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hotchpotch/learning-ml-reading.ipynb Secret

Last active Feb 21, 2021
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"cells": [
{
"source": [
" import numpy as np\n",
" import matplotlib.pyplot as plt\n",
" import pandas as pd\n",
" from IPython.display import display\n",
" from pprint import pp, pformat as pf"
],
"cell_type": "code",
"metadata": {},
"execution_count": 1,
"outputs": []
},
{
"source": [
"# csv として日にちと進捗率のデータを入れる。\n",
"\n",
"csv = '''day,percent\n",
"1,7\n",
"2,13\n",
"3,15\n",
"4,20\n",
"6,24\n",
"8,30\n",
"10,38\n",
"12,42\n",
"14,51\n",
"16,58\n",
"18,65\n",
"19,72\n",
"20,76\n",
"'''\n"
],
"cell_type": "code",
"metadata": {},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# pandas 経由で ndarray に変換\n",
"from io import StringIO\n",
"progress = pd.read_csv(StringIO(csv)).to_numpy()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"係数 3.45, 切片 4.02\n27.83日\n"
]
}
],
"source": [
"# 単純な線形回帰問題として解く\n",
"from sklearn.linear_model import LinearRegression\n",
"\n",
"X, y = progress[:, 0].reshape(-1, 1), progress[:, 1]\n",
"lr = LinearRegression().fit(X, y)\n",
"print(f'係数 {lr.coef_[0]:.2f}, 切片 {lr.intercept_:.2f}')\n",
"\n",
"# predict は X から y を推測するので、X をyと傾き,切片から求める関数を作る\n",
"calc_X = lambda y: (y - lr.intercept_) / lr.coef_\n",
"print(f'{calc_X(100)[0]:.2f}日')\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0.5, 0, 'day')"
]
},
"metadata": {},
"execution_count": 5
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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"# 図\n",
"\n",
"line = np.linspace(0, 30, 100).reshape(1, -1)\n",
"plt.plot(X.transpose()[0], y, 'o')\n",
"plt.plot(line[0], lr.predict(line.T))\n",
"plt.grid()\n",
"plt.ylim((0, 100))\n",
"plt.xlim((0, 30))\n",
"plt.ylabel('percent')\n",
"plt.xlabel('day')\n"
]
}
]
}
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