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rakuishi/20180225_multiple-linear-regression_1.ipynb

Last active February 3, 2022 02:14
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【キカガク流】人工知能・機械学習 脱ブラックボックス講座 - 中級編 -
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20180225_multiple-linear-regression_1.ipynb
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20180225_multiple-linear-regression_2.ipynb
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20180301_multiple-linear-regression_3.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.read_csv('housing.csv')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>x1</th>\n",
" <th>x2</th>\n",
" <th>x3</th>\n",
" <th>x4</th>\n",
" <th>x5</th>\n",
" <th>x6</th>\n",
" <th>x7</th>\n",
" <th>x8</th>\n",
" <th>x9</th>\n",
" <th>x10</th>\n",
" <th>x11</th>\n",
" <th>x12</th>\n",
" <th>x13</th>\n",
" <th>y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.00632</td>\n",
" <td>18.0</td>\n",
" <td>2.31</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>6.575</td>\n",
" <td>65.2</td>\n",
" <td>4.0900</td>\n",
" <td>1</td>\n",
" <td>296</td>\n",
" <td>15.3</td>\n",
" <td>396.90</td>\n",
" <td>4.98</td>\n",
" <td>24.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.02731</td>\n",
" <td>0.0</td>\n",
" <td>7.07</td>\n",
" <td>0</td>\n",
" <td>0.469</td>\n",
" <td>6.421</td>\n",
" <td>78.9</td>\n",
" <td>4.9671</td>\n",
" <td>2</td>\n",
" <td>242</td>\n",
" <td>17.8</td>\n",
" <td>396.90</td>\n",
" <td>9.14</td>\n",
" <td>21.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.02729</td>\n",
" <td>0.0</td>\n",
" <td>7.07</td>\n",
" <td>0</td>\n",
" <td>0.469</td>\n",
" <td>7.185</td>\n",
" <td>61.1</td>\n",
" <td>4.9671</td>\n",
" <td>2</td>\n",
" <td>242</td>\n",
" <td>17.8</td>\n",
" <td>392.83</td>\n",
" <td>4.03</td>\n",
" <td>34.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.03237</td>\n",
" <td>0.0</td>\n",
" <td>2.18</td>\n",
" <td>0</td>\n",
" <td>0.458</td>\n",
" <td>6.998</td>\n",
" <td>45.8</td>\n",
" <td>6.0622</td>\n",
" <td>3</td>\n",
" <td>222</td>\n",
" <td>18.7</td>\n",
" <td>394.63</td>\n",
" <td>2.94</td>\n",
" <td>33.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.06905</td>\n",
" <td>0.0</td>\n",
" <td>2.18</td>\n",
" <td>0</td>\n",
" <td>0.458</td>\n",
" <td>7.147</td>\n",
" <td>54.2</td>\n",
" <td>6.0622</td>\n",
" <td>3</td>\n",
" <td>222</td>\n",
" <td>18.7</td>\n",
" <td>396.90</td>\n",
" <td>5.33</td>\n",
" <td>36.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 \\\n",
"0 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 \n",
"1 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 \n",
"2 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 \n",
"3 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 \n",
"4 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 396.90 \n",
"\n",
" x13 y \n",
"0 4.98 24.0 \n",
"1 9.14 21.6 \n",
"2 4.03 34.7 \n",
"3 2.94 33.4 \n",
"4 5.33 36.2 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 分布の確認\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1a12c01b70>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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dQ7hFKMzyOF3KjFaX5VCQmWqtGTOv4uO6ZMbMUlPnIEXZHtyu+Jkp8+Ce5gs+tjg/ww6q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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a12bb84a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(df['x6'])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6.2846343873517876"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 外れ値除去（3σ法）\n",
"col = 'x6'\n",
"\n",
"# 平均\n",
"mean = df.mean()\n",
"mean[col]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.70261714341532344"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 標準偏差（standard deviation）\n",
"sigma = df.std()\n",
"sigma[col]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4.176782957105817"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"low = mean[col] - 3 * sigma[col]\n",
"low"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8.392485817597759"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"high = mean[col] + 3 * sigma[col]\n",
"high"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"df2 = df[(df[col] > low) & (df[col] < high)]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"506"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(df)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"498"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(df2)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1a16044358>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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dQ7hFKMzyOF3KjFaX5VCQmWqtGTOv4uO6ZMbMUlPnIEXZHtyu+Jkp8+Ce5gs+tjg/ww6q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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a12c58e80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(df['x6'])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1a15f8fdd8>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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8tr+Nfc09wFihX7e0hGVlQfIykj1OKIkqOzWJ/3nDefzxO+fw0+oGfvxGHXc/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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a1626a278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(df2['x6'])"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9', 'x10', 'x11',\n",
" 'x12', 'x13', 'y'],\n",
" dtype='object')"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cols = df.columns\n",
"cols"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"_df = df\n",
"for col in cols:\n",
" # 3σ法の上下限値を設定\n",
" low = mean[col] - 3 * sigma[col]\n",
" high = mean[col] + 3 * sigma[col]\n",
" # 条件での絞り込み\n",
" _df = _df[(_df[col] > low) & (_df[col] < high)]"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"415"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(_df)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>x1</th>\n",
" <th>x2</th>\n",
" <th>x3</th>\n",
" <th>x4</th>\n",
" <th>x5</th>\n",
" <th>x6</th>\n",
" <th>x7</th>\n",
" <th>x8</th>\n",
" <th>x9</th>\n",
" <th>x10</th>\n",
" <th>x11</th>\n",
" <th>x12</th>\n",
" <th>x13</th>\n",
" <th>y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.00632</td>\n",
" <td>18.0</td>\n",
" <td>2.31</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>6.575</td>\n",
" <td>65.2</td>\n",
" <td>4.0900</td>\n",
" <td>1</td>\n",
" <td>296</td>\n",
" <td>15.3</td>\n",
" <td>396.90</td>\n",
" <td>4.98</td>\n",
" <td>24.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.02731</td>\n",
" <td>0.0</td>\n",
" <td>7.07</td>\n",
" <td>0</td>\n",
" <td>0.469</td>\n",
" <td>6.421</td>\n",
" <td>78.9</td>\n",
" <td>4.9671</td>\n",
" <td>2</td>\n",
" <td>242</td>\n",
" <td>17.8</td>\n",
" <td>396.90</td>\n",
" <td>9.14</td>\n",
" <td>21.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.02729</td>\n",
" <td>0.0</td>\n",
" <td>7.07</td>\n",
" <td>0</td>\n",
" <td>0.469</td>\n",
" <td>7.185</td>\n",
" <td>61.1</td>\n",
" <td>4.9671</td>\n",
" <td>2</td>\n",
" <td>242</td>\n",
" <td>17.8</td>\n",
" <td>392.83</td>\n",
" <td>4.03</td>\n",
" <td>34.7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 \\\n",
"0 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 \n",
"1 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 \n",
"2 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 \n",
"\n",
" x13 y \n",
"0 4.98 24.0 \n",
"1 9.14 21.6 \n",
"2 4.03 34.7 "
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_df.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X = _df.iloc[:,:-1]\n",
"y = _df.iloc[:,-1]"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((415, 13), (415,))"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.shape, y.shape"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=1)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LinearRegression"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = LinearRegression()\n",
"model.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.79721092245351333"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 検証 ← 訓練データ\n",
"model.score(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 検証 ← 検証データ"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.62537760043295809"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.score(X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# スケーリング\n",
"from sklearn.preprocessing import StandardScaler"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"StandardScaler(copy=True, with_mean=True, with_std=True)"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scaler = StandardScaler()\n",
"scaler.fit(X_train)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"X_train2 = scaler.transform(X_train)\n",
"X_test2 = scaler.transform(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[-0.44982613, -0.47117023, -0.01455847, ..., 0.3716043 ,\n",
" 0.06655172, -0.73371428],\n",
" [-0.45396528, -0.47117023, -0.7386746 , ..., 0.3716043 ,\n",
" 0.38053452, -0.32783019],\n",
" [-0.19959841, -0.47117023, 1.29482264, ..., -1.75496677,\n",
" -0.18177662, -0.75722108],\n",
" ..., \n",
" [ 0.49446336, -0.47117023, 1.07385525, ..., 0.84417564,\n",
" 0.27992872, -0.12097034],\n",
" [-0.45659855, 0.61013288, -0.5893723 , ..., 0.08806149,\n",
" 0.44754069, -0.88259069],\n",
" [-0.47327932, -0.47117023, -0.7386746 , ..., 0.3716043 ,\n",
" 0.44754069, -0.54095851]])"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train2"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = LinearRegression()\n",
"model.fit(X_train2, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.79721092245351333"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.score(X_train2, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.62537760043295942"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.score(X_test2, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-0.19542642, 0.21154333, 0.50827735, 0. , -1.21336016,\n",
" 3.91986658, -0.36986122, -1.85711554, 1.16625182, -1.52855186,\n",
" -1.8725663 , 0.24442708, -2.76100147])"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 重みの確認\n",
"model.coef_"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-0.195, 0.212, 0.508, 0. , -1.213, 3.92 , -0.37 , -1.857,\n",
" 1.166, -1.529, -1.873, 0.244, -2.761])"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.set_printoptions(precision=3, suppress=True)\n",
"model.coef_"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1a175071d0>"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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JqtqqqmPf1T8ALpnsQqq6WVUrVLWisLBwJvWaOHDodDe+EY25nSDPZV15HgCvWevdRIlg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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a17533fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(_df['x6'])"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1a17538320>"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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O86hSXt9u4/tmWLddlMfSwlT+/eUDNLb3OF2OCQEW/A473dxFd5/HxvfNsFwu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\nmPl/hxBAQ2vhI+oAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a16e729e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(_df['x13'])"
]
}
],
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
@rakuishi
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rakuishi commented Mar 3, 2018

pairplot

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