fin TP3 SVM

TP1_prog2.py.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "id": "530f620c",
    "metadata": {},
    "outputs": [],
@@ -16,12 +16,13 @@
     "from matplotlib import pyplot as plt\n",
     "from sklearn.model_selection import KFold\n",
     "import time\n",
-    "import statistics"
+    "import statistics\n",
+    "from sklearn import metrics"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "68b6a517",
    "metadata": {},
    "outputs": [],
@@ -863,11 +864,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "98107e41",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Matrice de confusion K-NN :\n",
+      " [[51  0  0  0  0  1  0  0  0  0]\n",
+      " [ 0 56  0  0  0  0  0  0  0  0]\n",
+      " [ 3  1 45  1  0  0  1  1  0  0]\n",
+      " [ 0  1  1 35  0  1  0  1  1  1]\n",
+      " [ 0  3  0  0 48  0  0  0  0  2]\n",
+      " [ 0  1  0  1  0 38  0  0  0  0]\n",
+      " [ 0  0  0  0  0  2 44  0  0  0]\n",
+      " [ 0  2  0  0  3  0  0 47  0  0]\n",
+      " [ 2  0  0  0  0  3  1  0 42  2]\n",
+      " [ 0  0  0  0  4  1  0  1  2 50]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Create vector of 5000 random indexes\n",
+    "rand_indexes = np.random.randint(70000, size=5000)\n",
+    "### Load data with the previous vector\n",
+    "data = mnist.data[rand_indexes]\n",
+    "# print(\"Dataset : \", data)\n",
+    "target = mnist.target[rand_indexes]\n",
+    "\n",
+    "# Split the dataset\n",
+    "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
+    "\n",
+    "# Training on xtrain,ytrain\n",
+    "clf = neighbors.KNeighborsClassifier(n_neighbors=3,p=2,n_jobs=1)\n",
+    "clf.fit(xtrain, ytrain)\n",
+    "# Predicting on xtest\n",
+    "pred = clf.predict(xtest)\n",
+    "print(\"Matrice de confusion K-NN :\\n\", metrics.confusion_matrix(ytest, pred))"
+   ]
   },
   {
    "cell_type": "code",
@@ -894,7 +930,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,