320 lines
94 KiB
Plaintext
320 lines
94 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 125,
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"id": "366a3913",
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"metadata": {
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"pycharm": {
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"is_executing": true
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}
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},
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"outputs": [],
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"source": [
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"import ipywidgets as widgets\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from sklearn.linear_model import LinearRegression\n",
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"from sklearn.preprocessing import PolynomialFeatures\n",
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"\n",
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"%matplotlib inline\n",
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"%reload_ext autoreload\n",
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"%autoreload 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 126,
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"id": "b76f2b19",
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"metadata": {},
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"outputs": [],
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"source": [
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"data = pd.read_csv('data6.tsv',sep='\\t',names=['x','y'])\n",
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"data = data.sort_values(by=['x'])\n",
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"# data_x = pd.DataFrame(data['x'])\n",
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"# data_y = pd.DataFrame(data['y'])\n",
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"# data_x = data['x'].array.reshape(-1,1)\n",
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"# data_y = data['y'].array.reshape(-1,1)\n",
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"data_x = data['x']\n",
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"data_y = data['y']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 127,
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"id": "650769e3",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.collections.PathCollection at 0x7f7a01580610>"
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]
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},
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"execution_count": 127,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAjIAAAGdCAYAAAAIbpn/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAryElEQVR4nO3df3DU9Z3H8dcmkOXnJvIjv9jwQ/FAyg8L9jDTi4WSEpi0jQ3coTJKFeyBwSbgIZObitW5KQzMKDhV6E1PYaanVnDRE0Qm8rslBYzmBKwZddAEkk08vexGhCRsPvfHXrZZE8lussnuN3k+ZnbIfr7v3Xw+fJPsa74/Ph+bMcYIAADAguKi3QEAAICuIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLGhDtDvSUlpYWVVdXa/jw4bLZbNHuDgAACIExRg0NDUpPT1dcXOfHW/pskKmurlZGRka0uwEAALqgqqpKTqez07o+G2SGDx8uyf8f4XA4otwbAAAQCq/Xq4yMjMDneGf6bJBpPZ3kcDgIMgAAWEyol4VwsS8AALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALCsPjshHgAAiDyfTzpxQqqpkdLSpKwsKT4+ev0hyAAAgJC4XFJhoXTx4t/anE5p2zYpPz86feLUEgAA6JTLJS1eHBxiJOnSJX+7yxWdfhFkAADAdfl8/iMxxrTf1tpWVOSv620EGQAAcF0nTrQ/EtOWMVJVlb+utxFkAADAddXURLYukroVZDZt2iSbzaaioqJA29WrV1VQUKCRI0dq2LBhWrRokWpra4NeV1lZqdzcXA0ZMkTJyclat26drl27FlRz9OhRzZw5U3a7XRMnTtTOnTu701UAANBFaWmRrYukLgeZM2fO6He/+52mT58e1L5mzRq98cYb2r17t44dO6bq6mrlt7mU2efzKTc3V01NTTp58qR27dqlnTt3asOGDYGaCxcuKDc3V3PnzlV5ebmKioq0YsUKHTx4sKvdBQAAXZSV5b87yWbreLvNJmVk+Ot6nemChoYGc/PNN5uSkhLzgx/8wBQWFhpjjKmvrzcDBw40u3fvDtT+9a9/NZJMaWmpMcaYN99808TFxRm32x2o2b59u3E4HKaxsdEYY8yjjz5qvvOd7wR9zyVLlpicnJyQ++jxeIwk4/F4ujJEAADQxquvGmOz+R/+q2L8j9a2V1+NzPcJ9/O7S0dkCgoKlJubq+zs7KD2srIyNTc3B7VPnjxZY8eOVWlpqSSptLRU06ZNU0pKSqAmJydHXq9X58+fD9R8871zcnIC79GRxsZGeb3eoAcAAIiM/Hxpzx5pzJjgdqfT3x6teWTCnhDv5Zdf1rvvvqszZ8602+Z2u5WQkKCkpKSg9pSUFLnd7kBN2xDTur112/VqvF6vrly5osGDB7f73hs3btQTTzwR7nAAAECI8vOlvDwLz+xbVVWlwsJClZSUaNCgQT3Vpy4pLi7W2rVrA8+9Xq8yMjKi2CMAAPqe+Hhpzpxo9+Jvwjq1VFZWprq6Os2cOVMDBgzQgAEDdOzYMT3zzDMaMGCAUlJS1NTUpPr6+qDX1dbWKjU1VZKUmpra7i6m1ued1Tgcjg6PxkiS3W6Xw+EIegAAgL4trCAzb948nT17VuXl5YHHbbfdpqVLlwa+HjhwoA4dOhR4TUVFhSorK5WZmSlJyszM1NmzZ1VXVxeoKSkpkcPh0JQpUwI1bd+jtab1PQAAAKQwTy0NHz5cU6dODWobOnSoRo4cGWhfvny51q5dqxEjRsjhcOjhhx9WZmambr/9dknS/PnzNWXKFN17773avHmz3G63fvWrX6mgoEB2u12StHLlSv32t7/Vo48+qgceeECHDx/WK6+8ov3790dizAAAoI+I+OrXTz/9tOLi4rRo0SI1NjYqJydHzz33XGB7fHy89u3bp1WrVikzM1NDhw7VsmXL9OSTTwZqJkyYoP3792vNmjXatm2bnE6nfv/73ysnJyfS3QUAABZmM6ajJaCsz+v1KjExUR6Ph+tlAACwiHA/vyN+RAYAAHSfzxdbtznHKoIMAAAxxuWSCguDV5x2OqVt26I38VysYvVrAABiiMslLV4cHGIk6dIlf7vLFZ1+xSqCDAAAMcLn8x+J6ejq1da2oiJ/HfwIMgAAxIgTJ9ofiWnLGKmqyl8HP4IMAAAxoqYmsnX9AUEGAIAYkZYW2br+gCADAECMyMry351ks3W83WaTMjL8dfAjyAAAECPi4/23WEvtw0zr861bmU+mLYIMAAAxJD9f2rNHGjMmuN3p9Lczj0wwJsQDACDG5OdLeXnRm9nXSrMKE2QAAIhB8fHSnDm9/32tNqswp5YAAIAka84qTJABAACWnVWYIAMAACw7qzBBBgAAWHZWYYIMAACw7KzCBBkAAGDZWYUJMgAAwLKzChNkAACAJGvOKsyEeAAAICDaswqHiyADAACCRGtW4a7g1BIAALAsggwAALAsTi0BANAFVlohui8jyAAAECarrRDdl3FqCQCAMFhxhei+jCADAECIrLpCdF9GkAEAIERWXSG6LyPIAAAQIquuEN2XEWQAAAiRVVeI7ssIMgAAhMiqK0T3ZQQZAABCZNUVovsyggwAAGGw4grRfRkT4gEAECarrRDdlxFkAADoAiutEN2XEWQAAAgT6yzFDoIMAABhYJ2l2MLFvgAAhKi31lny+aSjR6WXXvL/y5IH344gAwBACHprnSWXSxo/Xpo7V7rnHv+/48ezGOW3IcgAABCC3lhniZW1w0eQAQAgBD29zhIra3cNQQYAgBD09DpLrKzdNQQZAABC0NPrLLGydtcQZAAACEFPr7PEytpdQ5ABACBEPbnOEitrdw0T4gEAEIaeWmep9YjP4sX+0NL2ol9W1v52BBkAAMLUU+sstR7x6Wjm4K1bmTm4IwQZAABiCCtrh4cgAwBAjGFl7dBxsS8AALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALCssILM9u3bNX36dDkcDjkcDmVmZurAgQOB7VevXlVBQYFGjhypYcOGadGiRaqtrQ16j8rKSuXm5mrIkCFKTk7WunXrdO3ataCao0ePaubMmbLb7Zo4caJ27tzZ9RECAIA+K6wg43Q6tWnTJpWVlemdd97RD3/4Q+Xl5en8+fOSpDVr1uiNN97Q7t27dezYMVVXVys/Pz/wep/Pp9zcXDU1NenkyZPatWuXdu7cqQ0bNgRqLly4oNzcXM2dO1fl5eUqKirSihUrdPDgwQgNGQAA9BU2Y4zpzhuMGDFCW7Zs0eLFizV69Gi9+OKLWrx4sSTpww8/1C233KLS0lLdfvvtOnDggH784x+rurpaKSkpkqQdO3Zo/fr1+vzzz5WQkKD169dr//79OnfuXOB73HXXXaqvr9dbb70Vcr+8Xq8SExPl8XjkcDi6M0QAANBLwv387vI1Mj6fTy+//LIuX76szMxMlZWVqbm5WdnZ2YGayZMna+zYsSotLZUklZaWatq0aYEQI0k5OTnyer2BozqlpaVB79Fa0/oe36axsVFerzfoAQAA+rawg8zZs2c1bNgw2e12rVy5Unv37tWUKVPkdruVkJCgpKSkoPqUlBS53W5JktvtDgoxrdtbt12vxuv16sqVK9/ar40bNyoxMTHwyMjICHdoAADAYsIOMpMmTVJ5eblOnTqlVatWadmyZfrggw96om9hKS4ulsfjCTyqqqqi3SUAANDDBoT7goSEBE2cOFGSNGvWLJ05c0bbtm3TkiVL1NTUpPr6+qCjMrW1tUpNTZUkpaam6vTp00Hv13pXU9uab97pVFtbK4fDocGDB39rv+x2u+x2e7jDAQAAFtbteWRaWlrU2NioWbNmaeDAgTp06FBgW0VFhSorK5WZmSlJyszM1NmzZ1VXVxeoKSkpkcPh0JQpUwI1bd+jtab1PQAAAFqFdUSmuLhYCxcu1NixY9XQ0KAXX3x
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"plt.scatter(data_x,data_y,color='blue')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 128,
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"id": "6bdbfb22",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"8 12.390\n",
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"19 12.424\n",
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"0 21.252\n",
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"11 63.254\n",
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"13 71.108\n",
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"12 72.439\n",
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"5 87.624\n",
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"16 99.073\n",
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"3 114.269\n",
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"2 118.162\n",
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"4 121.444\n",
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"18 131.023\n",
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"9 144.264\n",
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"15 169.084\n",
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"10 169.900\n",
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"6 170.039\n",
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"14 179.476\n",
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"1 179.842\n",
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"7 192.651\n",
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"17 195.528\n",
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"Name: x, dtype: float64"
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]
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},
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"execution_count": 128,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data_x"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 129,
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"id": "392159c0",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.collections.PathCollection at 0x7f7a01573a30>"
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]
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},
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"execution_count": 129,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"reg = LinearRegression()\n",
|
||
|
|
"reg.fit(data_x.array.reshape(-1,1),data_y.array.reshape(-1,1))\n",
|
||
|
|
"predict_data_y = reg.predict(data_x.array.reshape(-1,1))\n",
|
||
|
|
"plt.plot(data_x,predict_data_y,color='red')\n",
|
||
|
|
"plt.scatter(data_x,data_y,color='blue')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 130,
|
||
|
|
"id": "44f292f4",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([[1.23900000e+01, 1.53512100e+02],\n",
|
||
|
|
" [1.24240000e+01, 1.54355776e+02],\n",
|
||
|
|
" [2.12520000e+01, 4.51647504e+02],\n",
|
||
|
|
" [6.32540000e+01, 4.00106852e+03],\n",
|
||
|
|
" [7.11080000e+01, 5.05634766e+03],\n",
|
||
|
|
" [7.24390000e+01, 5.24740872e+03],\n",
|
||
|
|
" [8.76240000e+01, 7.67796538e+03],\n",
|
||
|
|
" [9.90730000e+01, 9.81545933e+03],\n",
|
||
|
|
" [1.14269000e+02, 1.30574044e+04],\n",
|
||
|
|
" [1.18162000e+02, 1.39622582e+04],\n",
|
||
|
|
" [1.21444000e+02, 1.47486451e+04],\n",
|
||
|
|
" [1.31023000e+02, 1.71670265e+04],\n",
|
||
|
|
" [1.44264000e+02, 2.08121017e+04],\n",
|
||
|
|
" [1.69084000e+02, 2.85893991e+04],\n",
|
||
|
|
" [1.69900000e+02, 2.88660100e+04],\n",
|
||
|
|
" [1.70039000e+02, 2.89132615e+04],\n",
|
||
|
|
" [1.79476000e+02, 3.22116346e+04],\n",
|
||
|
|
" [1.79842000e+02, 3.23431450e+04],\n",
|
||
|
|
" [1.92651000e+02, 3.71144078e+04],\n",
|
||
|
|
" [1.95528000e+02, 3.82311988e+04]])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 130,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"poly = PolynomialFeatures(degree=2,include_bias=False)\n",
|
||
|
|
"poly_features = poly.fit_transform(data_x.array.reshape(-1,1))\n",
|
||
|
|
"poly_features"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 131,
|
||
|
|
"id": "be4172cb",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([-202.19762153, -201.84331399, -104.38856598, 508.27954418,\n",
|
||
|
|
" 650.17014568, 675.06921604, 976.63832627, 1225.29257965,\n",
|
||
|
|
" 1583.58306598, 1680.55826071, 1763.95676717, 2015.96524541,\n",
|
||
|
|
" 2385.40255276, 3143.8333219 , 3170.22794171, 3174.73334816,\n",
|
||
|
|
" 3486.92151338, 3499.27966172, 3943.55854171, 4046.49646908])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 131,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"poly_reg = LinearRegression()\n",
|
||
|
|
"poly_reg.fit(poly_features,data_y)\n",
|
||
|
|
"y_predicted = poly_reg.predict(poly_features)\n",
|
||
|
|
"y_predicted"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 132,
|
||
|
|
"id": "f455e240",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x7f7a014e55b0>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 132,
|
||
|
|
"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": [
|
||
|
|
"plt.scatter(data_x,data_y,color='blue')\n",
|
||
|
|
"plt.plot(data_x,y_predicted,color='red')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 133,
|
||
|
|
"id": "3d3da8ad",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x7f7a01449a90>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 133,
|
||
|
|
"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": [
|
||
|
|
"poly = PolynomialFeatures(degree=5,include_bias=False)\n",
|
||
|
|
"poly_features = poly.fit_transform(data_x.array.reshape(-1,1))\n",
|
||
|
|
"poly_reg = LinearRegression()\n",
|
||
|
|
"poly_reg.fit(poly_features,data_y)\n",
|
||
|
|
"y_predicted = poly_reg.predict(poly_features)\n",
|
||
|
|
"plt.scatter(data_x,data_y,color='blue')\n",
|
||
|
|
"plt.plot(data_x,y_predicted,color='red')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "efc0facf",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3 (ipykernel)",
|
||
|
|
"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": "3.8.10"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|