.zmf(silver_price.universe, 35000, 45000)\n",
+    "silver_price['average'] = fuzz.trapmf(silver_price.universe, [35000, 45000, 50000, 60000])\n",
+    "silver_price['high'] = fuzz.smf(silver_price.universe, 50000, 60000)\n",
+    "\n",
+    "adj_close['low'] = fuzz.zmf(adj_close.universe,110, 120)\n",
+    "adj_close['average'] = fuzz.trapmf(adj_close.universe, [135, 145, 155, 165])\n",
+    "adj_close['high'] = fuzz.smf(adj_close.universe, 160, 170)\n",
+    "\n",
+    "oil_price.view()\n",
+    "silver_price.view()\n",
+    "adj_close.view()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Формирование и визуализация базы нечетких правил**\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 640x480 with 1 Axes>, <Axes: >)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import skfuzzy as fuzz\n",
+    "from skfuzzy import control as ctrl\n",
+    "# Нечеткие правила\n",
+    "rule1 = ctrl.Rule(silver_price[\"low\"] & oil_price[\"low\"], adj_close[\"low\"])\n",
+    "rule2 = ctrl.Rule(silver_price[\"low\"] & oil_price[\"average\"], adj_close[\"low\"])\n",
+    "rule3 = ctrl.Rule(silver_price[\"low\"] & oil_price[\"high\"], adj_close[\"low\"])\n",
+    "rule4 = ctrl.Rule(silver_price[\"average\"] & oil_price[\"low\"], adj_close[\"low\"])\n",
+    "rule5 = ctrl.Rule(silver_price[\"average\"] & oil_price[\"average\"], adj_close[\"low\"])\n",
+    "rule6 = ctrl.Rule(silver_price[\"average\"] & oil_price[\"high\"], adj_close[\"low\"])\n",
+    "rule7 = ctrl.Rule(silver_price[\"high\"] & oil_price[\"low\"], adj_close[\"average\"])\n",
+    "rule8 = ctrl.Rule(silver_price[\"high\"] & oil_price[\"average\"], adj_close[\"high\"])\n",
+    "rule9 = ctrl.Rule(silver_price[\"high\"] & oil_price[\"high\"], adj_close[\"high\"])\n",
+    "rule1.view()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Создание нечеткой системы и добавление нечетких правил в базу знаний нечеткой системы**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "price_ctrl = ctrl.ControlSystem(\n",
+    "    [\n",
+    "        rule1,\n",
+    "        rule2,\n",
+    "        rule3,\n",
+    "        rule4,\n",
+    "        rule5,\n",
+    "        rule6,\n",
+    "        rule7,\n",
+    "        rule8,\n",
+    "        rule9,\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "# Создание симулятора нечеткой системы\n",
+    "price_sim = ctrl.ControlSystemSimulation(price_ctrl)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Пример расчета выходной переменной adj_close на основе входных переменных silver_price и oil_price** \\\n",
+    "Система также формирует подробный журнал выполнения процесса нечеткого логического вывода"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=============\n",
+      " Antecedents \n",
+      "=============\n",
+      "Antecedent: silver_price            = 60000\n",
+      "  - low                             : 0.0\n",
+      "  - average                         : 0.014110000000000011\n",
+      "  - high                            : 0.99765774\n",
+      "Antecedent: oil_price               = 30\n",
+      "  - low                             : 1.0\n",
+      "  - average                         : 0.0\n",
+      "  - high                            : 0.0\n",
+      "\n",
+      "=======\n",
+      " Rules \n",
+      "=======\n",
+      "RULE #0:\n",
+      "  IF silver_price[low] AND oil_price[low] THEN adj_close[low]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - silver_price[low]                                      : 0.0\n",
+      "  - oil_price[low]                                         : 1.0\n",
+      "                      silver_price[low] AND oil_price[low] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                            adj_close[low] : 0.0\n",
+      "\n",
+      "RULE #1:\n",
+      "  IF silver_price[low] AND oil_price[average] THEN adj_close[low]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - silver_price[low]                                      : 0.0\n",
+      "  - oil_price[average]                                     : 0.0\n",
+      "                  silver_price[low] AND oil_price[average] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                            adj_close[low] : 0.0\n",
+      "\n",
+      "RULE #2:\n",
+      "  IF silver_price[low] AND oil_price[high] THEN adj_close[low]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - silver_price[low]                                      : 0.0\n",
+      "  - oil_price[high]                                        : 0.0\n",
+      "                     silver_price[low] AND oil_price[high] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                            adj_close[low] : 0.0\n",
+      "\n",
+      "RULE #3:\n",
+      "  IF silver_price[average] AND oil_price[low] THEN adj_close[low]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - silver_price[average]                                  : 0.014110000000000011\n",
+      "  - oil_price[low]                                         : 1.0\n",
+      "                  silver_price[average] AND oil_price[low] = 0.014110000000000011\n",
+      "  Activation (THEN-clause):\n",
+      "                                            adj_close[low] : 0.014110000000000011\n",
+      "\n",
+      "RULE #4:\n",
+      "  IF silver_price[average] AND oil_price[average] THEN adj_close[low]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - silver_price[average]                                  : 0.014110000000000011\n",
+      "  - oil_price[average]                                     : 0.0\n",
+      "              silver_price[average] AND oil_price[average] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                            adj_close[low] : 0.0\n",
+      "\n",
+      "RULE #5:\n",
+      "  IF silver_price[average] AND oil_price[high] THEN adj_close[low]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - silver_price[average]                                  : 0.014110000000000011\n",
+      "  - oil_price[high]                                        : 0.0\n",
+      "                 silver_price[average] AND oil_price[high] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                            adj_close[low] : 0.0\n",
+      "\n",
+      "RULE #6:\n",
+      "  IF silver_price[high] AND oil_price[low] THEN adj_close[average]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - silver_price[high]                                     : 0.99765774\n",
+      "  - oil_price[low]                                         : 1.0\n",
+      "                     silver_price[high] AND oil_price[low] = 0.99765774\n",
+      "  Activation (THEN-clause):\n",
+      "                                        adj_close[average] : 0.99765774\n",
+      "\n",
+      "RULE #7:\n",
+      "  IF silver_price[high] AND oil_price[average] THEN adj_close[high]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - silver_price[high]                                     : 0.99765774\n",
+      "  - oil_price[average]                                     : 0.0\n",
+      "                 silver_price[high] AND oil_price[average] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                           adj_close[high] : 0.0\n",
+      "\n",
+      "RULE #8:\n",
+      "  IF silver_price[high] AND oil_price[high] THEN adj_close[high]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - silver_price[high]                                     : 0.99765774\n",
+      "  - oil_price[high]                                        : 0.0\n",
+      "                    silver_price[high] AND oil_price[high] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                           adj_close[high] : 0.0\n",
+      "\n",
+      "\n",
+      "==============================\n",
+      " Intermediaries and Conquests \n",
+      "==============================\n",
+      "Consequent: adj_close                = 149.4518344511302\n",
+      "  low:\n",
+      "    Accumulate using accumulation_max : 0.014110000000000011\n",
+      "  average:\n",
+      "    Accumulate using accumulation_max : 0.99765774\n",
+      "  high:\n",
+      "    Accumulate using accumulation_max : 0.0\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(149.4518344511302)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "price_sim.input['silver_price'] = 60000\n",
+    "price_sim.input['oil_price'] = 30\n",
+    "price_sim.compute()\n",
+    "\n",
+    "price_sim.print_state()\n",
+    "\n",
+    "price_sim.output[\"adj_close\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Визуализация функции принадлежности для выходной переменной adj_close** \\\n",
+    "Функция получена в процессе аккумуляции и используется для дефаззификации значения выходной переменной influx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "adj_close.view(sim=price_sim)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Функция для автоматизации вычисления целевой переменной Y на основе вектора признаков X**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fuzzy_pred(row):\n",
+    "    price_sim.input[\"silver_price\"] = row[\"SF_Price\"]\n",
+    "    price_sim.input[\"oil_price\"] = row[\"OF_Price\"]\n",
+    "    price_sim.compute()\n",
+    "    return price_sim.output[\"adj_close\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Создадим выборки**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер обучающей выборки: 1030\n",
+      "Размер контрольной выборки: 344\n",
+      "Размер тестовой выборки: 344\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from imblearn.over_sampling import RandomOverSampler\n",
+    "df=pd.read_csv(\"..//static//csv//FINAL_USO.csv\")\n",
+    "# Разделение данных на обучающую и временную выборки\n",
+    "train_df, temp_df = train_test_split(df, test_size=0.4, random_state=42)\n",
+    "\n",
+    "# Разделение остатка на контрольную и тестовую выборки\n",
+    "val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42)\n",
+    "\n",
+    "# Проверка размеров выборок\n",
+    "print(\"Размер обучающей выборки:\", len(train_df))\n",
+    "print(\"Размер контрольной выборки:\", len(val_df))\n",
+    "print(\"Размер тестовой выборки:\", len(test_df))\n",
+    "\n",
+    "# Сохранение выборок в файлы\n",
+    "train_df.to_csv(\"..//static//csv//train_data.csv\", index=False)\n",
+    "val_df.to_csv(\"..//static//csv//val_data.csv\", index=False)\n",
+    "test_df.to_csv(\"..//static//csv//test_data.csv\", index=False)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Тестирование нечеткой системы на обучающей выборке"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Adj Close",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Adj_Pred",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "ce53009d-e81b-4031-8fd4-8e9ffdb72ab2",
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+         "109.43908430577174"
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+         "114.099998",
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+         "122.370003",
+         "109.13251875117075"
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+        [
+         "5",
+         "110.739998",
+         "110.37579149852452"
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+        [
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+         "109.22311196232465"
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+         "108.529999",
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+         "117.82874697686302"
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+         "152.619995",
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+         "116.720001",
+         "110.4578258996147"
+        ],
+        [
+         "12",
+         "133.919998",
+         "110.30550326427033"
+        ],
+        [
+         "13",
+         "124.389999",
+         "109.3296830096444"
+        ],
+        [
+         "14",
+         "124.230003",
+         "110.3014822655644"
+        ]
+       ],
+       "shape": {
+        "columns": 2,
+        "rows": 15
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Adj_Pred</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>168.000000</td>\n",
+       "      <td>132.711304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>112.570000</td>\n",
+       "      <td>109.439084</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>152.619995</td>\n",
+       "      <td>128.709309</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>114.099998</td>\n",
+       "      <td>108.918805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>122.370003</td>\n",
+       "      <td>109.132519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>110.739998</td>\n",
+       "      <td>110.375791</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>120.339996</td>\n",
+       "      <td>109.223112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>108.529999</td>\n",
+       "      <td>110.299032</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>155.990005</td>\n",
+       "      <td>117.828747</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>152.619995</td>\n",
+       "      <td>121.754787</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>114.690002</td>\n",
+       "      <td>108.616706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>116.720001</td>\n",
+       "      <td>110.457826</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>133.919998</td>\n",
+       "      <td>110.305503</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>124.389999</td>\n",
+       "      <td>109.329683</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>124.230003</td>\n",
+       "      <td>110.301482</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Adj Close    Adj_Pred\n",
+       "0   168.000000  132.711304\n",
+       "1   112.570000  109.439084\n",
+       "2   152.619995  128.709309\n",
+       "3   114.099998  108.918805\n",
+       "4   122.370003  109.132519\n",
+       "5   110.739998  110.375791\n",
+       "6   120.339996  109.223112\n",
+       "7   108.529999  110.299032\n",
+       "8   155.990005  117.828747\n",
+       "9   152.619995  121.754787\n",
+       "10  114.690002  108.616706\n",
+       "11  116.720001  110.457826\n",
+       "12  133.919998  110.305503\n",
+       "13  124.389999  109.329683\n",
+       "14  124.230003  110.301482"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "train_df = pd.read_csv(\"..//static//csv//train_data.csv\")\n",
+    "\n",
+    "result_train = train_df.copy()\n",
+    "\n",
+    "\n",
+    "result_train[\"Adj_Pred\"] = result_train.apply(fuzzy_pred, axis=1)\n",
+    "selected_cm=result_train[['Adj Close','Adj_Pred']]\n",
+    "selected_cm.head(15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Тестирование нечеткой системы на тестовой выборке"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Adj Close",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Adj_Pred",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "eb47032e-d776-46e0-9af1-3f22b5b808e6",
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+         "5",
+         "118.970001",
+         "109.81615450245467"
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+        [
+         "6",
+         "126.68",
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+         "117.519997",
+         "109.19862371377226"
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+        [
+         "8",
+         "126.730003",
+         "108.93870203425448"
+        ],
+        [
+         "9",
+         "120.309998",
+         "109.18489831395458"
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+        [
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+         "114.419998",
+         "108.7414478488308"
+        ],
+        [
+         "11",
+         "124.540001",
+         "108.94434696222575"
+        ],
+        [
+         "12",
+         "115.43",
+         "110.29943772801515"
+        ],
+        [
+         "13",
+         "118.220001",
+         "108.95159017449191"
+        ],
+        [
+         "14",
+         "121.050003",
+         "110.49406147820707"
+        ],
+        [
+         "15",
+         "106.220001",
+         "109.94038913171998"
+        ],
+        [
+         "16",
+         "109.139999",
+         "110.39131584127603"
+        ],
+        [
+         "17",
+         "112.239998",
+         "109.46808244359308"
+        ],
+        [
+         "18",
+         "122.879997",
+         "110.1342145340219"
+        ],
+        [
+         "19",
+         "117.290001",
+         "110.44927043503645"
+        ],
+        [
+         "20",
+         "127.400002",
+         "109.44414968857204"
+        ],
+        [
+         "21",
+         "171.020004",
+         "137.39971068213094"
+        ],
+        [
+         "22",
+         "118.120003",
+         "109.79565498750279"
+        ],
+        [
+         "23",
+         "119.43",
+         "109.75995043349762"
+        ],
+        [
+         "24",
+         "115.800003",
+         "108.87237507139429"
+        ]
+       ],
+       "shape": {
+        "columns": 2,
+        "rows": 25
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Adj_Pred</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>160.539993</td>\n",
+       "      <td>128.238594</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>121.610001</td>\n",
+       "      <td>110.497064</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>126.160004</td>\n",
+       "      <td>110.071318</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>160.990005</td>\n",
+       "      <td>140.910239</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>173.610001</td>\n",
+       "      <td>166.750349</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>118.970001</td>\n",
+       "      <td>109.816155</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>126.680000</td>\n",
+       "      <td>110.114797</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>117.519997</td>\n",
+       "      <td>109.198624</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>126.730003</td>\n",
+       "      <td>108.938702</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>120.309998</td>\n",
+       "      <td>109.184898</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>114.419998</td>\n",
+       "      <td>108.741448</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>124.540001</td>\n",
+       "      <td>108.944347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>115.430000</td>\n",
+       "      <td>110.299438</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>118.220001</td>\n",
+       "      <td>108.951590</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>121.050003</td>\n",
+       "      <td>110.494061</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>106.220001</td>\n",
+       "      <td>109.940389</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>109.139999</td>\n",
+       "      <td>110.391316</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>112.239998</td>\n",
+       "      <td>109.468082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>122.879997</td>\n",
+       "      <td>110.134215</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>117.290001</td>\n",
+       "      <td>110.449270</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>127.400002</td>\n",
+       "      <td>109.444150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>171.020004</td>\n",
+       "      <td>137.399711</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>118.120003</td>\n",
+       "      <td>109.795655</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>119.430000</td>\n",
+       "      <td>109.759950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>115.800003</td>\n",
+       "      <td>108.872375</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Adj Close    Adj_Pred\n",
+       "0   160.539993  128.238594\n",
+       "1   121.610001  110.497064\n",
+       "2   126.160004  110.071318\n",
+       "3   160.990005  140.910239\n",
+       "4   173.610001  166.750349\n",
+       "5   118.970001  109.816155\n",
+       "6   126.680000  110.114797\n",
+       "7   117.519997  109.198624\n",
+       "8   126.730003  108.938702\n",
+       "9   120.309998  109.184898\n",
+       "10  114.419998  108.741448\n",
+       "11  124.540001  108.944347\n",
+       "12  115.430000  110.299438\n",
+       "13  118.220001  108.951590\n",
+       "14  121.050003  110.494061\n",
+       "15  106.220001  109.940389\n",
+       "16  109.139999  110.391316\n",
+       "17  112.239998  109.468082\n",
+       "18  122.879997  110.134215\n",
+       "19  117.290001  110.449270\n",
+       "20  127.400002  109.444150\n",
+       "21  171.020004  137.399711\n",
+       "22  118.120003  109.795655\n",
+       "23  119.430000  109.759950\n",
+       "24  115.800003  108.872375"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "test_df=pd.read_csv(\"..//static//csv//test_data.csv\")\n",
+    "result_test = test_df.copy()\n",
+    "\n",
+    "result_test[\"Adj_Pred\"] = result_test.apply(fuzzy_pred, axis=1)\n",
+    "\n",
+    "selected_cm=result_test[['Adj Close','Adj_Pred']]\n",
+    "selected_cm.head(25)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Тестирование нечёткой системы на контрольной выборке"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Adj Close",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Adj_Pred",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "9dfb278b-651d-4efb-bce4-b47c6d430ea3",
+       "rows": [
+        [
+         "0",
+         "117.589996",
+         "110.0972480764752"
+        ],
+        [
+         "1",
+         "121.650002",
+         "110.27138888942741"
+        ],
+        [
+         "2",
+         "166.339996",
+         "142.87618000181152"
+        ],
+        [
+         "3",
+         "116.309998",
+         "108.74134927739742"
+        ],
+        [
+         "4",
+         "115.199997",
+         "110.496040723879"
+        ],
+        [
+         "5",
+         "126.940002",
+         "108.68012088172193"
+        ],
+        [
+         "6",
+         "127.480003",
+         "109.79270354585232"
+        ],
+        [
+         "7",
+         "120.779999",
+         "110.36466342894626"
+        ],
+        [
+         "8",
+         "151.619995",
+         "128.58449174345947"
+        ],
+        [
+         "9",
+         "118.290001",
+         "109.61805520760014"
+        ],
+        [
+         "10",
+         "122.860001",
+         "109.20084844724495"
+        ],
+        [
+         "11",
+         "118.360001",
+         "108.6201610852397"
+        ],
+        [
+         "12",
+         "123.32",
+         "109.6274088552575"
+        ],
+        [
+         "13",
+         "120.650002",
+         "110.36287346546014"
+        ],
+        [
+         "14",
+         "161.509995",
+         "134.42592836075045"
+        ],
+        [
+         "15",
+         "120.589996",
+         "110.36802022156732"
+        ],
+        [
+         "16",
+         "120.959999",
+         "109.8296547970471"
+        ],
+        [
+         "17",
+         "115.989998",
+         "108.7058996991467"
+        ],
+        [
+         "18",
+         "120.989998",
+         "109.89722722510368"
+        ],
+        [
+         "19",
+         "168.78999299999995",
+         "166.73290406408321"
+        ],
+        [
+         "20",
+         "114.290001",
+         "110.45579915545396"
+        ],
+        [
+         "21",
+         "114.209999",
+         "110.44521958602306"
+        ],
+        [
+         "22",
+         "115.050003",
+         "108.35584788951505"
+        ],
+        [
+         "23",
+         "118.860001",
+         "109.21069367359827"
+        ],
+        [
+         "24",
+         "120.050003",
+         "109.57832281769565"
+        ]
+       ],
+       "shape": {
+        "columns": 2,
+        "rows": 25
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Adj_Pred</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>117.589996</td>\n",
+       "      <td>110.097248</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>121.650002</td>\n",
+       "      <td>110.271389</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>166.339996</td>\n",
+       "      <td>142.876180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>116.309998</td>\n",
+       "      <td>108.741349</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>115.199997</td>\n",
+       "      <td>110.496041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>126.940002</td>\n",
+       "      <td>108.680121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>127.480003</td>\n",
+       "      <td>109.792704</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>120.779999</td>\n",
+       "      <td>110.364663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>151.619995</td>\n",
+       "      <td>128.584492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>118.290001</td>\n",
+       "      <td>109.618055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>122.860001</td>\n",
+       "      <td>109.200848</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>118.360001</td>\n",
+       "      <td>108.620161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>123.320000</td>\n",
+       "      <td>109.627409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>120.650002</td>\n",
+       "      <td>110.362873</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>161.509995</td>\n",
+       "      <td>134.425928</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>120.589996</td>\n",
+       "      <td>110.368020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>120.959999</td>\n",
+       "      <td>109.829655</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>115.989998</td>\n",
+       "      <td>108.705900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>120.989998</td>\n",
+       "      <td>109.897227</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>168.789993</td>\n",
+       "      <td>166.732904</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>114.290001</td>\n",
+       "      <td>110.455799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>114.209999</td>\n",
+       "      <td>110.445220</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>115.050003</td>\n",
+       "      <td>108.355848</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>118.860001</td>\n",
+       "      <td>109.210694</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>120.050003</td>\n",
+       "      <td>109.578323</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Adj Close    Adj_Pred\n",
+       "0   117.589996  110.097248\n",
+       "1   121.650002  110.271389\n",
+       "2   166.339996  142.876180\n",
+       "3   116.309998  108.741349\n",
+       "4   115.199997  110.496041\n",
+       "5   126.940002  108.680121\n",
+       "6   127.480003  109.792704\n",
+       "7   120.779999  110.364663\n",
+       "8   151.619995  128.584492\n",
+       "9   118.290001  109.618055\n",
+       "10  122.860001  109.200848\n",
+       "11  118.360001  108.620161\n",
+       "12  123.320000  109.627409\n",
+       "13  120.650002  110.362873\n",
+       "14  161.509995  134.425928\n",
+       "15  120.589996  110.368020\n",
+       "16  120.959999  109.829655\n",
+       "17  115.989998  108.705900\n",
+       "18  120.989998  109.897227\n",
+       "19  168.789993  166.732904\n",
+       "20  114.290001  110.455799\n",
+       "21  114.209999  110.445220\n",
+       "22  115.050003  108.355848\n",
+       "23  118.860001  109.210694\n",
+       "24  120.050003  109.578323"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "val_df=pd.read_csv(\"..//static//csv//val_data.csv\")\n",
+    "result_val = val_df.copy()\n",
+    "\n",
+    "result_val[\"Adj_Pred\"] = result_val.apply(fuzzy_pred, axis=1)\n",
+    "\n",
+    "selected_cm=result_val[['Adj Close','Adj_Pred']]\n",
+    "selected_cm.head(25)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Оценка результатов на основе метрик для задачи регрессии"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'RMSE_train': 15.892710880956223,\n",
+       " 'RMSE_test': 15.519716842963696,\n",
+       " 'RMAE_test': 3.6146976330239697,\n",
+       " 'R2_test': 0.16348948741550218}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import math\n",
+    "from sklearn import metrics\n",
+    "\n",
+    "\n",
+    "rmetrics = {}\n",
+    "rmetrics[\"RMSE_train\"] = math.sqrt(\n",
+    "    metrics.mean_squared_error(result_train[\"Adj Close\"], result_train[\"Adj_Pred\"])\n",
+    ")\n",
+    "rmetrics[\"RMSE_test\"] = math.sqrt(\n",
+    "    metrics.mean_squared_error(result_test[\"Adj Close\"], result_test[\"Adj_Pred\"])\n",
+    ")\n",
+    "rmetrics[\"RMAE_test\"] = math.sqrt(\n",
+    "    metrics.mean_absolute_error(result_test[\"Adj Close\"], result_test[\"Adj_Pred\"])\n",
+    ")\n",
+    "rmetrics[\"R2_test\"] = metrics.r2_score(\n",
+    "    result_test[\"Adj Close\"], result_test[\"Adj_Pred\"]\n",
+    ")\n",
+    "\n",
+    "rmetrics"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}