From b37614600a09b0fbfbdaed81b9320113b857d47e Mon Sep 17 00:00:00 2001 From: Arthur DANJOU Date: Wed, 19 Mar 2025 12:01:24 +0100 Subject: [PATCH] fix: TP2 --- M1/Statistical Learning/TP2_KNN.ipynb | 106 +++++++++++++------------- 1 file changed, 52 insertions(+), 54 deletions(-) diff --git a/M1/Statistical Learning/TP2_KNN.ipynb b/M1/Statistical Learning/TP2_KNN.ipynb index f8423c3..e092724 100644 --- a/M1/Statistical Learning/TP2_KNN.ipynb +++ b/M1/Statistical Learning/TP2_KNN.ipynb @@ -62,13 +62,13 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T16:32:17.846897Z", - "start_time": "2025-02-07T16:32:17.807782Z" + "end_time": "2025-03-19T11:00:08.826606Z", + "start_time": "2025-03-19T11:00:08.811426Z" } }, "source": "import numpy as np", "outputs": [], - "execution_count": 1 + "execution_count": 7 }, { "metadata": { @@ -167,8 +167,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T16:32:18.557770Z", - "start_time": "2025-02-07T16:32:17.940064Z" + "end_time": "2025-03-19T11:00:12.701811Z", + "start_time": "2025-03-19T11:00:12.595101Z" } }, "source": [ @@ -179,7 +179,7 @@ "y = iris.target" ], "outputs": [], - "execution_count": 3 + "execution_count": 8 }, { "cell_type": "markdown", @@ -1244,8 +1244,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T16:32:19.559521Z", - "start_time": "2025-02-07T16:32:19.282596Z" + "end_time": "2025-03-19T11:00:20.853667Z", + "start_time": "2025-03-19T11:00:20.714184Z" } }, "source": [ @@ -1255,7 +1255,7 @@ "from sklearn.neighbors import KNeighborsClassifier" ], "outputs": [], - "execution_count": 22 + "execution_count": 9 }, { "cell_type": "code", @@ -2128,8 +2128,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T16:33:33.791102Z", - "start_time": "2025-02-07T16:33:20.708161Z" + "end_time": "2025-03-19T11:00:47.008627Z", + "start_time": "2025-03-19T11:00:45.853619Z" } }, "source": [ @@ -2138,14 +2138,14 @@ "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.cifar10.load_data()" ], "outputs": [], - "execution_count": 39 + "execution_count": 16 }, { "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T16:33:33.819221Z", - "start_time": "2025-02-07T16:33:33.816280Z" + "end_time": "2025-03-19T11:00:47.016892Z", + "start_time": "2025-03-19T11:00:47.014283Z" } }, "source": [ @@ -2158,19 +2158,19 @@ "numpy.ndarray" ] }, - "execution_count": 40, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 40 + "execution_count": 17 }, { "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T16:33:33.847663Z", - "start_time": "2025-02-07T16:33:33.844954Z" + "end_time": "2025-03-19T11:00:47.097843Z", + "start_time": "2025-03-19T11:00:47.094336Z" } }, "source": [ @@ -2183,12 +2183,12 @@ "((50000, 32, 32, 3), (50000, 1))" ] }, - "execution_count": 41, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 41 + "execution_count": 18 }, { "cell_type": "markdown", @@ -2208,14 +2208,13 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T16:33:33.879318Z", - "start_time": "2025-02-07T16:33:33.875930Z" + "end_time": "2025-03-19T11:00:51.610775Z", + "start_time": "2025-03-19T11:00:51.607269Z" } }, "source": [ "y = y_train.ravel()\n", - "y.shape\n", - "\n" + "y.shape" ], "outputs": [ { @@ -2224,19 +2223,19 @@ "(50000,)" ] }, - "execution_count": 42, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 42 + "execution_count": 20 }, { "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T16:33:33.910196Z", - "start_time": "2025-02-07T16:33:33.904771Z" + "end_time": "2025-03-19T11:00:52.188886Z", + "start_time": "2025-03-19T11:00:52.185299Z" } }, "source": [ @@ -2249,19 +2248,19 @@ "array([6, 9, 9, 4, 1, 1, 2, 7, 8, 3], dtype=uint8)" ] }, - "execution_count": 43, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 43 + "execution_count": 21 }, { "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T16:33:33.980676Z", - "start_time": "2025-02-07T16:33:33.935546Z" + "end_time": "2025-03-19T11:00:55.705062Z", + "start_time": "2025-03-19T11:00:55.617852Z" } }, "source": [ @@ -2275,20 +2274,20 @@ "text/plain": [ "
" ], - "image/png": "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" + "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 44 + "execution_count": 22 }, { "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T16:33:34.007006Z", - "start_time": "2025-02-07T16:33:34.004187Z" + "end_time": "2025-03-19T11:00:59.717372Z", + "start_time": "2025-03-19T11:00:59.714673Z" } }, "source": [ @@ -2304,7 +2303,7 @@ ] } ], - "execution_count": 45 + "execution_count": 24 }, { "cell_type": "markdown", @@ -2317,8 +2316,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T16:33:34.260397Z", - "start_time": "2025-02-07T16:33:34.032213Z" + "end_time": "2025-03-19T11:01:02.017921Z", + "start_time": "2025-03-19T11:01:01.759177Z" } }, "source": [ @@ -2332,12 +2331,12 @@ "(50000, 3072)" ] }, - "execution_count": 46, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 46 + "execution_count": 25 }, { "cell_type": "markdown", @@ -2353,9 +2352,11 @@ { "cell_type": "code", "metadata": { + "jupyter": { + "is_executing": true + }, "ExecuteTime": { - "end_time": "2025-02-07T16:33:50.494818Z", - "start_time": "2025-02-07T16:33:34.284326Z" + "start_time": "2025-03-19T11:01:02.886276Z" } }, "source": [ @@ -2373,19 +2374,16 @@ "text": [ "(10000, 3072) (10000,)\n" ] - }, - { - "data": { - "text/plain": [ - "0.3303" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" } ], - "execution_count": 47 + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "" } ], "metadata": {