From ffe265e3f8f0c7d5563ed979eadf13d3317baead Mon Sep 17 00:00:00 2001 From: 0e57e1b8e1a23d7aaeee34c5821213f6 <0e57e1b8e1a23d7aaeee34c5821213f6@app-learninglab.inria.fr> Date: Mon, 24 May 2021 13:29:56 +0000 Subject: [PATCH] amelioration1 --- module2/exo5/exo5_fr.ipynb | 363 +++++++++++++++++++++---------------- 1 file changed, 203 insertions(+), 160 deletions(-) diff --git a/module2/exo5/exo5_fr.ipynb b/module2/exo5/exo5_fr.ipynb index 1624b5e..9beb862 100644 --- a/module2/exo5/exo5_fr.ipynb +++ b/module2/exo5/exo5_fr.ipynb @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -261,33 +261,33 @@ "" ], "text/plain": [ - " Date Count Temperature Pressure Malfunction\n", - "0 4/12/81 6 66 50 0\n", - "1 11/12/81 6 70 50 1\n", - "2 3/22/82 6 69 50 0\n", - "3 11/11/82 6 68 50 0\n", - "4 4/04/83 6 67 50 0\n", - "5 6/18/82 6 72 50 0\n", - "6 8/30/83 6 73 100 0\n", - "7 11/28/83 6 70 100 0\n", - "8 2/03/84 6 57 200 1\n", - "9 4/06/84 6 63 200 1\n", - "10 8/30/84 6 70 200 1\n", - "11 10/05/84 6 78 200 0\n", - "12 11/08/84 6 67 200 0\n", - "13 1/24/85 6 53 200 2\n", - "14 4/12/85 6 67 200 0\n", - "15 4/29/85 6 75 200 0\n", - "16 6/17/85 6 70 200 0\n", - "17 7/29/85 6 81 200 0\n", - "18 8/27/85 6 76 200 0\n", - "19 10/03/85 6 79 200 0\n", - "20 10/30/85 6 75 200 2\n", - "21 11/26/85 6 76 200 0\n", - "22 1/12/86 6 58 200 1" + " Date Count Temperature Pressure Malfunction\n", + "0 4/12/81 6 66 50 0\n", + "1 11/12/81 6 70 50 1\n", + "2 3/22/82 6 69 50 0\n", + "3 11/11/82 6 68 50 0\n", + "4 4/04/83 6 67 50 0\n", + "5 6/18/82 6 72 50 0\n", + "6 8/30/83 6 73 100 0\n", + "7 11/28/83 6 70 100 0\n", + "8 2/03/84 6 57 200 1\n", + "9 4/06/84 6 63 200 1\n", + "10 8/30/84 6 70 200 1\n", + "11 10/05/84 6 78 200 0\n", + "12 11/08/84 6 67 200 0\n", + "13 1/24/85 6 53 200 2\n", + "14 4/12/85 6 67 200 0\n", + "15 4/29/85 6 75 200 0\n", + "16 6/17/85 6 70 200 0\n", + "17 7/29/85 6 81 200 0\n", + "18 8/27/85 6 76 200 0\n", + "19 10/03/85 6 79 200 0\n", + "20 10/30/85 6 75 200 2\n", + "21 11/26/85 6 76 200 0\n", + "22 1/12/86 6 58 200 1" ] }, - "execution_count": 1, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -295,6 +295,7 @@ "source": [ "import numpy as np\n", "import pandas as pd\n", + "import seaborn as sns\n", "data = pd.read_csv(\"shuttle.csv\")\n", "data" ] @@ -329,138 +330,33 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
DateCountTemperaturePressureMalfunction
111/12/81670501
82/03/846572001
94/06/846632001
108/30/846702001
131/24/856532002
2010/30/856752002
221/12/866582001
\n", - "
" - ], - "text/plain": [ - " Date Count Temperature Pressure Malfunction\n", - "1 11/12/81 6 70 50 1\n", - "8 2/03/84 6 57 200 1\n", - "9 4/06/84 6 63 200 1\n", - "10 8/30/84 6 70 200 1\n", - "13 1/24/85 6 53 200 2\n", - "20 10/30/85 6 75 200 2\n", - "22 1/12/86 6 58 200 1" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "data = data[data.Malfunction>0]\n", - "data" + "#data = data[data.Malfunction>0]\n", + "#data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Très bien, nous avons une variabilité de température importante mais\n", + "*Très bien, nous avons une variabilité de température importante mais\n", "la pression est quasiment toujours égale à 200, ce qui devrait\n", - "simplifier l'analyse.\n", + "simplifier l'analyse.*\n", "\n", - "Comment la fréquence d'échecs varie-t-elle avec la température ?\n" + "*Comment la fréquence d'échecs varie-t-elle avec la température ?*\n" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -507,7 +403,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -516,10 +412,10 @@ "\n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", " \n", @@ -528,16 +424,16 @@ " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "
Generalized Linear Model Regression Results
Dep. Variable: Frequency No. Observations: 7Dep. Variable: Frequency No. Observations: 23
Model: GLM Df Residuals: 5Model: GLM Df Residuals: 21
Model Family: Binomial Df Model: 1Link Function: logit Scale: 1.0000
Method: IRLS Log-Likelihood: -2.5250Method: IRLS Log-Likelihood: -3.9210
Date: Sat, 13 Apr 2019 Deviance: 0.22231Date: Mon, 24 May 2021 Deviance: 3.0144
Time: 19:11:24 Pearson chi2: 0.236Time: 13:26:19 Pearson chi2: 5.00
No. Iterations: 4 Covariance Type: nonrobustNo. Iterations: 6 Covariance Type: nonrobust
\n", "\n", @@ -545,10 +441,10 @@ " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "
coef std err z P>|z| [0.025 0.975]
Intercept -1.3895 7.828 -0.178 0.859 -16.732 13.953Intercept 5.0850 7.477 0.680 0.496 -9.570 19.740
Temperature 0.0014 0.122 0.012 0.991 -0.238 0.240Temperature -0.1156 0.115 -1.004 0.316 -0.341 0.110
" ], @@ -557,24 +453,24 @@ "\"\"\"\n", " Generalized Linear Model Regression Results \n", "==============================================================================\n", - "Dep. Variable: Frequency No. Observations: 7\n", - "Model: GLM Df Residuals: 5\n", + "Dep. Variable: Frequency No. Observations: 23\n", + "Model: GLM Df Residuals: 21\n", "Model Family: Binomial Df Model: 1\n", "Link Function: logit Scale: 1.0000\n", - "Method: IRLS Log-Likelihood: -2.5250\n", - "Date: Sat, 13 Apr 2019 Deviance: 0.22231\n", - "Time: 19:11:24 Pearson chi2: 0.236\n", - "No. Iterations: 4 Covariance Type: nonrobust\n", + "Method: IRLS Log-Likelihood: -3.9210\n", + "Date: Mon, 24 May 2021 Deviance: 3.0144\n", + "Time: 13:26:19 Pearson chi2: 5.00\n", + "No. Iterations: 6 Covariance Type: nonrobust\n", "===============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "-------------------------------------------------------------------------------\n", - "Intercept -1.3895 7.828 -0.178 0.859 -16.732 13.953\n", - "Temperature 0.0014 0.122 0.012 0.991 -0.238 0.240\n", + "Intercept 5.0850 7.477 0.680 0.496 -9.570 19.740\n", + "Temperature -0.1156 0.115 -1.004 0.316 -0.341 0.110\n", "===============================================================================\n", "\"\"\"" ] }, - "execution_count": 4, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -600,6 +496,100 @@ "estimations avec des pincettes.\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "L'estimateur le plus probable du paramètre de temperature este de -0,1156 avec une erreur standard de 0,115. \n", + "ce resultat est assez conforme avec l'etude de *Dalal et al.*. Neanmoins la deviance associe au 21 degre de liberte est tres differente et je ne retourve pas la deviance de 18 mentionnee dans l'etude. Il semble que cet ecart provienne du fait que je ne donne par le nombre de replication de l'experience pour chaque etat.\n", + "voyons cela ...\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Generalized Linear Model Regression Results
Dep. Variable: Frequency No. Observations: 23
Model: GLM Df Residuals: 21
Model Family: Binomial Df Model: 1
Link Function: logit Scale: 1.0000
Method: IRLS Log-Likelihood: -23.526
Date: Mon, 24 May 2021 Deviance: 18.086
Time: 13:26:24 Pearson chi2: 30.0
No. Iterations: 6 Covariance Type: nonrobust
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
Intercept 5.0850 3.052 1.666 0.096 -0.898 11.068
Temperature -0.1156 0.047 -2.458 0.014 -0.208 -0.023
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Generalized Linear Model Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Frequency No. Observations: 23\n", + "Model: GLM Df Residuals: 21\n", + "Model Family: Binomial Df Model: 1\n", + "Link Function: logit Scale: 1.0000\n", + "Method: IRLS Log-Likelihood: -23.526\n", + "Date: Mon, 24 May 2021 Deviance: 18.086\n", + "Time: 13:26:24 Pearson chi2: 30.0\n", + "No. Iterations: 6 Covariance Type: nonrobust\n", + "===============================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "-------------------------------------------------------------------------------\n", + "Intercept 5.0850 3.052 1.666 0.096 -0.898 11.068\n", + "Temperature -0.1156 0.047 -2.458 0.014 -0.208 -0.023\n", + "===============================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import statsmodels.api as sm\n", + "\n", + "data[\"Success\"]=data.Count-data.Malfunction\n", + "data[\"Intercept\"]=1\n", + "\n", + "logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], family=sm.families.Binomial(sm.families.links.logit),var_weights=data['Count']).fit()\n", + "\n", + "logmodel.summary()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -612,12 +602,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { - "image/png": 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5KgqSDz7YzSHPPLnf9RSHS1tfE2awbHy57KYS1ErUSNTUQI1ErYQENTUdt0WNoLZmb7u9j8nuqylaRo0K5teI2hoxvOPxUr4sAFFbky1b+bq7PK5omWv68HSUFfSSLgC+B9QCd0fEXxbd/3VgPtAKvAH8SUT8qg/19Nk+r/qljgAKjhpKvcruEzzFO0vnEUL2uP0dtaz/TSvvvbRjnzCIotvdBl3Rq3rbfu7b35HBPvOLQ6rLkcq+Ry7dHYE1N3/AyFVPdd7X2ZcSwVNYb9fneyD/Qnpp3UsH9PDC8FBhQOThURwEtTXZziyU7/D7Pr62RojC8MkeO6y2hkOG7Q2cjmDqWEbn8vL17vztbzn6qCP3CaWOdvvczutUPr+wPqlrqGX9UNewLFFDTan11AhREKI1RcuVgL3PRZdw7QjfGvHsM88we/YZBbUL1RQ9FwXPrfLlVotb+vCYHoNeUi1wB3Ae0ASskrQsIjYWNFsDnBoRH0j6r8BfAVfsb7nbm9s596//uXQQlArtff716Ro8Q9Lavrz2ltbxh93lFb9oR+g86ug4AqnpLmz2hkoWGnuPHrJ1lA6O2jwUJHizpoWjfv/wLjt58Tpqa9Sl9pJHPwV9KWyX9TN7LHmfawtrrum6jNqa7LkR+4ZNjYAuobdvUBTWvnrV85x+2qx9gqXrkV/R9ig6EhzK4dHY2EhDw8mDXUa/+djIGj4+ZuRglzGklHNEPxPYGhGvAkh6ELgY6Az6iHiqoP2zwBd6WujwGlH/e2M6d8CSQdBNKOzdWfNQoGvoFR/xFP6bpOLbRUc1xUddxQGyN/T2hlvx0UiNxIurVzNr1oyCYN7fkR37HAWpKJiGmiwspg12Gf2iaXQNnxg3erDLMKuYcoJ+AvB6wXQTMGs/7b8E/GOpOyQtABYAjB8/nssmvFdmmX0Q+U8FteU/5RirD2ja+EJlCxhCmpubaWxsHOwy+kXKfQP372BUTtCXOpwsGaGSvgCcCvxhqfsjYjGwGKC+vj4aGhrKq7IKZUe8DYNdRr9JuX8p9w3cv4NROUHfBBxTMD0R2FHcSNK5wP8E/jAiPqxMeWZmdqDK+cDUKmCKpMmSRgBXAssKG0iaBtwFzI2InZUv08zM+qrHoI+IVuBaYAWwCVgSERsk3Sxpbt5sEXAo8BNJayUt62ZxZmY2wMq6jj4ilgPLi+Z9q+D2uRWuy6zXlq7ZzqIVm9nxTgtHH17Hwjn1APvMu2TahAGtoT/X1xvfXLqeB557na+dsIcv3bCcebOO4ZZLThzssmwA+JOxloSla7ZzwyPradmTXRe1/Z0WFv7kJRDsaYvOeTc8sh6gX8K3VA39ub7e+ObS9fzw2dc6p9siOqcd9unzoGaWhEUrNncGbIc97dEZ8h1a9rSxaMXmAauhP9fXGw8893qv5ltaHPSWhB3vtPRL20rU0F/r6422bsab6G6+pcVBb0k4+vC6fmlbiRr6a329UdvNp6u7m29pcdBbEhbOqadueG2XecNrxPDarkFWN7y2803agaihP9fXG/NmHdOr+ZYWvxlrSeh4s3Mwr7rprobBfiMW9r7h2nFOvlbyVTcHEQe9JeOSaRNKhupABm13NQwFt1xyIrdcciKNjY3821UNg12ODSCfujEzS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBJXVtBLukDSZklbJX2jxP2HSHoov/85SZMqXaiZmfVNj0EvqRa4A7gQOB6YJ+n4omZfAt6OiH8HfBf4X5Uu1MzM+qacI/qZwNaIeDUiPgIeBC4uanMx8A/57YeBz0hS5co0M7O+GlZGmwnA6wXTTcCs7tpERKukd4FxwJuFjSQtABbkkx9KerkvRVeJIynqf2JS7l/KfQP3r9rV9/YB5QR9qSPz6EMbImIxsBhA0uqIOLWM9Vcl9696pdw3cP+qnaTVvX1MOadumoBjCqYnAju6ayNpGDAW+F1vizEzs8orJ+hXAVMkTZY0ArgSWFbUZhnwx/ntS4F/ioh9jujNzGzg9XjqJj/nfi2wAqgF7o2IDZJuBlZHxDLgHuB+SVvJjuSvLGPdiw+g7mrg/lWvlPsG7l+163X/5ANvM7O0+ZOxZmaJc9CbmSVuQIJe0khJz0t6SdIGSd/O50/Oh0zYkg+hMGIg6ukPkmolrZH0s3w6pb5tk7Re0tqOS7skHSHp8bx/j0v62GDX2VeSDpf0sKT/J2mTpNNT6Z+k+ny7dfy8J+lrCfXvv+WZ8rKkB/KsSWnf+/O8bxskfS2f1+ttN1BH9B8C50TEycBU4AJJp5ENlfDdiJgCvE02lEK1+nNgU8F0Sn0DODsiphZcn/wN4Mm8f0/m09Xqe8DPI+I44GSy7ZhE/yJic77dpgKnAB8Aj5JA/yRNAK4DTo2IE8guFrmSRPY9SScAXyYbneBk4HOSptCXbRcRA/oDjAJeJPt07ZvAsHz+6cCKga6nQn2amD/h5wA/I/sAWRJ9y+vfBhxZNG8zcFR++yhg82DX2ce+HQb8kvzChNT6V9Sn84GnU+kfez+RfwTZFYQ/A+aksu8BlwF3F0z/BfA/+rLtBuwcfX5qYy2wE3gc+DfgnYhozZs0kW24anQ72QZoz6fHkU7fIPuU80pJL+TDWAD8XkT8GiD//fFBq+7AfBJ4A/j7/NTb3ZJGk07/Cl0JPJDfrvr+RcR24DbgNeDXwLvAC6Sz770MnCVpnKRRwGfJPpja6203YEEfEW2R/fs4kexfkU+XajZQ9VSKpM8BOyPihcLZJZpWXd8KzI6I6WQjmP6ZpLMGu6AKGgZMB+6MiGnA+1ThaYye5Oep5wI/GexaKiU/N30xMBk4GhhN9jdarCr3vYjYRHYa6nHg58BLQOt+H9SNAb/qJiLeARqB04DD8yEToPTQCtVgNjBX0jaykT3PITvCT6FvAETEjvz3TrLzuzOB30o6CiD/vXPwKjwgTUBTRDyXTz9MFvyp9K/DhcCLEfHbfDqF/p0L/DIi3oiIPcAjwBmkte/dExHTI+Issg+jbqEP226grroZL+nw/HYd2QbaBDxFNmQCZEMoPDYQ9VRSRNwQERMjYhLZv8b/FBFXkUDfACSNljSm4zbZed6X6TrsRdX2LyJ+A7wuqWNEwM8AG0mkfwXmsfe0DaTRv9eA0ySNyodF79h2Sex7AJI+nv8+FviPZNuw19tuQD4ZK+kksvHqa8leXJZExM2SPkl2FHwEsAb4QkR82O8F9RNJDcD1EfG5VPqW9+PRfHIY8OOIuFXSOGAJcCzZDndZRFTlQHaSpgJ3AyOAV4Evkv+dkkb/RpG9afnJiHg3n5fE9ssv1b6C7JTGGmA+2Tn5qt/3ACT9C9l7fnuAr0fEk33Zdh4Cwcwscf5krJlZ4hz0ZmaJc9CbmSXOQW9mljgHvZlZ4sr5cnCzAZVfPvZkPvn7QBvZMAUAMyPio0EpbD8k/QmwPL8u32xI8eWVNqRJuglojojbhkAttRHR1s19/wpcGxFre7G8YQVjspj1G5+6saoi6Y+VfbfBWkl/I6lG0jBJ70haJOlFSSskzZL0z5JelfTZ/LHzJT2a379Z0jfLXO4tkp4HZkr6tqRV+Rjhf6vMFWTDbz+UP36EpKaCT4OfJumJ/PYtku6S9DjZQGrDJP11vu51kuYP/LNqqXPQW9XIx+f+PHBGPkDeMPZ+Ef1YYGU++NpHwE1kH4m/DLi5YDEz88dMB/6zpKllLPfFiJgZEc8A34uIGcCJ+X0XRMRDwFrgisjGfu/p1NI04KKI+CNgAdmgeDOBGWSDxh3bl+fHrDs+R2/V5FyyMFydDW1CHdlH+wFaIuLx/PZ64N2IaJW0HphUsIwVEfE2gKSlwH8g2w+6W+5H7B0CAuAzkhYCI4EjyYbF/cde9uOxiNid3z4f+LSkwheWKWQfbTerCAe9VRMB90bEX3SZmY1UWHgU3U72rWYdtwv/zovflIoeltsS+RtZ+Zgx/xeYHhHbJd1CFviltLL3P+biNu8X9elPI+JJzPqJT91YNXkCuFzSkZBdndOH0xznK/uO2FFkY5k/3Yvl1pG9cLyZj+j5nwru2wWMKZjeRvbVfRS1K7YC+NOOYXWVfcdrXS/7ZLZfPqK3qhER6/PRCp+QVEM2ot9/oXfjjf8r8GPgD4D7O66SKWe5EfGWpH8gG6b5V8BzBXf/PXC3pBay9wFuAv5O0m+A5/dTz11koxCuzU8b7SR7ATKrGF9eaQeN/IqWEyLia4Ndi9lA8qkbM7PE+YjezCxxPqI3M0ucg97MLHEOejOzxDnozcwS56A3M0vc/wcowwoTqhaBUgAAAABJRU5ErkJggg==\n", 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\n", 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" ] @@ -637,6 +627,59 @@ "plt.grid(True)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il y'a bien une incidence de la temperature avec un risque de panne estimatif de \\>0,81 pour une unetemerature de 31F.Cepdnant je ne connais pas l'intervale de confiance de ce que j'ai dans ce modèle statistique. Ce aui me derange c'est que je suis tres loin des condtions de mesures. Aussi il est difficile de verifier la conordance du modele ave le phenomene physique réel. Ca donne pas envie de monter dans la navette..." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", + " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.set(color_codes=True)\n", + "plt.xlim(30,90)\n", + "plt.ylim(0,1)\n", + "sns.regplot(x='Temperature', y='Frequency', data=data, logistic=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Mon interval de confiance varie entre 0 et 1 ce aui confirme que mon modèle n'est pas fiable. Je ne peux pas considerer les elements de sortie du modèle." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": { -- 2.18.1