添加电容预测
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qdjinghao
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1d946b4464
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"id": "6a94278b-8f51-4edc-966b-4a32876a4536",
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"[228 rows x 8 columns]"
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"id": "f72789a6-f3fa-4ab1-8b62-999413958608",
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"metadata": {},
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"outputs": [
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" '固定炭Fcad(%)']"
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]
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},
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"execution_count": 10,
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"execution_count": 3,
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 4,
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"id": "6ffb1989-3f45-4d1c-84c9-59b1045b7d9e",
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"metadata": {},
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"outputs": [],
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"cell_type": "code",
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"execution_count": 27,
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"execution_count": 5,
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"id": "9c708cc0-9f1b-4669-a350-6d24cb720794",
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"metadata": {},
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 6,
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"id": "103349e1-aa4a-427a-a489-9ab28787088b",
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"metadata": {},
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"outputs": [
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"['氢Had(%)', '碳Cad(%)', '氮Nad(%)', '氧Oad(%)', '弹筒发热量Qb,adMJ/kg']"
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]
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},
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"execution_count": 16,
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"execution_count": 6,
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"cell_type": "code",
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"execution_count": 44,
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"execution_count": 7,
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"id": "839e45dc-e9c8-4956-950b-035687469c81",
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"metadata": {},
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"outputs": [
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"4 54.78 "
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]
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},
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"execution_count": 44,
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"execution_count": 7,
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"cell_type": "code",
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"execution_count": 19,
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"id": "24233d12-9468-49b8-a371-0c6c508c387e",
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"metadata": {},
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"outputs": [],
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"source": [
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"import seaborn as sns"
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]
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": 8,
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"id": "54cd27a6-1a8a-47c0-93d9-c948960a7842",
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"execution_count": 23,
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"execution_count": 9,
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"id": "bba14f71-9d69-4c82-b6bc-b9b74c725b25",
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"execution_count": 24,
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"execution_count": 10,
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"id": "e3a9ad55-0132-430f-ac57-c2e7f8e8590a",
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"execution_count": 40,
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"id": "013c6a58-65f6-48e9-8d7f-b56c87de5b11",
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"metadata": {},
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"outputs": [],
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"source": [
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"param_xgb = {\"silent\": True,\n",
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" \"obj\": 'reg:linear',\n",
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"params_xgb = {\"objective\": 'reg:squarederror',\n",
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" \"subsample\": 1,\n",
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" \"max_depth\": 15,\n",
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" \"eta\": 0.3,\n",
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" \"lambda\": 1,\n",
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" \"alpha\": 0,\n",
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" \"colsample_bytree\": 0.9,}\n",
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"num_round = 1000"
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"num_boost_round = 1000"
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]
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},
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"cell_type": "code",
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"execution_count": 41,
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"execution_count": 26,
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"id": "086f1901-8388-47e9-ae7c-1b2709bc1e22",
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"execution_count": 43,
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"id": "fb7b06af-84bc-483c-b086-7826d7befc9c",
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"MSE: 1.9436, RMSE: 1.3941, MAE: 1.1861, MAPE: 3.94 %, R_2: 0.6095\n",
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"MSE: 1.8735, RMSE: 1.3688, MAE: 1.132, MAPE: 3.77 %, R_2: 0.495\n",
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"MSE: 2.0587, RMSE: 1.4348, MAE: 1.0706, MAPE: 4.08 %, R_2: 0.7862\n",
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"MSE: 1.9298, RMSE: 1.3892, MAE: 1.1469, MAPE: 3.84 %, R_2: 0.5332\n",
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"MSE: 1.4583, RMSE: 1.2076, MAE: 1.097, MAPE: 3.67 %, R_2: 0.6894\n",
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"MSE: 2.0822, RMSE: 1.443, MAE: 1.1645, MAPE: 3.88 %, R_2: 0.5975\n",
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"MSE: 1.3521, RMSE: 1.1628, MAE: 0.9905, MAPE: 3.37 %, R_2: 0.7479\n",
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"MSE: 1.4057, RMSE: 1.1856, MAE: 0.9998, MAPE: 3.3 %, R_2: 0.2946\n",
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"MSE: 2.2274, RMSE: 1.4925, MAE: 1.2638, MAPE: 4.19 %, R_2: 0.6785\n",
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"MSE: 1.4866, RMSE: 1.2193, MAE: 1.0797, MAPE: 3.67 %, R_2: 0.7261\n"
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"MSE: 0.475, RMSE: 0.6892, MAE: 0.5507, MAPE: 1.86 %, R_2: 0.9046\n",
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"MSE: 1.1415, RMSE: 1.0684, MAE: 0.9133, MAPE: 3.06 %, R_2: 0.6923\n",
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"MSE: 0.7247, RMSE: 0.8513, MAE: 0.6606, MAPE: 2.32 %, R_2: 0.9247\n",
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"MSE: 1.3652, RMSE: 1.1684, MAE: 0.9609, MAPE: 3.24 %, R_2: 0.6698\n",
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"MSE: 0.4552, RMSE: 0.6747, MAE: 0.5732, MAPE: 1.94 %, R_2: 0.903\n",
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"MSE: 0.6357, RMSE: 0.7973, MAE: 0.6374, MAPE: 2.2 %, R_2: 0.8771\n",
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"MSE: 0.9972, RMSE: 0.9986, MAE: 0.752, MAPE: 2.47 %, R_2: 0.8141\n",
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"MSE: 1.5218, RMSE: 1.2336, MAE: 1.0569, MAPE: 3.45 %, R_2: 0.2363\n",
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"MSE: 0.6891, RMSE: 0.8301, MAE: 0.6825, MAPE: 2.22 %, R_2: 0.9005\n",
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"MSE: 1.6864, RMSE: 1.2986, MAE: 1.0004, MAPE: 3.51 %, R_2: 0.6893\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"MSE 1.781792\n",
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"RMSE 1.329760\n",
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"MAE 1.113084\n",
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"MAPE 0.037719\n",
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"R_2 0.615796\n",
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"MSE 0.969172\n",
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"RMSE 0.961023\n",
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"MAE 0.778783\n",
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"MAPE 0.026288\n",
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"R_2 0.761188\n",
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"dtype: float64"
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]
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},
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"execution_count": 43,
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"execution_count": 27,
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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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"cell_type": "code",
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"execution_count": 48,
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"execution_count": 28,
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"id": "90841cb7-4f28-4a33-93ac-93df69f1a5a1",
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"metadata": {},
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"MSE: 4.6724, RMSE: 2.1616, MAE: 1.7297, MAPE: 3.42 %, R2: 0.8346\n",
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"MSE: 3.0512, RMSE: 1.7468, MAE: 1.4485, MAPE: 2.62 %, R2: 0.8011\n",
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"MSE: 7.6672, RMSE: 2.769, MAE: 1.951, MAPE: 4.56 %, R2: 0.8856\n",
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"MSE: 4.0334, RMSE: 2.0083, MAE: 1.487, MAPE: 2.77 %, R2: 0.8216\n",
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"MSE: 2.6382, RMSE: 1.6243, MAE: 1.1551, MAPE: 2.12 %, R2: 0.846\n",
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"MSE: 5.8097, RMSE: 2.4103, MAE: 1.8683, MAPE: 3.8 %, R2: 0.83\n",
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"MSE: 2.3446, RMSE: 1.5312, MAE: 1.1294, MAPE: 2.28 %, R2: 0.9069\n",
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"MSE: 3.0069, RMSE: 1.734, MAE: 1.3782, MAPE: 2.46 %, R2: 0.6541\n",
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"MSE: 4.1652, RMSE: 2.0409, MAE: 1.5685, MAPE: 3.2 %, R2: 0.859\n",
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"MSE: 4.2023, RMSE: 2.05, MAE: 1.6284, MAPE: 3.2 %, R2: 0.869\n"
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"MSE: 0.9821, RMSE: 0.991, MAE: 0.7698, MAPE: 1.44 %, R2: 0.9652\n",
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"MSE: 1.2674, RMSE: 1.1258, MAE: 0.8756, MAPE: 1.64 %, R2: 0.9174\n",
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"MSE: 0.9137, RMSE: 0.9559, MAE: 0.757, MAPE: 1.46 %, R2: 0.9864\n",
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"MSE: 1.6012, RMSE: 1.2654, MAE: 1.0173, MAPE: 1.89 %, R2: 0.9292\n",
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"MSE: 1.4694, RMSE: 1.2122, MAE: 0.8524, MAPE: 1.59 %, R2: 0.9142\n",
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"MSE: 0.7552, RMSE: 0.869, MAE: 0.7202, MAPE: 1.39 %, R2: 0.9779\n",
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"MSE: 0.5474, RMSE: 0.7398, MAE: 0.5467, MAPE: 1.0 %, R2: 0.9783\n",
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"MSE: 1.2779, RMSE: 1.1305, MAE: 0.9452, MAPE: 1.73 %, R2: 0.853\n",
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"MSE: 1.1908, RMSE: 1.0912, MAE: 0.9004, MAPE: 1.72 %, R2: 0.9597\n",
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"MSE: 3.9312, RMSE: 1.9827, MAE: 1.2707, MAPE: 2.65 %, R2: 0.8775\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"MSE 4.159107\n",
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"RMSE 2.007631\n",
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"MAE 1.534427\n",
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"MAPE 0.030424\n",
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"R2 0.830794\n",
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"MSE 1.393623\n",
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"RMSE 1.136351\n",
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"MAE 0.865538\n",
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"MAPE 0.016509\n",
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"R2 0.935872\n",
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"dtype: float64"
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]
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},
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"execution_count": 48,
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"execution_count": 28,
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"metadata": {},
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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": 67,
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"execution_count": null,
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"id": "aa67bc97-1258-44bb-9dae-14ace1661ff6",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>MSE</th>\n",
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" <th>RMSE</th>\n",
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" <th>MAE</th>\n",
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" <th>MAPE</th>\n",
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" <th>R2</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>十折交叉验证均值</th>\n",
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" <td>4.159107</td>\n",
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" <td>2.007631</td>\n",
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" <td>1.534427</td>\n",
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" <td>0.030424</td>\n",
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" <td>0.830794</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" MSE RMSE MAE MAPE R2\n",
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"十折交叉验证均值 4.159107 2.007631 1.534427 0.030424 0.830794"
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]
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},
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"execution_count": 67,
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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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"outputs": [],
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"source": []
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.16"
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"version": "3.8.18"
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.16"
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"version": "3.8.18"
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"execution_count": 2,
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"id": "6b84fefd-5936-4da4-ab6b-5b944329ad1d",
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"execution_count": 3,
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},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 5,
|
||||
"id": "972f1e9c-3ebc-45cf-8d1f-7611645e5238",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
|
@ -265,7 +265,7 @@
|
|||
" '固定炭Fcad(%)']"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -277,7 +277,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 6,
|
||||
"id": "c95f1106-b3a4-43c6-88ec-3cdebf91d79a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -287,7 +287,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": 7,
|
||||
"id": "2e96af0a-feda-4a1f-a13e-9c8861c6f4d4",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
|
@ -479,7 +479,7 @@
|
|||
"[228 rows x 8 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -490,18 +490,20 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 8,
|
||||
"id": "04b177a7-2f02-4e23-8ea9-29f34cf3eafc",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"out_cols = ['挥发分Vad(%)']\n",
|
||||
"# out_cols = ['固定炭Fcad(%)']"
|
||||
"# out_cols = ['挥发分Vad(%)']\n",
|
||||
"# drop_cols = ['化验编号', '固定炭Fcad(%)']\n",
|
||||
"out_cols = ['固定炭Fcad(%)']\n",
|
||||
"drop_cols = ['挥发分Vad(%)', '化验编号']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 9,
|
||||
"id": "31169fbf-d78e-42f7-87f3-71ba3dd0979d",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
|
@ -511,7 +513,7 @@
|
|||
"['挥发分Vad(%)']"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -522,7 +524,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 10,
|
||||
"id": "feaedd50-f999-45bf-b465-3d359b0c0110",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -532,7 +534,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 11,
|
||||
"id": "a40bee0f-011a-4edb-80f8-4e2f40e755fd",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -542,7 +544,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 12,
|
||||
"id": "535d37b6-b9de-4025-ac8f-62f5bdbe2451",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
|
@ -550,7 +552,7 @@
|
|||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2024-01-05 17:02:16.953831: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n"
|
||||
"2024-01-08 18:03:14.359273: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
@ -563,7 +565,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 13,
|
||||
"id": "1c85d462-f248-4ffb-908f-eb4b20eab179",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -591,7 +593,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 14,
|
||||
"id": "790284a3-b9d3-4144-b481-38a7c3ecb4b9",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -601,7 +603,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": 15,
|
||||
"id": "cd9a1ca1-d0ca-4cb5-9ef5-fd5d63576cd2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -611,7 +613,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 16,
|
||||
"id": "9bc02f29-0fb7-420d-99a8-435eadc06e29",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -651,7 +653,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 17,
|
||||
"id": "a190207e-5a59-4813-9660-758760cf1b73",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -661,7 +663,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"execution_count": 18,
|
||||
"id": "80f32155-e71f-4615-8d0c-01dfd04988fe",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -709,7 +711,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"execution_count": 21,
|
||||
"id": "7f27bd56-4f6b-4242-9f79-c7d6b3ee2f13",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
|
@ -781,7 +783,7 @@
|
|||
"1 0.674897 0.794606 "
|
||||
]
|
||||
},
|
||||
"execution_count": 22,
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -792,19 +794,19 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"execution_count": 22,
|
||||
"id": "baf45a3d-dc01-44fc-9f0b-456964ac2cdb",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# feature_cols = [x for x in train_data.columns if x not in out_cols and '第二次' not in x]\n",
|
||||
"feature_cols = [x for x in train_data.columns if x not in out_cols]\n",
|
||||
"feature_cols = [x for x in train_data.columns if x not in out_cols and x not in drop_cols]\n",
|
||||
"use_cols = feature_cols + out_cols"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"execution_count": 23,
|
||||
"id": "f2d27538-d2bc-4202-b0cf-d3e0949b4686",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -816,7 +818,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"execution_count": 24,
|
||||
"id": "50daf170-efec-49e5-8f8e-9a45938cacfc",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -827,7 +829,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"execution_count": 25,
|
||||
"id": "0f863423-be12-478b-a08d-e3c6f5dfb8ee",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -839,7 +841,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"execution_count": 26,
|
||||
"id": "2c89b32a-017c-4d05-ab78-8b9b8eb0dcbb",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -850,34 +852,49 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"execution_count": 27,
|
||||
"id": "ae24eea7-7dc1-4e33-9d41-3baff07ebb88",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2024-01-08 18:03:45.062553: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n",
|
||||
"2024-01-08 18:03:45.094631: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_INVALID_DEVICE: invalid device ordinal\n",
|
||||
"2024-01-08 18:03:45.094657: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: zhaojh-yv621\n",
|
||||
"2024-01-08 18:03:45.094661: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: zhaojh-yv621\n",
|
||||
"2024-01-08 18:03:45.094825: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 520.61.5\n",
|
||||
"2024-01-08 18:03:45.094846: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 520.61.5\n",
|
||||
"2024-01-08 18:03:45.094849: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 520.61.5\n",
|
||||
"2024-01-08 18:03:45.095157: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n",
|
||||
"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Model: \"model_2\"\n",
|
||||
"Model: \"model\"\n",
|
||||
"_________________________________________________________________\n",
|
||||
"Layer (type) Output Shape Param # \n",
|
||||
"=================================================================\n",
|
||||
"input (InputLayer) [(None, 1, 7)] 0 \n",
|
||||
"input (InputLayer) [(None, 1, 5)] 0 \n",
|
||||
"_________________________________________________________________\n",
|
||||
"conv1d_3 (Conv1D) (None, 1, 64) 512 \n",
|
||||
"conv1d (Conv1D) (None, 1, 64) 384 \n",
|
||||
"_________________________________________________________________\n",
|
||||
"bidirectional_3 (Bidirection (None, 1, 128) 66048 \n",
|
||||
"bidirectional (Bidirectional (None, 1, 128) 66048 \n",
|
||||
"_________________________________________________________________\n",
|
||||
"dense_5 (Dense) (None, 1, 128) 16512 \n",
|
||||
"dense (Dense) (None, 1, 128) 16512 \n",
|
||||
"_________________________________________________________________\n",
|
||||
"dropout_3 (Dropout) (None, 1, 128) 0 \n",
|
||||
"dropout (Dropout) (None, 1, 128) 0 \n",
|
||||
"_________________________________________________________________\n",
|
||||
"dense_6 (Dense) (None, 1, 64) 8256 \n",
|
||||
"dense_1 (Dense) (None, 1, 64) 8256 \n",
|
||||
"_________________________________________________________________\n",
|
||||
"vad (Dense) (None, 1, 1) 65 \n",
|
||||
"=================================================================\n",
|
||||
"Total params: 91,393\n",
|
||||
"Trainable params: 91,393\n",
|
||||
"Total params: 91,265\n",
|
||||
"Trainable params: 91,265\n",
|
||||
"Non-trainable params: 0\n",
|
||||
"_________________________________________________________________\n"
|
||||
]
|
||||
|
|
@ -890,7 +907,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"execution_count": 28,
|
||||
"id": "ca6ce434-80b6-4609-9596-9a5120680462",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -911,7 +928,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"execution_count": 29,
|
||||
"id": "503bbec7-2020-44c8-b622-05bb41082e43",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -921,17 +938,30 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 30,
|
||||
"id": "6308b1dc-8e2e-4bf9-9b28-3b81979bf7e0",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2024-01-08 18:03:50.956250: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n",
|
||||
"2024-01-08 18:03:50.974801: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2200000000 Hz\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"COL: 挥发分Vad, MSE: 2.49E-01,RMSE: 0.499,MAPE: 1.336 %,MAE: 0.398,R_2: 0.946\n",
|
||||
"COL: 挥发分Vad, MSE: 3.81E-01,RMSE: 0.617,MAPE: 1.597 %,MAE: 0.455,R_2: 0.954\n",
|
||||
"COL: 挥发分Vad, MSE: 5.71E-01,RMSE: 0.756,MAPE: 2.077 %,MAE: 0.621,R_2: 0.854\n"
|
||||
"COL: 挥发分Vad, MSE: 5.84E-01,RMSE: 0.764,MAPE: 2.111 %,MAE: 0.633,R_2: 0.874\n",
|
||||
"COL: 挥发分Vad, MSE: 1.06E+00,RMSE: 1.028,MAPE: 2.941 %,MAE: 0.869,R_2: 0.872\n",
|
||||
"COL: 挥发分Vad, MSE: 6.70E-01,RMSE: 0.819,MAPE: 2.217 %,MAE: 0.658,R_2: 0.829\n",
|
||||
"COL: 挥发分Vad, MSE: 5.96E-01,RMSE: 0.772,MAPE: 2.07 %,MAE: 0.607,R_2: 0.896\n",
|
||||
"WARNING:tensorflow:5 out of the last 9 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f6e8d6f8940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n",
|
||||
"COL: 挥发分Vad, MSE: 8.56E-01,RMSE: 0.925,MAPE: 2.335 %,MAE: 0.717,R_2: 0.805\n",
|
||||
"WARNING:tensorflow:6 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f6e8f6e4160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n",
|
||||
"COL: 挥发分Vad, MSE: 7.24E-01,RMSE: 0.851,MAPE: 2.435 %,MAE: 0.713,R_2: 0.851\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
@ -971,20 +1001,22 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 49,
|
||||
"execution_count": 31,
|
||||
"id": "f7132465-89e9-4193-829b-c6e7606cd266",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"COL: 固定炭Fcad, MSE: 2.10E-01,RMSE: 0.458,MAPE: 0.687 %,MAE: 0.361,R_2: 0.992\n",
|
||||
"COL: 固定炭Fcad, MSE: 3.45E-01,RMSE: 0.587,MAPE: 0.865 %,MAE: 0.404,R_2: 0.993\n",
|
||||
"COL: 固定炭Fcad, MSE: 3.77E-01,RMSE: 0.614,MAPE: 0.837 %,MAE: 0.465,R_2: 0.973\n",
|
||||
"COL: 固定炭Fcad, MSE: 2.15E-01,RMSE: 0.463,MAPE: 0.693 %,MAE: 0.35,R_2: 0.994\n",
|
||||
"COL: 固定炭Fcad, MSE: 2.75E-01,RMSE: 0.525,MAPE: 0.746 %,MAE: 0.41,R_2: 0.987\n",
|
||||
"COL: 固定炭Fcad, MSE: 4.84E-01,RMSE: 0.696,MAPE: 0.968 %,MAE: 0.483,R_2: 0.979\n"
|
||||
"ename": "KeyError",
|
||||
"evalue": "\"None of [Index(['固定炭Fcad(%)'], dtype='object')] are in the [columns]\"",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[0;32mIn[31], line 8\u001b[0m\n\u001b[1;32m 6\u001b[0m valid \u001b[38;5;241m=\u001b[39m train_data\u001b[38;5;241m.\u001b[39mloc[test_index]\n\u001b[1;32m 7\u001b[0m X \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mexpand_dims(train[feature_cols]\u001b[38;5;241m.\u001b[39mvalues, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m----> 8\u001b[0m Y \u001b[38;5;241m=\u001b[39m [x \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m \u001b[43mtrain\u001b[49m\u001b[43m[\u001b[49m\u001b[43mout_cols\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mT]\n\u001b[1;32m 9\u001b[0m X_valid \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mexpand_dims(valid[feature_cols]\u001b[38;5;241m.\u001b[39mvalues, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 10\u001b[0m Y_valid \u001b[38;5;241m=\u001b[39m [x \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m valid[out_cols]\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mT]\n",
|
||||
"File \u001b[0;32m~/miniconda3/envs/python38/lib/python3.8/site-packages/pandas/core/frame.py:3030\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3028\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[1;32m 3029\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[0;32m-> 3030\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_listlike_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mraise_missing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 3032\u001b[0m \u001b[38;5;66;03m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[1;32m 3033\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(indexer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n",
|
||||
"File \u001b[0;32m~/miniconda3/envs/python38/lib/python3.8/site-packages/pandas/core/indexing.py:1265\u001b[0m, in \u001b[0;36m_LocIndexer._get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m 1262\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1263\u001b[0m keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[0;32m-> 1265\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_read_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mraise_missing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mraise_missing\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1266\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m keyarr, indexer\n",
|
||||
"File \u001b[0;32m~/miniconda3/envs/python38/lib/python3.8/site-packages/pandas/core/indexing.py:1307\u001b[0m, in \u001b[0;36m_LocIndexer._validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m 1305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m missing \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(indexer):\n\u001b[1;32m 1306\u001b[0m axis_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis_name(axis)\n\u001b[0;32m-> 1307\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1309\u001b[0m ax \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis(axis)\n\u001b[1;32m 1311\u001b[0m \u001b[38;5;66;03m# We (temporarily) allow for some missing keys with .loc, except in\u001b[39;00m\n\u001b[1;32m 1312\u001b[0m \u001b[38;5;66;03m# some cases (e.g. setting) in which \"raise_missing\" will be False\u001b[39;00m\n",
|
||||
"\u001b[0;31mKeyError\u001b[0m: \"None of [Index(['固定炭Fcad(%)'], dtype='object')] are in the [columns]\""
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
@ -1020,22 +1052,22 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"execution_count": 32,
|
||||
"id": "27e0abf7-aa29-467f-bc5e-b66a1adf6165",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"MSE 0.394351\n",
|
||||
"RMSE 0.625663\n",
|
||||
"MAE 0.507130\n",
|
||||
"MAPE 0.017249\n",
|
||||
"R_2 0.920159\n",
|
||||
"MSE 0.747816\n",
|
||||
"RMSE 0.859839\n",
|
||||
"MAE 0.699474\n",
|
||||
"MAPE 0.023513\n",
|
||||
"R_2 0.854338\n",
|
||||
"dtype: float64"
|
||||
]
|
||||
},
|
||||
"execution_count": 51,
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -1047,26 +1079,10 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 52,
|
||||
"execution_count": null,
|
||||
"id": "070cdb94-6e7b-4028-b6d5-ba8570c902ba",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"MSE 0.317628\n",
|
||||
"RMSE 0.557178\n",
|
||||
"MAE 0.412263\n",
|
||||
"MAPE 0.007993\n",
|
||||
"R_2 0.986373\n",
|
||||
"dtype: float64"
|
||||
]
|
||||
},
|
||||
"execution_count": 52,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fcad_df = pd.DataFrame.from_records(fcad_eva_list, columns=['MSE', 'RMSE', 'MAE', 'MAPE', 'R_2'])\n",
|
||||
"fcad_df.sort_values(by='R_2').mean()"
|
||||
|
|
|
|||
|
|
@ -0,0 +1,150 @@
|
|||
,共碳化物/煤沥青,加热次数,模板剂比例,KOH与煤沥青比例,活化温度,升温速率,活化时间,共碳化物质_2-甲基咪唑,共碳化物质_三聚氰胺,共碳化物质_尿素,共碳化物质_无,共碳化物质_硫酸铵,共碳化物质_聚磷酸铵,是否有碳化过程_否,是否有碳化过程_是,模板剂种类_Al2O3,模板剂种类_TiO2,模板剂种类_α-Fe2O3,模板剂种类_γ-Fe2O3,模板剂种类_二氧化硅,模板剂种类_无,模板剂种类_氯化钾,模板剂种类_纤维素,模板剂种类_自制氢氧化镁,模板剂种类_自制氧化钙,模板剂种类_自制氧化锌,模板剂种类_自制氧化镁,模板剂种类_自制碱式碳酸镁,模板剂种类_购买氢氧化镁,模板剂种类_购买氧化钙,模板剂种类_购买氧化锌,模板剂种类_购买氧化镁,模板剂种类_购买氯化钠,模板剂种类_购买碳酸钙,混合方式_溶剂,混合方式_研磨,比表面积
|
||||
0,0.0,0.0,0.1,0.06666667,0.0,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.2734374
|
||||
1,0.0,0.0,0.1,0.033333335,0.16666667,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.28734466
|
||||
2,0.0,0.0,0.1,0.06666667,0.16666667,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.41910276
|
||||
3,0.0,0.0,0.1,0.13333334,0.16666667,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.43608063
|
||||
4,0.0,1.0,0.1,0.06666667,0.16666667,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.30766597
|
||||
5,0.0,1.0,0.1,0.06666667,0.33333334,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.26624128
|
||||
6,0.0,1.0,0.1,0.06666667,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.49787512
|
||||
7,0.0,1.0,0.1,0.033333335,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.3080297
|
||||
8,0.0,1.0,0.1,0.13333334,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.59416187
|
||||
9,0.0,1.0,0.1,0.0,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.14238557
|
||||
10,0.0,0.0,0.1,0.06666667,0.16666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.31685358
|
||||
11,0.0,0.0,0.1,0.06666667,0.16666667,0.3,0.6666667,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.39767203
|
||||
12,0.0,0.0,0.1,0.06666667,0.33333334,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.61802363
|
||||
13,0.0,1.0,0.0,0.0,0.33333334,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0020951803
|
||||
14,0.0,1.0,0.0,0.06666667,0.33333334,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.3966605
|
||||
15,0.0,1.0,0.0,0.13333334,0.33333334,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.5894941
|
||||
16,0.0,1.0,0.0,0.2,0.33333334,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.78140527
|
||||
17,0.8,0.0,0.0,1.0,0.33333334,0.3,0.33333334,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.33728373
|
||||
18,0.8,0.0,0.0,0.6666667,0.33333334,0.3,0.33333334,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.6601188
|
||||
19,0.8,0.0,0.0,0.33333334,0.33333334,0.3,0.33333334,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.4943704
|
||||
20,0.0,0.0,0.0,0.0,0.6666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.004337678
|
||||
21,0.4,0.0,0.0,0.0,0.6666667,0.3,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.14383873
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22,0.8,0.0,0.0,0.0,0.6666667,0.3,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.17430039
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23,1.0,1.0,0.0,0.26666668,0.16666667,0.3,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.35616854
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24,1.0,1.0,0.0,0.26666668,0.33333334,0.3,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.5586541
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25,1.0,1.0,0.0,0.26666668,0.5,0.3,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.6759624
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26,0.0,0.0,0.0,0.26666668,0.16666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.2079418
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27,0.0,0.0,0.6,0.13333334,0.5,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.7371931
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28,0.0,0.0,0.6,0.26666668,0.5,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.79205817
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29,0.0,0.0,0.6,0.4,0.5,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.9515005
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30,0.0,0.0,0.6,0.4,0.6666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.9314944
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31,0.0,0.0,0.0,0.13333334,0.16666667,0.8,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.27311307
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32,0.0,0.0,0.0,0.13333334,0.33333334,0.8,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.41709608
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33,0.0,0.0,0.0,0.13333334,0.5,0.8,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.60230374
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34,0.0,0.0,0.0,0.06666667,0.33333334,0.8,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.1927857
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35,0.0,0.0,0.0,0.2,0.33333334,0.8,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.50530463
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36,0.0,0.0,0.0,0.26666668,0.33333334,0.8,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.70809335
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37,0.0,1.0,0.1,0.06666667,0.33333334,0.8,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.29039103
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38,0.0,1.0,0.1,0.13333334,0.33333334,0.8,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.50227344
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39,0.0,1.0,0.1,0.2,0.33333334,0.8,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.60533494
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40,0.0,1.0,0.1,0.26666668,0.33333334,0.8,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.305244
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41,0.0,0.0,0.0,0.13333334,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.30494088
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42,0.0,0.0,0.0,0.13333334,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.2518945
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43,0.0,0.0,0.0,0.06666667,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.2270385
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44,0.0,1.0,0.1,0.0,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.15822977
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45,0.0,1.0,0.1,0.06666667,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.48438922
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46,0.0,1.0,0.1,0.13333334,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.439224
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47,0.0,0.0,0.1,0.33333334,0.5,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.92573506
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48,0.0,0.0,0.25,0.33333334,0.5,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.64655954
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49,0.0,0.0,0.4,0.33333334,0.5,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.5568354
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50,0.0,0.0,0.25,0.33333334,0.5833333,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.86207944
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||||
51,0.0,0.0,0.5,0.5,0.16666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.28250986
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52,0.0,0.0,0.5,0.5,0.33333334,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.60533494
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53,0.0,0.0,0.5,0.5,0.5,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.74992424
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54,0.05,0.0,0.5,0.5,0.5,0.3,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.7784177
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55,0.2,0.0,0.5,0.5,0.5,0.3,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.45862383
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56,0.4,0.0,0.5,0.5,0.5,0.3,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.45589572
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57,0.05,0.0,0.5,0.5,0.5,0.3,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.7829645
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58,0.2,0.0,0.5,0.5,0.5,0.3,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.898151
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59,0.4,0.0,0.5,0.5,0.5,0.3,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.8184298
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60,0.0,0.0,0.0,0.028666666,0.68333334,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.081236735
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61,0.0,0.0,0.0,0.05733333,0.68333334,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.36586845
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62,0.0,0.0,0.0,0.09533333,0.68333334,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.45953318
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63,0.0,0.0,0.0,0.13333334,0.68333334,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.5316763
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64,0.0,0.0,0.0,0.26666668,0.5833333,0.3,0.6666667,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.5316763
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65,0.0,0.0,0.05,0.26666668,0.5833333,0.3,0.6666667,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.76689905
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66,0.0,0.0,0.1,0.26666668,0.5833333,0.3,0.6666667,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.4186117
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67,0.0,0.0,0.2,0.26666668,0.5833333,0.3,0.6666667,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.75204605
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68,0.0,1.0,0.05,0.2,0.6666667,0.3,0.16666667,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.4434374
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69,0.0,1.0,0.1,0.2,0.33333334,0.3,0.16666667,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.62612915
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70,0.0,1.0,0.0334,0.2,0.6666667,0.3,0.16666667,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.28181267
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75,0.0,1.0,1.0,0.0,0.33333334,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.08578357
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76,0.0,1.0,1.0,0.06666667,0.16666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.44377083
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77,0.0,1.0,1.0,0.06666667,0.33333334,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.53682935
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78,0.0,1.0,1.0,0.06666667,0.5,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.5822977
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79,0.0,0.0,0.005,0.26666668,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.86238253
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80,0.0,0.0,0.01,0.26666668,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0
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81,0.0,0.0,0.03,0.26666668,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.62443167
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82,0.0,0.0,0.01,0.2,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.75932103
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83,0.0,0.0,0.01,0.33333334,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.63989085
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84,0.0,1.0,0.025,0.26666668,0.5833333,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.12337072
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85,0.0,1.0,0.0334,0.26666668,0.5833333,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.4131555
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86,0.0,1.0,0.05,0.26666668,0.5833333,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.44710517
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87,0.0,1.0,0.1,0.26666668,0.5833333,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.4037587
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88,0.0,0.0,0.0,0.057466667,0.6666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.30312216
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89,0.0,0.0,0.2,0.057133332,0.6666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.37859958
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90,0.0,0.0,0.4,0.09533333,0.6666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.40133375
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91,0.0,0.0,0.2,0.057133332,0.6666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.37223402
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92,0.0,0.0,0.1333,0.044466667,0.6666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.3488936
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93,0.0,0.0,0.05,0.0286,0.6666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.22885723
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94,0.0,0.0,0.2,0.0,0.6666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.10366778
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95,0.0,0.0,0.0,0.0,0.33333334,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0024249773
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96,0.0,0.0,0.0,0.13333334,0.16666667,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.2518945
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97,0.0,0.0,0.0,0.13333334,0.33333334,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.27129433
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98,0.0,0.0,0.0,0.13333334,0.5,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.3476811
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99,0.0,0.0,0.0,0.13333334,0.6666667,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.32797816
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100,0.0,0.0,0.0,0.0,0.6666667,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0006062443
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101,0.0,0.0,0.05,0.0,0.6666667,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0051530767
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102,0.0,0.0,0.05,0.13333334,0.6666667,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.4580176
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103,0.0,0.0,0.1,0.13333334,0.6666667,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.56047285
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104,0.0,0.0,0.15,0.13333334,0.6666667,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.3886026
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105,0.0,0.0,0.05,0.13333334,0.33333334,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.4677175
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106,0.0,0.0,0.0,0.06666667,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.39735374
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107,0.0,0.0,0.0,0.13333334,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.4139618
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108,0.0,0.0,0.0,0.2,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.4243589
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109,0.0,0.0,0.0,0.06666667,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.273398
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110,0.0,0.0,0.0,0.13333334,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.45190665
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111,0.0,0.0,0.0,0.2,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.6100697
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112,0.2,0.0,0.0,0.13333334,0.5,0.3,0.33333334,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.5117975
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113,0.0666,0.0,0.0,0.13333334,0.5,0.3,0.33333334,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.8563868
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114,0.0,0.0,0.0,0.13333334,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.35588664
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115,0.6,1.0,0.0,0.2,0.5,0.3,0.33333334,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.76395875
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116,0.6,1.0,0.0,0.13333334,0.5,0.3,0.33333334,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.4960079
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117,0.6,1.0,0.0,0.0,0.5,0.3,0.33333334,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.377863
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118,0.6,0.0,1.0,0.0,0.5,0.3,0.33333334,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.4002122
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119,0.4,0.0,1.0,0.0,0.5,0.3,0.33333334,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.4308518
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@ -1,43 +1,193 @@
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"cell_type": "markdown",
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||||
"\n# A demo for multi-output regression\n\nThe demo is adopted from scikit-learn:\n\nhttps://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py\n\nSee :doc:`/tutorials/multioutput` for more information.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>The feature is experimental. For the `multi_output_tree` strategy, many features are\n missing.</p></div>\n"
|
||||
]
|
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},
|
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|
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"import argparse\nfrom typing import Dict, List, Tuple\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nimport xgboost as xgb\n\n\ndef plot_predt(y: np.ndarray, y_predt: np.ndarray, name: str) -> None:\n s = 25\n plt.scatter(y[:, 0], y[:, 1], c=\"navy\", s=s, edgecolor=\"black\", label=\"data\")\n plt.scatter(\n y_predt[:, 0], y_predt[:, 1], c=\"cornflowerblue\", s=s, edgecolor=\"black\"\n )\n plt.xlim([-1, 2])\n plt.ylim([-1, 2])\n plt.show()\n\n\ndef gen_circle() -> Tuple[np.ndarray, np.ndarray]:\n \"Generate a sample dataset that y is a 2 dim circle.\"\n rng = np.random.RandomState(1994)\n X = np.sort(200 * rng.rand(100, 1) - 100, axis=0)\n y = np.array([np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]).T\n y[::5, :] += 0.5 - rng.rand(20, 2)\n y = y - y.min()\n y = y / y.max()\n return X, y\n\n\ndef rmse_model(plot_result: bool, strategy: str) -> None:\n \"\"\"Draw a circle with 2-dim coordinate as target variables.\"\"\"\n X, y = gen_circle()\n # Train a regressor on it\n reg = xgb.XGBRegressor(\n tree_method=\"hist\",\n n_estimators=128,\n n_jobs=16,\n max_depth=8,\n multi_strategy=strategy,\n subsample=0.6,\n )\n reg.fit(X, y, eval_set=[(X, y)])\n\n y_predt = reg.predict(X)\n if plot_result:\n plot_predt(y, y_predt, \"multi\")\n\n\ndef custom_rmse_model(plot_result: bool, strategy: str) -> None:\n \"\"\"Train using Python implementation of Squared Error.\"\"\"\n\n # As the experimental support status, custom objective doesn't support matrix as\n # gradient and hessian, which will be changed in future release.\n def gradient(predt: np.ndarray, dtrain: xgb.DMatrix) -> np.ndarray:\n \"\"\"Compute the gradient squared error.\"\"\"\n y = dtrain.get_label().reshape(predt.shape)\n return (predt - y).reshape(y.size)\n\n def hessian(predt: np.ndarray, dtrain: xgb.DMatrix) -> np.ndarray:\n \"\"\"Compute the hessian for squared error.\"\"\"\n return np.ones(predt.shape).reshape(predt.size)\n\n def squared_log(\n predt: np.ndarray, dtrain: xgb.DMatrix\n ) -> Tuple[np.ndarray, np.ndarray]:\n grad = gradient(predt, dtrain)\n hess = hessian(predt, dtrain)\n return grad, hess\n\n def rmse(predt: np.ndarray, dtrain: xgb.DMatrix) -> Tuple[str, float]:\n y = dtrain.get_label().reshape(predt.shape)\n v = np.sqrt(np.sum(np.power(y - predt, 2)))\n return \"PyRMSE\", v\n\n X, y = gen_circle()\n Xy = xgb.DMatrix(X, y)\n results: Dict[str, Dict[str, List[float]]] = {}\n # Make sure the `num_target` is passed to XGBoost when custom objective is used.\n # When builtin objective is used, XGBoost can figure out the number of targets\n # automatically.\n booster = xgb.train(\n {\n \"tree_method\": \"hist\",\n \"num_target\": y.shape[1],\n \"multi_strategy\": strategy,\n },\n dtrain=Xy,\n num_boost_round=128,\n obj=squared_log,\n evals=[(Xy, \"Train\")],\n evals_result=results,\n custom_metric=rmse,\n )\n\n y_predt = booster.inplace_predict(X)\n if plot_result:\n plot_predt(y, y_predt, \"multi\")\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--plot\", choices=[0, 1], type=int, default=1)\n args = parser.parse_args()\n\n # Train with builtin RMSE objective\n # - One model per output.\n rmse_model(args.plot == 1, \"one_output_per_tree\")\n # - One model for all outputs, this is still working in progress, many features are\n # missing.\n rmse_model(args.plot == 1, \"multi_output_tree\")\n\n # Train with custom objective.\n # - One model per output.\n custom_rmse_model(args.plot == 1, \"one_output_per_tree\")\n # - One model for all outputs, this is still working in progress, many features are\n # missing.\n custom_rmse_model(args.plot == 1, \"multi_output_tree\")"
|
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|
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|
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|
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|
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|
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{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"\n",
|
||||
"# A demo for multi-output regression\n",
|
||||
"\n",
|
||||
"The demo is adopted from scikit-learn:\n",
|
||||
"\n",
|
||||
"https://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py\n",
|
||||
"\n",
|
||||
"See :doc:`/tutorials/multioutput` for more information.\n",
|
||||
"\n",
|
||||
"<div class=\"alert alert-info\"><h4>Note</h4><p>The feature is experimental. For the `multi_output_tree` strategy, many features are\n",
|
||||
" missing.</p></div>\n"
|
||||
]
|
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|
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|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ModuleNotFoundError",
|
||||
"evalue": "No module named 'xgboost'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[0;32mIn[1], line 7\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pyplot \u001b[38;5;28;01mas\u001b[39;00m plt\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mxgboost\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mxgb\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot_predt\u001b[39m(y: np\u001b[38;5;241m.\u001b[39mndarray, y_predt: np\u001b[38;5;241m.\u001b[39mndarray, name: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 11\u001b[0m s \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m25\u001b[39m\n",
|
||||
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'xgboost'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import argparse\n",
|
||||
"from typing import Dict, List, Tuple\n",
|
||||
"\n",
|
||||
"import numpy as np\n",
|
||||
"from matplotlib import pyplot as plt\n",
|
||||
"\n",
|
||||
"import xgboost as xgb\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def plot_predt(y: np.ndarray, y_predt: np.ndarray, name: str) -> None:\n",
|
||||
" s = 25\n",
|
||||
" plt.scatter(y[:, 0], y[:, 1], c=\"navy\", s=s, edgecolor=\"black\", label=\"data\")\n",
|
||||
" plt.scatter(\n",
|
||||
" y_predt[:, 0], y_predt[:, 1], c=\"cornflowerblue\", s=s, edgecolor=\"black\"\n",
|
||||
" )\n",
|
||||
" plt.xlim([-1, 2])\n",
|
||||
" plt.ylim([-1, 2])\n",
|
||||
" plt.show()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def gen_circle() -> Tuple[np.ndarray, np.ndarray]:\n",
|
||||
" \"Generate a sample dataset that y is a 2 dim circle.\"\n",
|
||||
" rng = np.random.RandomState(1994)\n",
|
||||
" X = np.sort(200 * rng.rand(100, 1) - 100, axis=0)\n",
|
||||
" y = np.array([np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]).T\n",
|
||||
" y[::5, :] += 0.5 - rng.rand(20, 2)\n",
|
||||
" y = y - y.min()\n",
|
||||
" y = y / y.max()\n",
|
||||
" return X, y\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def rmse_model(plot_result: bool, strategy: str) -> None:\n",
|
||||
" \"\"\"Draw a circle with 2-dim coordinate as target variables.\"\"\"\n",
|
||||
" X, y = gen_circle()\n",
|
||||
" # Train a regressor on it\n",
|
||||
" reg = xgb.XGBRegressor(\n",
|
||||
" tree_method=\"hist\",\n",
|
||||
" n_estimators=128,\n",
|
||||
" n_jobs=16,\n",
|
||||
" max_depth=8,\n",
|
||||
" multi_strategy=strategy,\n",
|
||||
" subsample=0.6,\n",
|
||||
" )\n",
|
||||
" reg.fit(X, y, eval_set=[(X, y)])\n",
|
||||
"\n",
|
||||
" y_predt = reg.predict(X)\n",
|
||||
" if plot_result:\n",
|
||||
" plot_predt(y, y_predt, \"multi\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def custom_rmse_model(plot_result: bool, strategy: str) -> None:\n",
|
||||
" \"\"\"Train using Python implementation of Squared Error.\"\"\"\n",
|
||||
"\n",
|
||||
" # As the experimental support status, custom objective doesn't support matrix as\n",
|
||||
" # gradient and hessian, which will be changed in future release.\n",
|
||||
" def gradient(predt: np.ndarray, dtrain: xgb.DMatrix) -> np.ndarray:\n",
|
||||
" \"\"\"Compute the gradient squared error.\"\"\"\n",
|
||||
" y = dtrain.get_label().reshape(predt.shape)\n",
|
||||
" return (predt - y).reshape(y.size)\n",
|
||||
"\n",
|
||||
" def hessian(predt: np.ndarray, dtrain: xgb.DMatrix) -> np.ndarray:\n",
|
||||
" \"\"\"Compute the hessian for squared error.\"\"\"\n",
|
||||
" return np.ones(predt.shape).reshape(predt.size)\n",
|
||||
"\n",
|
||||
" def squared_log(\n",
|
||||
" predt: np.ndarray, dtrain: xgb.DMatrix\n",
|
||||
" ) -> Tuple[np.ndarray, np.ndarray]:\n",
|
||||
" grad = gradient(predt, dtrain)\n",
|
||||
" hess = hessian(predt, dtrain)\n",
|
||||
" return grad, hess\n",
|
||||
"\n",
|
||||
" def rmse(predt: np.ndarray, dtrain: xgb.DMatrix) -> Tuple[str, float]:\n",
|
||||
" y = dtrain.get_label().reshape(predt.shape)\n",
|
||||
" v = np.sqrt(np.sum(np.power(y - predt, 2)))\n",
|
||||
" return \"PyRMSE\", v\n",
|
||||
"\n",
|
||||
" X, y = gen_circle()\n",
|
||||
" Xy = xgb.DMatrix(X, y)\n",
|
||||
" results: Dict[str, Dict[str, List[float]]] = {}\n",
|
||||
" # Make sure the `num_target` is passed to XGBoost when custom objective is used.\n",
|
||||
" # When builtin objective is used, XGBoost can figure out the number of targets\n",
|
||||
" # automatically.\n",
|
||||
" booster = xgb.train(\n",
|
||||
" {\n",
|
||||
" \"tree_method\": \"hist\",\n",
|
||||
" \"num_target\": y.shape[1],\n",
|
||||
" \"multi_strategy\": strategy,\n",
|
||||
" },\n",
|
||||
" dtrain=Xy,\n",
|
||||
" num_boost_round=128,\n",
|
||||
" obj=squared_log,\n",
|
||||
" evals=[(Xy, \"Train\")],\n",
|
||||
" evals_result=results,\n",
|
||||
" custom_metric=rmse,\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" y_predt = booster.inplace_predict(X)\n",
|
||||
" if plot_result:\n",
|
||||
" plot_predt(y, y_predt, \"multi\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"if __name__ == \"__main__\":\n",
|
||||
" parser = argparse.ArgumentParser()\n",
|
||||
" parser.add_argument(\"--plot\", choices=[0, 1], type=int, default=1)\n",
|
||||
" args = parser.parse_args()\n",
|
||||
"\n",
|
||||
" # Train with builtin RMSE objective\n",
|
||||
" # - One model per output.\n",
|
||||
" rmse_model(args.plot == 1, \"one_output_per_tree\")\n",
|
||||
" # - One model for all outputs, this is still working in progress, many features are\n",
|
||||
" # missing.\n",
|
||||
" rmse_model(args.plot == 1, \"multi_output_tree\")\n",
|
||||
"\n",
|
||||
" # Train with custom objective.\n",
|
||||
" # - One model per output.\n",
|
||||
" custom_rmse_model(args.plot == 1, \"one_output_per_tree\")\n",
|
||||
" # - One model for all outputs, this is still working in progress, many features are\n",
|
||||
" # missing.\n",
|
||||
" custom_rmse_model(args.plot == 1, \"multi_output_tree\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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": "3.8.18"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
|
|
|||
Binary file not shown.
|
After Width: | Height: | Size: 982 KiB |
|
|
@ -1,759 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "e2fb2c7b-89ca-4e2b-aa44-19403cef590a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "f47b0afa-9e2d-4f2d-a51b-6e2071ffd08a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"old_data = pd.read_excel('./data/煤质碳材料数据.xlsx')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "77fa919c-d186-4079-a7b1-70842c97c3ec",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"nature_data = pd.read_excel('./data/nature.xlsx')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "38a1f29b-06e1-47a4-8839-e37568bac6cf",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"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>编号</th>\n",
|
||||
" <th>煤种</th>\n",
|
||||
" <th>分析水Mad</th>\n",
|
||||
" <th>灰分</th>\n",
|
||||
" <th>挥发分</th>\n",
|
||||
" <th>碳</th>\n",
|
||||
" <th>氢</th>\n",
|
||||
" <th>氮</th>\n",
|
||||
" <th>硫</th>\n",
|
||||
" <th>氧</th>\n",
|
||||
" <th>碳化温度（℃）</th>\n",
|
||||
" <th>升温速率（℃/min)</th>\n",
|
||||
" <th>保温时间(h)</th>\n",
|
||||
" <th>KOH</th>\n",
|
||||
" <th>K2CO3</th>\n",
|
||||
" <th>BET比表面积（m2/g）</th>\n",
|
||||
" <th>孔体积（cm3/g)</th>\n",
|
||||
" <th>微孔体积（cm3/g)</th>\n",
|
||||
" <th>介孔体积（cm3/g)</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>中级烟煤</td>\n",
|
||||
" <td>2.12</td>\n",
|
||||
" <td>8.49</td>\n",
|
||||
" <td>37.14</td>\n",
|
||||
" <td>86.20</td>\n",
|
||||
" <td>5.42</td>\n",
|
||||
" <td>1.60</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>6.78</td>\n",
|
||||
" <td>1100.0</td>\n",
|
||||
" <td>2.0</td>\n",
|
||||
" <td>2.0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>296.0</td>\n",
|
||||
" <td>0.270</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>萃取中级烟煤</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>75.11</td>\n",
|
||||
" <td>4.73</td>\n",
|
||||
" <td>1.38</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>18.78</td>\n",
|
||||
" <td>1100.0</td>\n",
|
||||
" <td>2.0</td>\n",
|
||||
" <td>2.0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>316.0</td>\n",
|
||||
" <td>0.481</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>褐煤</td>\n",
|
||||
" <td>14.91</td>\n",
|
||||
" <td>4.35</td>\n",
|
||||
" <td>48.42</td>\n",
|
||||
" <td>67.76</td>\n",
|
||||
" <td>4.57</td>\n",
|
||||
" <td>1.29</td>\n",
|
||||
" <td>3.56</td>\n",
|
||||
" <td>22.82</td>\n",
|
||||
" <td>650.0</td>\n",
|
||||
" <td>10.0</td>\n",
|
||||
" <td>0.5</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>665.0</td>\n",
|
||||
" <td>0.356</td>\n",
|
||||
" <td>0.289</td>\n",
|
||||
" <td>0.067</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>褐煤</td>\n",
|
||||
" <td>14.91</td>\n",
|
||||
" <td>4.35</td>\n",
|
||||
" <td>48.42</td>\n",
|
||||
" <td>67.76</td>\n",
|
||||
" <td>4.57</td>\n",
|
||||
" <td>1.29</td>\n",
|
||||
" <td>3.56</td>\n",
|
||||
" <td>22.82</td>\n",
|
||||
" <td>650.0</td>\n",
|
||||
" <td>10.0</td>\n",
|
||||
" <td>0.5</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1221.0</td>\n",
|
||||
" <td>0.608</td>\n",
|
||||
" <td>0.482</td>\n",
|
||||
" <td>0.126</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>褐煤</td>\n",
|
||||
" <td>14.91</td>\n",
|
||||
" <td>4.35</td>\n",
|
||||
" <td>48.42</td>\n",
|
||||
" <td>67.76</td>\n",
|
||||
" <td>4.57</td>\n",
|
||||
" <td>1.29</td>\n",
|
||||
" <td>3.56</td>\n",
|
||||
" <td>22.82</td>\n",
|
||||
" <td>650.0</td>\n",
|
||||
" <td>10.0</td>\n",
|
||||
" <td>0.5</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2609.0</td>\n",
|
||||
" <td>1.438</td>\n",
|
||||
" <td>0.670</td>\n",
|
||||
" <td>0.768</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>...</th>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>66</th>\n",
|
||||
" <td>67</td>\n",
|
||||
" <td>无烟煤</td>\n",
|
||||
" <td>0.81</td>\n",
|
||||
" <td>4.15</td>\n",
|
||||
" <td>9.77</td>\n",
|
||||
" <td>91.59</td>\n",
|
||||
" <td>3.96</td>\n",
|
||||
" <td>1.76</td>\n",
|
||||
" <td>0.21</td>\n",
|
||||
" <td>2.48</td>\n",
|
||||
" <td>800.0</td>\n",
|
||||
" <td>5.0</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3142.0</td>\n",
|
||||
" <td>1.608</td>\n",
|
||||
" <td>1.204</td>\n",
|
||||
" <td>0.404</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>67</th>\n",
|
||||
" <td>68</td>\n",
|
||||
" <td>无烟煤</td>\n",
|
||||
" <td>0.81</td>\n",
|
||||
" <td>4.15</td>\n",
|
||||
" <td>9.77</td>\n",
|
||||
" <td>91.59</td>\n",
|
||||
" <td>3.96</td>\n",
|
||||
" <td>1.76</td>\n",
|
||||
" <td>0.21</td>\n",
|
||||
" <td>2.48</td>\n",
|
||||
" <td>800.0</td>\n",
|
||||
" <td>5.0</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3389.0</td>\n",
|
||||
" <td>2.041</td>\n",
|
||||
" <td>1.022</td>\n",
|
||||
" <td>1.019</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>68</th>\n",
|
||||
" <td>69</td>\n",
|
||||
" <td>无烟煤</td>\n",
|
||||
" <td>0.88</td>\n",
|
||||
" <td>8.42</td>\n",
|
||||
" <td>8.83</td>\n",
|
||||
" <td>91.69</td>\n",
|
||||
" <td>2.31</td>\n",
|
||||
" <td>2.04</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>3.96</td>\n",
|
||||
" <td>700.0</td>\n",
|
||||
" <td>5.0</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2542.0</td>\n",
|
||||
" <td>1.135</td>\n",
|
||||
" <td>0.916</td>\n",
|
||||
" <td>0.219</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>69</th>\n",
|
||||
" <td>70</td>\n",
|
||||
" <td>无烟煤</td>\n",
|
||||
" <td>0.88</td>\n",
|
||||
" <td>8.42</td>\n",
|
||||
" <td>8.83</td>\n",
|
||||
" <td>91.69</td>\n",
|
||||
" <td>2.31</td>\n",
|
||||
" <td>2.04</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>3.96</td>\n",
|
||||
" <td>800.0</td>\n",
|
||||
" <td>5.0</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2665.0</td>\n",
|
||||
" <td>1.219</td>\n",
|
||||
" <td>0.947</td>\n",
|
||||
" <td>0.272</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>70</th>\n",
|
||||
" <td>71</td>\n",
|
||||
" <td>无烟煤</td>\n",
|
||||
" <td>0.88</td>\n",
|
||||
" <td>8.42</td>\n",
|
||||
" <td>8.83</td>\n",
|
||||
" <td>91.69</td>\n",
|
||||
" <td>2.31</td>\n",
|
||||
" <td>2.04</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>3.96</td>\n",
|
||||
" <td>900.0</td>\n",
|
||||
" <td>5.0</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2947.0</td>\n",
|
||||
" <td>1.473</td>\n",
|
||||
" <td>0.718</td>\n",
|
||||
" <td>0.755</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>71 rows × 19 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" 编号 煤种 分析水Mad 灰分 挥发分 碳 氢 氮 硫 氧 碳化温度（℃） \\\n",
|
||||
"0 1 中级烟煤 2.12 8.49 37.14 86.20 5.42 1.60 0.00 6.78 1100.0 \n",
|
||||
"1 2 萃取中级烟煤 NaN NaN NaN 75.11 4.73 1.38 0.00 18.78 1100.0 \n",
|
||||
"2 3 褐煤 14.91 4.35 48.42 67.76 4.57 1.29 3.56 22.82 650.0 \n",
|
||||
"3 4 褐煤 14.91 4.35 48.42 67.76 4.57 1.29 3.56 22.82 650.0 \n",
|
||||
"4 5 褐煤 14.91 4.35 48.42 67.76 4.57 1.29 3.56 22.82 650.0 \n",
|
||||
".. .. ... ... ... ... ... ... ... ... ... ... \n",
|
||||
"66 67 无烟煤 0.81 4.15 9.77 91.59 3.96 1.76 0.21 2.48 800.0 \n",
|
||||
"67 68 无烟煤 0.81 4.15 9.77 91.59 3.96 1.76 0.21 2.48 800.0 \n",
|
||||
"68 69 无烟煤 0.88 8.42 8.83 91.69 2.31 2.04 0.00 3.96 700.0 \n",
|
||||
"69 70 无烟煤 0.88 8.42 8.83 91.69 2.31 2.04 0.00 3.96 800.0 \n",
|
||||
"70 71 无烟煤 0.88 8.42 8.83 91.69 2.31 2.04 0.00 3.96 900.0 \n",
|
||||
"\n",
|
||||
" 升温速率（℃/min) 保温时间(h) KOH K2CO3 BET比表面积（m2/g） 孔体积（cm3/g) 微孔体积（cm3/g) \\\n",
|
||||
"0 2.0 2.0 0 0 296.0 0.270 NaN \n",
|
||||
"1 2.0 2.0 0 0 316.0 0.481 NaN \n",
|
||||
"2 10.0 0.5 1 0 665.0 0.356 0.289 \n",
|
||||
"3 10.0 0.5 1 0 1221.0 0.608 0.482 \n",
|
||||
"4 10.0 0.5 1 0 2609.0 1.438 0.670 \n",
|
||||
".. ... ... ... ... ... ... ... \n",
|
||||
"66 5.0 1.0 1 0 3142.0 1.608 1.204 \n",
|
||||
"67 5.0 1.0 1 0 3389.0 2.041 1.022 \n",
|
||||
"68 5.0 1.0 1 0 2542.0 1.135 0.916 \n",
|
||||
"69 5.0 1.0 1 0 2665.0 1.219 0.947 \n",
|
||||
"70 5.0 1.0 1 0 2947.0 1.473 0.718 \n",
|
||||
"\n",
|
||||
" 介孔体积（cm3/g) \n",
|
||||
"0 NaN \n",
|
||||
"1 NaN \n",
|
||||
"2 0.067 \n",
|
||||
"3 0.126 \n",
|
||||
"4 0.768 \n",
|
||||
".. ... \n",
|
||||
"66 0.404 \n",
|
||||
"67 1.019 \n",
|
||||
"68 0.219 \n",
|
||||
"69 0.272 \n",
|
||||
"70 0.755 \n",
|
||||
"\n",
|
||||
"[71 rows x 19 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"old_data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "ff938db8-3824-4f9b-8a0f-ae12559fbfbb",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
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|
||||
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|
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Csp(F/g)</th>\n",
|
||||
" <th>electrolyte</th>\n",
|
||||
" <th>υ(mV/s)</th>\n",
|
||||
" <th>SAmicro(m2/g)</th>\n",
|
||||
" <th>SAmeso(m2/g)</th>\n",
|
||||
" <th>O</th>\n",
|
||||
" <th>N</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>6MKOH</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>6MKOH</td>\n",
|
||||
" <td>300</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>6MKOH</td>\n",
|
||||
" <td>500</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>6MKOH</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>17.00</td>\n",
|
||||
" <td>15.60</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>6MKOH</td>\n",
|
||||
" <td>300</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>17.00</td>\n",
|
||||
" <td>15.60</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>...</th>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>283</th>\n",
|
||||
" <td>218.17</td>\n",
|
||||
" <td>1MH2SO4</td>\n",
|
||||
" <td>150</td>\n",
|
||||
" <td>1691</td>\n",
|
||||
" <td>258</td>\n",
|
||||
" <td>16.45</td>\n",
|
||||
" <td>3.31</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>284</th>\n",
|
||||
" <td>198.38</td>\n",
|
||||
" <td>1MH2SO4</td>\n",
|
||||
" <td>200</td>\n",
|
||||
" <td>1691</td>\n",
|
||||
" <td>258</td>\n",
|
||||
" <td>16.45</td>\n",
|
||||
" <td>3.31</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>285</th>\n",
|
||||
" <td>171.19</td>\n",
|
||||
" <td>1MH2SO4</td>\n",
|
||||
" <td>300</td>\n",
|
||||
" <td>1691</td>\n",
|
||||
" <td>258</td>\n",
|
||||
" <td>16.45</td>\n",
|
||||
" <td>3.31</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>286</th>\n",
|
||||
" <td>152.27</td>\n",
|
||||
" <td>1MH2SO4</td>\n",
|
||||
" <td>400</td>\n",
|
||||
" <td>1691</td>\n",
|
||||
" <td>258</td>\n",
|
||||
" <td>16.45</td>\n",
|
||||
" <td>3.31</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>287</th>\n",
|
||||
" <td>137.40</td>\n",
|
||||
" <td>1MH2SO4</td>\n",
|
||||
" <td>500</td>\n",
|
||||
" <td>1691</td>\n",
|
||||
" <td>258</td>\n",
|
||||
" <td>16.45</td>\n",
|
||||
" <td>3.31</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>288 rows × 7 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Csp(F/g) electrolyte υ(mV/s) SAmicro(m2/g) SAmeso(m2/g) O N\n",
|
||||
"0 0.00 6MKOH 1 0 0 0.00 0.00\n",
|
||||
"1 0.00 6MKOH 300 0 0 0.00 0.00\n",
|
||||
"2 0.00 6MKOH 500 0 0 0.00 0.00\n",
|
||||
"3 0.00 6MKOH 1 0 0 17.00 15.60\n",
|
||||
"4 0.00 6MKOH 300 0 0 17.00 15.60\n",
|
||||
".. ... ... ... ... ... ... ...\n",
|
||||
"283 218.17 1MH2SO4 150 1691 258 16.45 3.31\n",
|
||||
"284 198.38 1MH2SO4 200 1691 258 16.45 3.31\n",
|
||||
"285 171.19 1MH2SO4 300 1691 258 16.45 3.31\n",
|
||||
"286 152.27 1MH2SO4 400 1691 258 16.45 3.31\n",
|
||||
"287 137.40 1MH2SO4 500 1691 258 16.45 3.31\n",
|
||||
"\n",
|
||||
"[288 rows x 7 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
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|
||||
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|
||||
],
|
||||
"source": [
|
||||
"nature_data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "11ae5919-681c-4667-8c8f-bf71cde0f036",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"基于微孔介孔，推一下CHS？"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "435c980c-251f-42d5-883c-233d083df3a3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fea_cols = ['微孔体积（cm3/g)', '介孔体积（cm3/g)', '氧', '氮']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "c787ae5c-db4a-4424-ac97-fafdd60a0b5c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"out_cols = ['碳', '氢', '硫']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
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|
||||
"id": "361dce5d-3d08-4c7b-9bcf-9823a75b1f9e",
|
||||
"metadata": {},
|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
" <th>O</th>\n",
|
||||
" <th>N</th>\n",
|
||||
" <th>SAmicro(m2/g)</th>\n",
|
||||
" <th>SAmeso(m2/g)</th>\n",
|
||||
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|
||||
" </thead>\n",
|
||||
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|
||||
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||||
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|
||||
" <th>3</th>\n",
|
||||
" <td>17.00</td>\n",
|
||||
" <td>15.60</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>6</th>\n",
|
||||
" <td>8.50</td>\n",
|
||||
" <td>7.80</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>9</th>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>120</td>\n",
|
||||
" <td>216</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>13</th>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>107</td>\n",
|
||||
" <td>315</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>...</th>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>159</th>\n",
|
||||
" <td>6.25</td>\n",
|
||||
" <td>9.57</td>\n",
|
||||
" <td>640</td>\n",
|
||||
" <td>184</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>160</th>\n",
|
||||
" <td>8.49</td>\n",
|
||||
" <td>5.38</td>\n",
|
||||
" <td>563</td>\n",
|
||||
" <td>120</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>161</th>\n",
|
||||
" <td>7.84</td>\n",
|
||||
" <td>7.02</td>\n",
|
||||
" <td>680</td>\n",
|
||||
" <td>641</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>164</th>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1082</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>165</th>\n",
|
||||
" <td>14.97</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>1590</td>\n",
|
||||
" <td>1030</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>63 rows × 4 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" O N SAmicro(m2/g) SAmeso(m2/g)\n",
|
||||
"0 0.00 0.00 0 0\n",
|
||||
"3 17.00 15.60 0 0\n",
|
||||
"6 8.50 7.80 0 0\n",
|
||||
"9 0.00 0.00 120 216\n",
|
||||
"13 0.00 0.00 107 315\n",
|
||||
".. ... ... ... ...\n",
|
||||
"159 6.25 9.57 640 184\n",
|
||||
"160 8.49 5.38 563 120\n",
|
||||
"161 7.84 7.02 680 641\n",
|
||||
"164 0.00 0.00 0 1082\n",
|
||||
"165 14.97 0.00 1590 1030\n",
|
||||
"\n",
|
||||
"[63 rows x 4 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"nature_data[nature_data.electrolyte=='6MKOH'][['O', 'N', 'SAmicro(m2/g)', 'SAmeso(m2/g)']].drop_duplicates()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "101dba3e-4029-4d53-b64a-89c5a90f3471",
|
||||
"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": "3.8.16"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
|
|
@ -667,7 +667,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.16"
|
||||
"version": "3.8.18"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
|
|
@ -645,7 +645,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.16"
|
||||
"version": "3.8.18"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
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|
|
@ -1432,7 +1432,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.16"
|
||||
"version": "3.8.18"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
|
|
@ -495,8 +495,10 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"out_cols = ['挥发分Vad(%)']\n",
|
||||
"# out_cols = ['固定炭Fcad(%)']"
|
||||
"# out_cols = ['挥发分Vad(%)']\n",
|
||||
"# drop_cols = ['化验编号', '固定炭Fcad(%)']\n",
|
||||
"out_cols = ['固定炭Fcad(%)']\n",
|
||||
"drop_cols = ['挥发分Vad(%)', '化验编号']"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -508,7 +510,7 @@
|
|||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"['挥发分Vad(%)']"
|
||||
"['固定炭Fcad(%)']"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
|
|
@ -550,7 +552,7 @@
|
|||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2024-01-05 17:02:16.953831: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n"
|
||||
"2024-01-08 18:09:21.597754: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
@ -661,7 +663,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"execution_count": 17,
|
||||
"id": "80f32155-e71f-4615-8d0c-01dfd04988fe",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -684,7 +686,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"execution_count": 18,
|
||||
"id": "372011ea-9876-41eb-a4e6-83ccd6c71559",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -694,7 +696,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"execution_count": 19,
|
||||
"id": "1eebdab3-1f88-48a1-b5e0-bc8787528c1b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -709,7 +711,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"execution_count": 20,
|
||||
"id": "7f27bd56-4f6b-4242-9f79-c7d6b3ee2f13",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
|
@ -781,7 +783,7 @@
|
|||
"1 0.674897 0.794606 "
|
||||
]
|
||||
},
|
||||
"execution_count": 22,
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -792,19 +794,19 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"execution_count": 21,
|
||||
"id": "baf45a3d-dc01-44fc-9f0b-456964ac2cdb",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# feature_cols = [x for x in train_data.columns if x not in out_cols and '第二次' not in x]\n",
|
||||
"feature_cols = [x for x in train_data.columns if x not in out_cols]\n",
|
||||
"feature_cols = [x for x in train_data.columns if x not in out_cols and x not in drop_cols]\n",
|
||||
"use_cols = feature_cols + out_cols"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"execution_count": 22,
|
||||
"id": "f2d27538-d2bc-4202-b0cf-d3e0949b4686",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -816,7 +818,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"execution_count": 23,
|
||||
"id": "50daf170-efec-49e5-8f8e-9a45938cacfc",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -827,7 +829,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"execution_count": 24,
|
||||
"id": "0f863423-be12-478b-a08d-e3c6f5dfb8ee",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -839,7 +841,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"execution_count": 25,
|
||||
"id": "2c89b32a-017c-4d05-ab78-8b9b8eb0dcbb",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -850,34 +852,49 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"execution_count": 26,
|
||||
"id": "ae24eea7-7dc1-4e33-9d41-3baff07ebb88",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2024-01-08 18:09:35.590279: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n",
|
||||
"2024-01-08 18:09:35.656503: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_INVALID_DEVICE: invalid device ordinal\n",
|
||||
"2024-01-08 18:09:35.656548: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: zhaojh-yv621\n",
|
||||
"2024-01-08 18:09:35.656557: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: zhaojh-yv621\n",
|
||||
"2024-01-08 18:09:35.656758: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 520.61.5\n",
|
||||
"2024-01-08 18:09:35.656795: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 520.61.5\n",
|
||||
"2024-01-08 18:09:35.656802: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 520.61.5\n",
|
||||
"2024-01-08 18:09:35.657280: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n",
|
||||
"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Model: \"model_2\"\n",
|
||||
"Model: \"model\"\n",
|
||||
"_________________________________________________________________\n",
|
||||
"Layer (type) Output Shape Param # \n",
|
||||
"=================================================================\n",
|
||||
"input (InputLayer) [(None, 1, 7)] 0 \n",
|
||||
"input (InputLayer) [(None, 1, 5)] 0 \n",
|
||||
"_________________________________________________________________\n",
|
||||
"conv1d_3 (Conv1D) (None, 1, 64) 512 \n",
|
||||
"conv1d (Conv1D) (None, 1, 64) 384 \n",
|
||||
"_________________________________________________________________\n",
|
||||
"bidirectional_3 (Bidirection (None, 1, 128) 66048 \n",
|
||||
"bidirectional (Bidirectional (None, 1, 128) 66048 \n",
|
||||
"_________________________________________________________________\n",
|
||||
"dense_5 (Dense) (None, 1, 128) 16512 \n",
|
||||
"dense (Dense) (None, 1, 128) 16512 \n",
|
||||
"_________________________________________________________________\n",
|
||||
"dropout_3 (Dropout) (None, 1, 128) 0 \n",
|
||||
"dropout (Dropout) (None, 1, 128) 0 \n",
|
||||
"_________________________________________________________________\n",
|
||||
"dense_6 (Dense) (None, 1, 64) 8256 \n",
|
||||
"dense_1 (Dense) (None, 1, 64) 8256 \n",
|
||||
"_________________________________________________________________\n",
|
||||
"vad (Dense) (None, 1, 1) 65 \n",
|
||||
"=================================================================\n",
|
||||
"Total params: 91,393\n",
|
||||
"Trainable params: 91,393\n",
|
||||
"Total params: 91,265\n",
|
||||
"Trainable params: 91,265\n",
|
||||
"Non-trainable params: 0\n",
|
||||
"_________________________________________________________________\n"
|
||||
]
|
||||
|
|
@ -890,7 +907,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"execution_count": 27,
|
||||
"id": "ca6ce434-80b6-4609-9596-9a5120680462",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -911,7 +928,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"execution_count": 28,
|
||||
"id": "503bbec7-2020-44c8-b622-05bb41082e43",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -921,22 +938,30 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 63,
|
||||
"execution_count": 30,
|
||||
"id": "6308b1dc-8e2e-4bf9-9b28-3b81979bf7e0",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2024-01-08 18:03:50.956250: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n",
|
||||
"2024-01-08 18:03:50.974801: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2200000000 Hz\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"COL: 挥发分Vad, MSE: 2.49E-01,RMSE: 0.499,MAPE: 1.336 %,MAE: 0.398,R_2: 0.946\n",
|
||||
"COL: 挥发分Vad, MSE: 3.81E-01,RMSE: 0.617,MAPE: 1.597 %,MAE: 0.455,R_2: 0.954\n",
|
||||
"COL: 挥发分Vad, MSE: 5.71E-01,RMSE: 0.756,MAPE: 2.077 %,MAE: 0.621,R_2: 0.854\n",
|
||||
"WARNING:tensorflow:5 out of the last 45 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f00004145e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n",
|
||||
"COL: 挥发分Vad, MSE: 3.24E-01,RMSE: 0.569,MAPE: 1.575 %,MAE: 0.46,R_2: 0.943\n",
|
||||
"WARNING:tensorflow:6 out of the last 47 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f0165b81e50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n",
|
||||
"COL: 挥发分Vad, MSE: 3.13E-01,RMSE: 0.56,MAPE: 1.548 %,MAE: 0.466,R_2: 0.929\n",
|
||||
"COL: 挥发分Vad, MSE: 4.94E-01,RMSE: 0.703,MAPE: 1.852 %,MAE: 0.539,R_2: 0.898\n"
|
||||
"COL: 挥发分Vad, MSE: 5.84E-01,RMSE: 0.764,MAPE: 2.111 %,MAE: 0.633,R_2: 0.874\n",
|
||||
"COL: 挥发分Vad, MSE: 1.06E+00,RMSE: 1.028,MAPE: 2.941 %,MAE: 0.869,R_2: 0.872\n",
|
||||
"COL: 挥发分Vad, MSE: 6.70E-01,RMSE: 0.819,MAPE: 2.217 %,MAE: 0.658,R_2: 0.829\n",
|
||||
"COL: 挥发分Vad, MSE: 5.96E-01,RMSE: 0.772,MAPE: 2.07 %,MAE: 0.607,R_2: 0.896\n",
|
||||
"WARNING:tensorflow:5 out of the last 9 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f6e8d6f8940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n",
|
||||
"COL: 挥发分Vad, MSE: 8.56E-01,RMSE: 0.925,MAPE: 2.335 %,MAE: 0.717,R_2: 0.805\n",
|
||||
"WARNING:tensorflow:6 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f6e8f6e4160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n",
|
||||
"COL: 挥发分Vad, MSE: 7.24E-01,RMSE: 0.851,MAPE: 2.435 %,MAE: 0.713,R_2: 0.851\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
@ -976,20 +1001,26 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 65,
|
||||
"execution_count": null,
|
||||
"id": "f7132465-89e9-4193-829b-c6e7606cd266",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2024-01-08 18:09:42.506363: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n",
|
||||
"2024-01-08 18:09:42.522809: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2200000000 Hz\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"COL: 固定炭Fcad, MSE: 1.75E-01,RMSE: 0.419,MAPE: 0.639 %,MAE: 0.339,R_2: 0.993\n",
|
||||
"COL: 固定炭Fcad, MSE: 2.85E-01,RMSE: 0.534,MAPE: 0.822 %,MAE: 0.386,R_2: 0.994\n",
|
||||
"COL: 固定炭Fcad, MSE: 2.23E-01,RMSE: 0.472,MAPE: 0.609 %,MAE: 0.344,R_2: 0.984\n",
|
||||
"COL: 固定炭Fcad, MSE: 1.89E-01,RMSE: 0.435,MAPE: 0.662 %,MAE: 0.318,R_2: 0.994\n",
|
||||
"COL: 固定炭Fcad, MSE: 2.94E-01,RMSE: 0.542,MAPE: 0.842 %,MAE: 0.446,R_2: 0.986\n",
|
||||
"COL: 固定炭Fcad, MSE: 2.30E-01,RMSE: 0.48,MAPE: 0.741 %,MAE: 0.386,R_2: 0.99\n"
|
||||
"COL: 固定炭Fcad, MSE: 7.16E-01,RMSE: 0.846,MAPE: 1.337 %,MAE: 0.715,R_2: 0.972\n",
|
||||
"COL: 固定炭Fcad, MSE: 1.04E+00,RMSE: 1.018,MAPE: 1.65 %,MAE: 0.847,R_2: 0.98\n",
|
||||
"COL: 固定炭Fcad, MSE: 8.89E-01,RMSE: 0.943,MAPE: 1.294 %,MAE: 0.724,R_2: 0.936\n",
|
||||
"COL: 固定炭Fcad, MSE: 5.17E-01,RMSE: 0.719,MAPE: 1.066 %,MAE: 0.545,R_2: 0.985\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
@ -1025,22 +1056,22 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 66,
|
||||
"execution_count": 32,
|
||||
"id": "27e0abf7-aa29-467f-bc5e-b66a1adf6165",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"MSE 0.388723\n",
|
||||
"RMSE 0.617294\n",
|
||||
"MAE 0.489930\n",
|
||||
"MAPE 0.016641\n",
|
||||
"R_2 0.920706\n",
|
||||
"MSE 0.747816\n",
|
||||
"RMSE 0.859839\n",
|
||||
"MAE 0.699474\n",
|
||||
"MAPE 0.023513\n",
|
||||
"R_2 0.854338\n",
|
||||
"dtype: float64"
|
||||
]
|
||||
},
|
||||
"execution_count": 66,
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -1052,26 +1083,10 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 67,
|
||||
"execution_count": null,
|
||||
"id": "070cdb94-6e7b-4028-b6d5-ba8570c902ba",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"MSE 0.232791\n",
|
||||
"RMSE 0.480288\n",
|
||||
"MAE 0.369610\n",
|
||||
"MAPE 0.007189\n",
|
||||
"R_2 0.990404\n",
|
||||
"dtype: float64"
|
||||
]
|
||||
},
|
||||
"execution_count": 67,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fcad_df = pd.DataFrame.from_records(fcad_eva_list, columns=['MSE', 'RMSE', 'MAE', 'MAPE', 'R_2'])\n",
|
||||
"fcad_df.sort_values(by='R_2').mean()"
|
||||
|
|
@ -1308,7 +1323,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.16"
|
||||
"version": "3.8.18"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
|
|
@ -0,0 +1,150 @@
|
|||
,共碳化物/煤沥青,加热次数,模板剂比例,KOH与煤沥青比例,活化温度,升温速率,活化时间,共碳化物质_2-甲基咪唑,共碳化物质_三聚氰胺,共碳化物质_尿素,共碳化物质_无,共碳化物质_硫酸铵,共碳化物质_聚磷酸铵,是否有碳化过程_否,是否有碳化过程_是,模板剂种类_Al2O3,模板剂种类_TiO2,模板剂种类_α-Fe2O3,模板剂种类_γ-Fe2O3,模板剂种类_二氧化硅,模板剂种类_无,模板剂种类_氯化钾,模板剂种类_纤维素,模板剂种类_自制氢氧化镁,模板剂种类_自制氧化钙,模板剂种类_自制氧化锌,模板剂种类_自制氧化镁,模板剂种类_自制碱式碳酸镁,模板剂种类_购买氢氧化镁,模板剂种类_购买氧化钙,模板剂种类_购买氧化锌,模板剂种类_购买氧化镁,模板剂种类_购买氯化钠,模板剂种类_购买碳酸钙,混合方式_溶剂,混合方式_研磨,比表面积
|
||||
0,0.0,0.0,0.1,0.06666667,0.0,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.2734374
|
||||
1,0.0,0.0,0.1,0.033333335,0.16666667,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.28734466
|
||||
2,0.0,0.0,0.1,0.06666667,0.16666667,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.41910276
|
||||
3,0.0,0.0,0.1,0.13333334,0.16666667,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.43608063
|
||||
4,0.0,1.0,0.1,0.06666667,0.16666667,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.30766597
|
||||
5,0.0,1.0,0.1,0.06666667,0.33333334,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.26624128
|
||||
6,0.0,1.0,0.1,0.06666667,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.49787512
|
||||
7,0.0,1.0,0.1,0.033333335,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.3080297
|
||||
8,0.0,1.0,0.1,0.13333334,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.59416187
|
||||
9,0.0,1.0,0.1,0.0,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.14238557
|
||||
10,0.0,0.0,0.1,0.06666667,0.16666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.31685358
|
||||
11,0.0,0.0,0.1,0.06666667,0.16666667,0.3,0.6666667,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.39767203
|
||||
12,0.0,0.0,0.1,0.06666667,0.33333334,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.61802363
|
||||
13,0.0,1.0,0.0,0.0,0.33333334,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0020951803
|
||||
14,0.0,1.0,0.0,0.06666667,0.33333334,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.3966605
|
||||
15,0.0,1.0,0.0,0.13333334,0.33333334,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.5894941
|
||||
16,0.0,1.0,0.0,0.2,0.33333334,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.78140527
|
||||
17,0.8,0.0,0.0,1.0,0.33333334,0.3,0.33333334,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.33728373
|
||||
18,0.8,0.0,0.0,0.6666667,0.33333334,0.3,0.33333334,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.6601188
|
||||
19,0.8,0.0,0.0,0.33333334,0.33333334,0.3,0.33333334,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.4943704
|
||||
20,0.0,0.0,0.0,0.0,0.6666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.004337678
|
||||
21,0.4,0.0,0.0,0.0,0.6666667,0.3,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.14383873
|
||||
22,0.8,0.0,0.0,0.0,0.6666667,0.3,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.17430039
|
||||
23,1.0,1.0,0.0,0.26666668,0.16666667,0.3,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.35616854
|
||||
24,1.0,1.0,0.0,0.26666668,0.33333334,0.3,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.5586541
|
||||
25,1.0,1.0,0.0,0.26666668,0.5,0.3,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.6759624
|
||||
26,0.0,0.0,0.0,0.26666668,0.16666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.2079418
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27,0.0,0.0,0.6,0.13333334,0.5,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.7371931
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28,0.0,0.0,0.6,0.26666668,0.5,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.79205817
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29,0.0,0.0,0.6,0.4,0.5,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.9515005
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30,0.0,0.0,0.6,0.4,0.6666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.9314944
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31,0.0,0.0,0.0,0.13333334,0.16666667,0.8,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.27311307
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32,0.0,0.0,0.0,0.13333334,0.33333334,0.8,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.41709608
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33,0.0,0.0,0.0,0.13333334,0.5,0.8,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.60230374
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34,0.0,0.0,0.0,0.06666667,0.33333334,0.8,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.1927857
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35,0.0,0.0,0.0,0.2,0.33333334,0.8,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.50530463
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36,0.0,0.0,0.0,0.26666668,0.33333334,0.8,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.70809335
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37,0.0,1.0,0.1,0.06666667,0.33333334,0.8,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.29039103
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38,0.0,1.0,0.1,0.13333334,0.33333334,0.8,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.50227344
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39,0.0,1.0,0.1,0.2,0.33333334,0.8,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.60533494
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40,0.0,1.0,0.1,0.26666668,0.33333334,0.8,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.305244
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41,0.0,0.0,0.0,0.13333334,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.30494088
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42,0.0,0.0,0.0,0.13333334,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.2518945
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43,0.0,0.0,0.0,0.06666667,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.2270385
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44,0.0,1.0,0.1,0.0,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.15822977
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45,0.0,1.0,0.1,0.06666667,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.48438922
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46,0.0,1.0,0.1,0.13333334,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.439224
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47,0.0,0.0,0.1,0.33333334,0.5,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.92573506
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48,0.0,0.0,0.25,0.33333334,0.5,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.64655954
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49,0.0,0.0,0.4,0.33333334,0.5,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.5568354
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50,0.0,0.0,0.25,0.33333334,0.5833333,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.86207944
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51,0.0,0.0,0.5,0.5,0.16666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.28250986
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52,0.0,0.0,0.5,0.5,0.33333334,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.60533494
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53,0.0,0.0,0.5,0.5,0.5,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.74992424
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54,0.05,0.0,0.5,0.5,0.5,0.3,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.7784177
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55,0.2,0.0,0.5,0.5,0.5,0.3,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.45862383
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56,0.4,0.0,0.5,0.5,0.5,0.3,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.45589572
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57,0.05,0.0,0.5,0.5,0.5,0.3,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.7829645
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58,0.2,0.0,0.5,0.5,0.5,0.3,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.898151
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59,0.4,0.0,0.5,0.5,0.5,0.3,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.8184298
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60,0.0,0.0,0.0,0.028666666,0.68333334,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.081236735
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61,0.0,0.0,0.0,0.05733333,0.68333334,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.36586845
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62,0.0,0.0,0.0,0.09533333,0.68333334,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.45953318
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63,0.0,0.0,0.0,0.13333334,0.68333334,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.5316763
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64,0.0,0.0,0.0,0.26666668,0.5833333,0.3,0.6666667,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.5316763
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65,0.0,0.0,0.05,0.26666668,0.5833333,0.3,0.6666667,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.76689905
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66,0.0,0.0,0.1,0.26666668,0.5833333,0.3,0.6666667,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.4186117
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67,0.0,0.0,0.2,0.26666668,0.5833333,0.3,0.6666667,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.75204605
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68,0.0,1.0,0.05,0.2,0.6666667,0.3,0.16666667,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.4434374
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69,0.0,1.0,0.1,0.2,0.33333334,0.3,0.16666667,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.62612915
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70,0.0,1.0,0.0334,0.2,0.6666667,0.3,0.16666667,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.28181267
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71,0.0,1.0,0.05,0.2,0.33333334,0.3,0.16666667,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.43782964
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72,0.0,1.0,0.1,0.2,0.5,0.3,0.16666667,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.45686573
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73,0.0,0.0,0.6,0.13333334,0.5,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.58836013
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74,0.0,0.0,0.4,0.09533333,0.5,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.56138223
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75,0.0,1.0,1.0,0.0,0.33333334,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.08578357
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77,0.0,1.0,1.0,0.06666667,0.33333334,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.53682935
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78,0.0,1.0,1.0,0.06666667,0.5,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.5822977
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79,0.0,0.0,0.005,0.26666668,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.86238253
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80,0.0,0.0,0.01,0.26666668,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0
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81,0.0,0.0,0.03,0.26666668,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.62443167
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82,0.0,0.0,0.01,0.2,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.75932103
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83,0.0,0.0,0.01,0.33333334,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.63989085
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84,0.0,1.0,0.025,0.26666668,0.5833333,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.12337072
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85,0.0,1.0,0.0334,0.26666668,0.5833333,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.4131555
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86,0.0,1.0,0.05,0.26666668,0.5833333,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.44710517
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87,0.0,1.0,0.1,0.26666668,0.5833333,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.4037587
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88,0.0,0.0,0.0,0.057466667,0.6666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.30312216
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89,0.0,0.0,0.2,0.057133332,0.6666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.37859958
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90,0.0,0.0,0.4,0.09533333,0.6666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.40133375
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91,0.0,0.0,0.2,0.057133332,0.6666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.37223402
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92,0.0,0.0,0.1333,0.044466667,0.6666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.3488936
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93,0.0,0.0,0.05,0.0286,0.6666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.22885723
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94,0.0,0.0,0.2,0.0,0.6666667,0.3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.10366778
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95,0.0,0.0,0.0,0.0,0.33333334,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0024249773
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96,0.0,0.0,0.0,0.13333334,0.16666667,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.2518945
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97,0.0,0.0,0.0,0.13333334,0.33333334,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.27129433
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98,0.0,0.0,0.0,0.13333334,0.5,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.3476811
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99,0.0,0.0,0.0,0.13333334,0.6666667,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.32797816
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100,0.0,0.0,0.0,0.0,0.6666667,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0006062443
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101,0.0,0.0,0.05,0.0,0.6666667,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0051530767
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102,0.0,0.0,0.05,0.13333334,0.6666667,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.4580176
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103,0.0,0.0,0.1,0.13333334,0.6666667,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.56047285
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104,0.0,0.0,0.15,0.13333334,0.6666667,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.3886026
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105,0.0,0.0,0.05,0.13333334,0.33333334,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.4677175
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106,0.0,0.0,0.0,0.06666667,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.39735374
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107,0.0,0.0,0.0,0.13333334,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.4139618
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108,0.0,0.0,0.0,0.2,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.4243589
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109,0.0,0.0,0.0,0.06666667,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.273398
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110,0.0,0.0,0.0,0.13333334,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.45190665
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111,0.0,0.0,0.0,0.2,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.6100697
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112,0.2,0.0,0.0,0.13333334,0.5,0.3,0.33333334,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.5117975
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113,0.0666,0.0,0.0,0.13333334,0.5,0.3,0.33333334,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.8563868
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114,0.0,0.0,0.0,0.13333334,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.35588664
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115,0.6,1.0,0.0,0.2,0.5,0.3,0.33333334,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.76395875
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116,0.6,1.0,0.0,0.13333334,0.5,0.3,0.33333334,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.4960079
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117,0.6,1.0,0.0,0.0,0.5,0.3,0.33333334,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.377863
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118,0.6,0.0,1.0,0.0,0.5,0.3,0.33333334,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.4002122
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119,0.4,0.0,1.0,0.0,0.5,0.3,0.33333334,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.4308518
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120,0.2,0.0,1.0,0.0,0.5,0.3,0.33333334,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.34779024
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121,0.0,0.0,1.0,0.0,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.121076085
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122,0.0,0.0,0.0,0.0,0.5,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.01273113
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123,0.0,0.0,0.0,0.0,0.6666667,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0069718095
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124,0.0,0.0,0.0,0.0,0.8333333,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0036374659
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|
|
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|
@ -0,0 +1,185 @@
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真实值,预测值
|
||||
1266.95,1199.6592
|
||||
354,789.75793
|
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3322,3201.8806
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2160,3126.631
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3047.5,2449.849
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903,1056.5598
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614.13,1013.1533
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1446,1225.9176
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1348.4,1590.5961
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1040,1234.5201
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311,297.95325
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73.6,639.59326
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954,1214.2247
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1361,1062.7231
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971,1086.9929
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3054.9,3225.5757
|
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3103,3214.4526
|
||||
1772,2002.6473
|
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630.3,957.1507
|
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2511,1427.715
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1476,1056.5598
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2342,3201.8806
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1451,1322.3773
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2318,1377.8091
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1694,1248.5482
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2582.7,1710.3027
|
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2384,2432.3953
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1841.49,1914.1414
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794,1315.9065
|
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1107,1056.5598
|
||||
920,1403.9894
|
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0.09,0.48767585
|
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0.669,0.68562025
|
||||
1.106,1.2752197
|
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0.41,0.42102402
|
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0.593,0.48638818
|
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0.307,0.39488634
|
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0.06,0.16702902
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0.2,0.21776177
|
||||
0.17,0.19962527
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0.42,0.28074315
|
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0.86,0.63029045
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0.634,1.0294249
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0.356,0.45718768
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1.786,1.8230422
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1.755,1.9801164
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1.949,1.4029663
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1.26,1.0357726
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0.498,0.24582684
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1.08,1.1989071
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0.7383,1.021262
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1.608,1.4073043
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0.841,0.5150561
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0.638,0.5172716
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0.88,0.8333097
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0.487,0.57212603
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0.495,0.48414204
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0.229,0.31693947
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0.31,0.19502579
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0.42,0.19324148
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0.16,0.19648847
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0.67,0.6929045
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0.72,0.7718477
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0.7281,0.59599847
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0.09,0.5033262
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1.35,1.2466724
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1.06,0.86608654
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1.18,1.2867856
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1.683,1.4816906
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1.339,1.1426073
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2.65,1.8023566
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1.83,1.7278047
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1.352,1.2030903
|
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2.041,1.5949395
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1.248,1.2068672
|
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1.24,1.1895779
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0.1,0.10356517
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||||
0.17,0.18440628
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0.621,0.6660887
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||||
0.669,0.5801821
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0.736,0.66076094
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0.42,0.48471504
|
||||
0.18,0.17535393
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0.46,0.71197385
|
||||
0.55,0.427174
|
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0.88,1.0949665
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1.13,1.1181045
|
||||
1.28,1.0548297
|
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1.113,0.7645609
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1.551,1.374725
|
||||
1.191,1.3679994
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1.164,1.1463214
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0.225,0.5057385
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0.828,0.5885821
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2.18,2.047648
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1.037,0.73976415
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1.229,1.2479352
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0.803,1.2242457
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1.185,1.2479352
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0.537,0.73469305
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0.524,0.6406121
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0.874,1.0285063
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1.09,1.2665594
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0.52,0.56475025
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0.496,0.40291283
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0.583,0.39963228
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0.75,0.96787745
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0.69,0.7121689
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0.74,0.7348132
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0.78,0.67922235
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2.76,1.9579074
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2.17,1.9579074
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0.5917,0.73892397
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0.6371,0.75767624
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0.4793,0.6173721
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1.201,1.2096013
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1.321,1.4694718
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1.146,0.88004696
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1.083,0.9984938
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0.356,0.9329532
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0.08,0.58885336
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0.46,0.59819096
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0.41,0.3972643
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0.38,0.29991606
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0.519,0.54503036
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0.393,0.356785
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0.15,0.12743953
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0.72,0.64135486
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0.78,0.6668498
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0.3,0.21079245
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0.63,0.76027185
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0.6693,0.6887063
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0.8436,0.7761461
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0.381,0.46610036
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1.305,1.3265989
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1.31,1.2111275
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1.438,1.4289135
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0.909,0.9882558
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1.442,1.1672359
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1.75,1.8152558
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1.282,0.7182266
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0.06,0.06908494
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0.226,0.47602454
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0.559,0.7597236
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0.737,0.70830667
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0.765,0.8051188
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0.25,0.36717495
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0.55,0.4684503
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0.566,0.577461
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0.448,0.3919188
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0.66,0.8014552
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0.71,0.78170377
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0.93,0.772743
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0.67,0.6187317
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0.32,0.48071888
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0.6552,0.8981791
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1.333,1.2834239
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1.964,2.22443
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1.311,1.4879532
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1.061,1.1750947
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1.38,1.0879021
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0.556,0.56562763
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0.53,0.5445734
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1.57,1.3463788
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0.485,0.65084183
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0.564,0.52309275
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0.484,0.46505818
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0.27,0.4495849
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0.57,0.49448618
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0.68,0.57854456
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0.78,0.7710798
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0.95,0.9177168
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0.54,0.6080524
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0.6831,0.75551635
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1.29,1.1946983
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1.353,1.312073
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2.211,2.025374
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761
旧数据建模.ipynb
761
旧数据建模.ipynb
|
|
@ -1,761 +0,0 @@
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|||
{
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"cells": [
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{
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"id": "e2fb2c7b-89ca-4e2b-aa44-19403cef590a",
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"source": [
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{
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"metadata": {},
|
||||
"outputs": [],
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"source": [
|
||||
"old_data = pd.read_excel('./data/煤质碳材料数据.xlsx')"
|
||||
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},
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{
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"cell_type": "code",
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"metadata": {},
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||||
"outputs": [],
|
||||
"source": [
|
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"nature_data = pd.read_excel('./data/nature.xlsx')"
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||||
]
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" <td>9.77</td>\n",
|
||||
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|
||||
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|
||||
" <td>1.76</td>\n",
|
||||
" <td>0.21</td>\n",
|
||||
" <td>2.48</td>\n",
|
||||
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|
||||
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|
||||
" <td>1.0</td>\n",
|
||||
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|
||||
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|
||||
" <td>3142.0</td>\n",
|
||||
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|
||||
" <td>1.204</td>\n",
|
||||
" <td>0.404</td>\n",
|
||||
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|
||||
" <tr>\n",
|
||||
" <th>67</th>\n",
|
||||
" <td>68</td>\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" <td>0</td>\n",
|
||||
" <td>3389.0</td>\n",
|
||||
" <td>2.041</td>\n",
|
||||
" <td>1.022</td>\n",
|
||||
" <td>1.019</td>\n",
|
||||
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|
||||
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|
||||
" <th>68</th>\n",
|
||||
" <td>69</td>\n",
|
||||
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|
||||
" <td>0.88</td>\n",
|
||||
" <td>8.42</td>\n",
|
||||
" <td>8.83</td>\n",
|
||||
" <td>91.69</td>\n",
|
||||
" <td>2.31</td>\n",
|
||||
" <td>2.04</td>\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" <td>1.135</td>\n",
|
||||
" <td>0.916</td>\n",
|
||||
" <td>0.219</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>69</th>\n",
|
||||
" <td>70</td>\n",
|
||||
" <td>无烟煤</td>\n",
|
||||
" <td>0.88</td>\n",
|
||||
" <td>8.42</td>\n",
|
||||
" <td>8.83</td>\n",
|
||||
" <td>91.69</td>\n",
|
||||
" <td>2.31</td>\n",
|
||||
" <td>2.04</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>3.96</td>\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" <td>2.04</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>3.96</td>\n",
|
||||
" <td>900.0</td>\n",
|
||||
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|
||||
" <td>1.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2947.0</td>\n",
|
||||
" <td>1.473</td>\n",
|
||||
" <td>0.718</td>\n",
|
||||
" <td>0.755</td>\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
"text/plain": [
|
||||
" 编号 煤种 分析水Mad 灰分 挥发分 碳 氢 氮 硫 氧 碳化温度（℃） \\\n",
|
||||
"0 1 中级烟煤 2.12 8.49 37.14 86.20 5.42 1.60 0.00 6.78 1100.0 \n",
|
||||
"1 2 萃取中级烟煤 NaN NaN NaN 75.11 4.73 1.38 0.00 18.78 1100.0 \n",
|
||||
"2 3 褐煤 14.91 4.35 48.42 67.76 4.57 1.29 3.56 22.82 650.0 \n",
|
||||
"3 4 褐煤 14.91 4.35 48.42 67.76 4.57 1.29 3.56 22.82 650.0 \n",
|
||||
"4 5 褐煤 14.91 4.35 48.42 67.76 4.57 1.29 3.56 22.82 650.0 \n",
|
||||
".. .. ... ... ... ... ... ... ... ... ... ... \n",
|
||||
"66 67 无烟煤 0.81 4.15 9.77 91.59 3.96 1.76 0.21 2.48 800.0 \n",
|
||||
"67 68 无烟煤 0.81 4.15 9.77 91.59 3.96 1.76 0.21 2.48 800.0 \n",
|
||||
"68 69 无烟煤 0.88 8.42 8.83 91.69 2.31 2.04 0.00 3.96 700.0 \n",
|
||||
"69 70 无烟煤 0.88 8.42 8.83 91.69 2.31 2.04 0.00 3.96 800.0 \n",
|
||||
"70 71 无烟煤 0.88 8.42 8.83 91.69 2.31 2.04 0.00 3.96 900.0 \n",
|
||||
"\n",
|
||||
" 升温速率（℃/min) 保温时间(h) KOH K2CO3 BET比表面积（m2/g） 孔体积（cm3/g) 微孔体积（cm3/g) \\\n",
|
||||
"0 2.0 2.0 0 0 296.0 0.270 NaN \n",
|
||||
"1 2.0 2.0 0 0 316.0 0.481 NaN \n",
|
||||
"2 10.0 0.5 1 0 665.0 0.356 0.289 \n",
|
||||
"3 10.0 0.5 1 0 1221.0 0.608 0.482 \n",
|
||||
"4 10.0 0.5 1 0 2609.0 1.438 0.670 \n",
|
||||
".. ... ... ... ... ... ... ... \n",
|
||||
"66 5.0 1.0 1 0 3142.0 1.608 1.204 \n",
|
||||
"67 5.0 1.0 1 0 3389.0 2.041 1.022 \n",
|
||||
"68 5.0 1.0 1 0 2542.0 1.135 0.916 \n",
|
||||
"69 5.0 1.0 1 0 2665.0 1.219 0.947 \n",
|
||||
"70 5.0 1.0 1 0 2947.0 1.473 0.718 \n",
|
||||
"\n",
|
||||
" 介孔体积（cm3/g) \n",
|
||||
"0 NaN \n",
|
||||
"1 NaN \n",
|
||||
"2 0.067 \n",
|
||||
"3 0.126 \n",
|
||||
"4 0.768 \n",
|
||||
".. ... \n",
|
||||
"66 0.404 \n",
|
||||
"67 1.019 \n",
|
||||
"68 0.219 \n",
|
||||
"69 0.272 \n",
|
||||
"70 0.755 \n",
|
||||
"\n",
|
||||
"[71 rows x 19 columns]"
|
||||
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|
||||
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|
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|
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>6MKOH</td>\n",
|
||||
" <td>300</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>6MKOH</td>\n",
|
||||
" <td>500</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>6MKOH</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>17.00</td>\n",
|
||||
" <td>15.60</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>6MKOH</td>\n",
|
||||
" <td>300</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>17.00</td>\n",
|
||||
" <td>15.60</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>...</th>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>283</th>\n",
|
||||
" <td>218.17</td>\n",
|
||||
" <td>1MH2SO4</td>\n",
|
||||
" <td>150</td>\n",
|
||||
" <td>1691</td>\n",
|
||||
" <td>258</td>\n",
|
||||
" <td>16.45</td>\n",
|
||||
" <td>3.31</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>284</th>\n",
|
||||
" <td>198.38</td>\n",
|
||||
" <td>1MH2SO4</td>\n",
|
||||
" <td>200</td>\n",
|
||||
" <td>1691</td>\n",
|
||||
" <td>258</td>\n",
|
||||
" <td>16.45</td>\n",
|
||||
" <td>3.31</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>285</th>\n",
|
||||
" <td>171.19</td>\n",
|
||||
" <td>1MH2SO4</td>\n",
|
||||
" <td>300</td>\n",
|
||||
" <td>1691</td>\n",
|
||||
" <td>258</td>\n",
|
||||
" <td>16.45</td>\n",
|
||||
" <td>3.31</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>286</th>\n",
|
||||
" <td>152.27</td>\n",
|
||||
" <td>1MH2SO4</td>\n",
|
||||
" <td>400</td>\n",
|
||||
" <td>1691</td>\n",
|
||||
" <td>258</td>\n",
|
||||
" <td>16.45</td>\n",
|
||||
" <td>3.31</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>287</th>\n",
|
||||
" <td>137.40</td>\n",
|
||||
" <td>1MH2SO4</td>\n",
|
||||
" <td>500</td>\n",
|
||||
" <td>1691</td>\n",
|
||||
" <td>258</td>\n",
|
||||
" <td>16.45</td>\n",
|
||||
" <td>3.31</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>288 rows × 7 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Csp(F/g) electrolyte υ(mV/s) SAmicro(m2/g) SAmeso(m2/g) O N\n",
|
||||
"0 0.00 6MKOH 1 0 0 0.00 0.00\n",
|
||||
"1 0.00 6MKOH 300 0 0 0.00 0.00\n",
|
||||
"2 0.00 6MKOH 500 0 0 0.00 0.00\n",
|
||||
"3 0.00 6MKOH 1 0 0 17.00 15.60\n",
|
||||
"4 0.00 6MKOH 300 0 0 17.00 15.60\n",
|
||||
".. ... ... ... ... ... ... ...\n",
|
||||
"283 218.17 1MH2SO4 150 1691 258 16.45 3.31\n",
|
||||
"284 198.38 1MH2SO4 200 1691 258 16.45 3.31\n",
|
||||
"285 171.19 1MH2SO4 300 1691 258 16.45 3.31\n",
|
||||
"286 152.27 1MH2SO4 400 1691 258 16.45 3.31\n",
|
||||
"287 137.40 1MH2SO4 500 1691 258 16.45 3.31\n",
|
||||
"\n",
|
||||
"[288 rows x 7 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"nature_data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "11ae5919-681c-4667-8c8f-bf71cde0f036",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"基于微孔介孔，推一下CHS？"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "435c980c-251f-42d5-883c-233d083df3a3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fea_cols = ['微孔体积（cm3/g)', '介孔体积（cm3/g)', '氧', '氮']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "c787ae5c-db4a-4424-ac97-fafdd60a0b5c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"out_cols = ['碳', '氢', '硫']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "361dce5d-3d08-4c7b-9bcf-9823a75b1f9e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"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>O</th>\n",
|
||||
" <th>N</th>\n",
|
||||
" <th>SAmicro(m2/g)</th>\n",
|
||||
" <th>SAmeso(m2/g)</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>17.00</td>\n",
|
||||
" <td>15.60</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>6</th>\n",
|
||||
" <td>8.50</td>\n",
|
||||
" <td>7.80</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>9</th>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>120</td>\n",
|
||||
" <td>216</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>13</th>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>107</td>\n",
|
||||
" <td>315</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>...</th>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>159</th>\n",
|
||||
" <td>6.25</td>\n",
|
||||
" <td>9.57</td>\n",
|
||||
" <td>640</td>\n",
|
||||
" <td>184</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>160</th>\n",
|
||||
" <td>8.49</td>\n",
|
||||
" <td>5.38</td>\n",
|
||||
" <td>563</td>\n",
|
||||
" <td>120</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>161</th>\n",
|
||||
" <td>7.84</td>\n",
|
||||
" <td>7.02</td>\n",
|
||||
" <td>680</td>\n",
|
||||
" <td>641</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>164</th>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1082</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>165</th>\n",
|
||||
" <td>14.97</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>1590</td>\n",
|
||||
" <td>1030</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>63 rows × 4 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" O N SAmicro(m2/g) SAmeso(m2/g)\n",
|
||||
"0 0.00 0.00 0 0\n",
|
||||
"3 17.00 15.60 0 0\n",
|
||||
"6 8.50 7.80 0 0\n",
|
||||
"9 0.00 0.00 120 216\n",
|
||||
"13 0.00 0.00 107 315\n",
|
||||
".. ... ... ... ...\n",
|
||||
"159 6.25 9.57 640 184\n",
|
||||
"160 8.49 5.38 563 120\n",
|
||||
"161 7.84 7.02 680 641\n",
|
||||
"164 0.00 0.00 0 1082\n",
|
||||
"165 14.97 0.00 1590 1030\n",
|
||||
"\n",
|
||||
"[63 rows x 4 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"nature_data[nature_data.electrolyte=='6MKOH'][['O', 'N', 'SAmicro(m2/g)', 'SAmeso(m2/g)']].drop_duplicates()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "101dba3e-4029-4d53-b64a-89c5a90f3471",
|
||||
"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": "3.8.16"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Loading…
Reference in New Issue