368 lines
28 KiB
Plaintext
368 lines
28 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "d9f26d2d6e2a17cf",
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-03-03T14:01:57.209338Z",
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"start_time": "2024-03-03T14:01:57.151076Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" no_risk thermal_distress launch_temp leak_pressure INDEX\n",
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"0 6 0 66 50 1\n",
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"1 6 1 70 50 2\n",
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"2 6 0 69 50 3\n",
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"3 6 0 68 50 4\n",
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"4 6 0 67 50 5\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from statsmodels.genmod.generalized_linear_model import GLM\n",
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"\n",
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"file_path = \"oringerosiononly.csv\"\n",
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"df = pd.read_csv(file_path, names=[\"no_risk\", \"thermal_distress\", \"launch_temp\", \"leak_pressure\", \"INDEX\"])\n",
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"print(df.head())\n"
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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": 2,
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"id": "c97f4d7287c4fe23",
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-03-03T14:01:57.219680Z",
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"start_time": "2024-03-03T14:01:57.210356Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" no_risk thermal_distress launch_temp leak_pressure INDEX\n",
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"count 23.0 23.000000 23.000000 23.000000 23.00000\n",
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"mean 6.0 0.391304 69.565217 152.173913 12.00000\n",
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"std 0.0 0.656376 7.057080 68.221332 6.78233\n",
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"min 6.0 0.000000 53.000000 50.000000 1.00000\n",
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"25% 6.0 0.000000 67.000000 75.000000 6.50000\n",
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"50% 6.0 0.000000 70.000000 200.000000 12.00000\n",
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"75% 6.0 1.000000 75.000000 200.000000 17.50000\n",
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"max 6.0 2.000000 81.000000 200.000000 23.00000\n"
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]
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}
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],
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"source": [
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"print(df.describe())"
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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": 3,
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"id": "d84f2d04a09fb990",
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-03-03T14:01:57.229742Z",
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"start_time": "2024-03-03T14:01:57.220680Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 23 entries, 0 to 22\n",
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"Data columns (total 5 columns):\n",
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" # Column Non-Null Count Dtype\n",
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"--- ------ -------------- -----\n",
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" 0 no_risk 23 non-null int64\n",
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" 1 thermal_distress 23 non-null int64\n",
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" 2 launch_temp 23 non-null int64\n",
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" 3 leak_pressure 23 non-null int64\n",
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" 4 INDEX 23 non-null int64\n",
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"dtypes: int64(5)\n",
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"memory usage: 1.0 KB\n",
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"None\n"
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]
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}
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],
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"source": [
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"print(df.info())"
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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": 4,
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"id": "3dfdfa04f3e4734c",
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-03-03T14:01:57.236301Z",
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"start_time": "2024-03-03T14:01:57.230742Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(23, 5)\n"
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]
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}
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],
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"source": [
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"print(df.shape)"
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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": 5,
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"id": "7a467de0f28e434b",
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-03-03T14:01:57.364776Z",
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"start_time": "2024-03-03T14:01:57.237303Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": "<Figure size 640x480 with 1 Axes>",
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import matplotlib\n",
|
|
"\n",
|
|
"matplotlib.rcParams['font.sans-serif']=['SimHei'] \n",
|
|
"matplotlib.rcParams['axes.unicode_minus']=False \n",
|
|
"\n",
|
|
"plt.hist(df[\"thermal_distress\"])\n",
|
|
"plt.xlabel(\"热损伤 O 型环数量\")\n",
|
|
"plt.ylabel(\"频数\")\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "f9ba5a5f8de5d14f",
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-03T14:01:57.368684Z",
|
|
"start_time": "2024-03-03T14:01:57.364776Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"均值： 0.391304347826087\n",
|
|
"方差： 0.4120982986767487\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"mean = np.mean(df[\"thermal_distress\"])\n",
|
|
"var = np.var(df[\"thermal_distress\"])\n",
|
|
"\n",
|
|
"print(\"均值：\", mean)\n",
|
|
"print(\"方差：\", var)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "b345be89cd98335a",
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-03T14:01:57.378021Z",
|
|
"start_time": "2024-03-03T14:01:57.369684Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" 发射温度 检漏压力\n",
|
|
"0 66 50\n",
|
|
"1 70 50\n",
|
|
"2 69 50\n",
|
|
"3 68 50\n",
|
|
"4 67 50\n",
|
|
"5 72 50\n",
|
|
"6 73 100\n",
|
|
"7 70 100\n",
|
|
"8 57 200\n",
|
|
"9 63 200\n",
|
|
"10 70 200\n",
|
|
"11 78 200\n",
|
|
"12 67 200\n",
|
|
"13 53 200\n",
|
|
"14 67 200\n",
|
|
"15 75 200\n",
|
|
"16 70 200\n",
|
|
"17 81 200\n",
|
|
"18 76 200\n",
|
|
"19 79 200\n",
|
|
"20 75 200\n",
|
|
"21 76 200\n",
|
|
"22 58 200\n",
|
|
"0 0\n",
|
|
"1 1\n",
|
|
"2 0\n",
|
|
"3 0\n",
|
|
"4 0\n",
|
|
"5 0\n",
|
|
"6 0\n",
|
|
"7 0\n",
|
|
"8 1\n",
|
|
"9 1\n",
|
|
"10 1\n",
|
|
"11 0\n",
|
|
"12 0\n",
|
|
"13 2\n",
|
|
"14 0\n",
|
|
"15 0\n",
|
|
"16 0\n",
|
|
"17 0\n",
|
|
"18 0\n",
|
|
"19 0\n",
|
|
"20 2\n",
|
|
"21 0\n",
|
|
"22 1\n",
|
|
"Name: thermal_distress, dtype: int64\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"X = df[[\"launch_temp\", \"leak_pressure\"]]\n",
|
|
"y = df[\"thermal_distress\"]\n",
|
|
"\n",
|
|
"\n",
|
|
"X.columns = [\"发射温度\", \"检漏压力\"]\n",
|
|
"\n",
|
|
"\n",
|
|
"print(X)\n",
|
|
"print(y)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "b838a41d7eb6384e",
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-03T14:01:57.795499Z",
|
|
"start_time": "2024-03-03T14:01:57.379048Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" Generalized Linear Model Regression Results \n",
|
|
"==============================================================================\n",
|
|
"Dep. Variable: thermal_distress No. Observations: 23\n",
|
|
"Model: GLM Df Residuals: 21\n",
|
|
"Model Family: Poisson Df Model: 1\n",
|
|
"Link Function: Log Scale: 1.0000\n",
|
|
"Method: IRLS Log-Likelihood: -15.916\n",
|
|
"Date: Sun, 03 Mar 2024 Deviance: 16.604\n",
|
|
"Time: 22:01:57 Pearson chi2: 26.5\n",
|
|
"No. Iterations: 5 Pseudo R-squ. (CS): 0.2239\n",
|
|
"Covariance Type: nonrobust \n",
|
|
"==============================================================================\n",
|
|
" coef std err z P>|z| [0.025 0.975]\n",
|
|
"------------------------------------------------------------------------------\n",
|
|
"发射温度 -0.0491 0.022 -2.233 0.026 -0.092 -0.006\n",
|
|
"检漏压力 0.0135 0.008 1.783 0.075 -0.001 0.028\n",
|
|
"==============================================================================\n",
|
|
"发射温度 -0.049104\n",
|
|
"检漏压力 0.013532\n",
|
|
"dtype: float64\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import statsmodels.api as sm\n",
|
|
"\n",
|
|
"model = GLM(y, X, family=sm.families.Poisson()).fit()\n",
|
|
"print(model.summary())\n",
|
|
"print(model.params)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "d94f8ca63a53302e",
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-03T14:01:57.800340Z",
|
|
"start_time": "2024-03-03T14:01:57.796504Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"均方误差： 0.27336465601874094\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"y_pred = model.predict(X)\n",
|
|
"mse = np.mean((y_pred - y)**2)\n",
|
|
"print(\"均方误差：\", mse)\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 2
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython2",
|
|
"version": "2.7.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|