Added comments

ann.ipynb

@@ -1,6 +1,14 @@
 {
  "cells": [
   {
+   "cell_type": "markdown",
+   "id": "4d162f18",
+   "metadata": {},
+   "source": [
+    "## Import du dataset"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 1,
    "id": "861d1252",
@@ -24,6 +32,14 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "id": "95e0ce45",
+   "metadata": {},
+   "source": [
+    "## Echantillonnage du dataset"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 4,
    "id": "91e6919c",
@@ -34,10 +50,15 @@
     "data = mnist.data.values[echantillon]\n",
     "target = mnist.target[echantillon]\n",
     "trainSize = 17500/25000\n",
-    "xtrain,  xtest,  ytrain,  ytest  =  train_test_split(data,  target, train_size=trainSize)\n",
-    "#trainSize = 49000/len(data)\n",
-    "#xtrain,  xtest,  ytrain,  ytest  =  train_test_split(data,  target, \n",
-    "#train_size=trainSize)"
+    "xtrain,  xtest,  ytrain,  ytest  =  train_test_split(data,  target, train_size=trainSize)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f35a2a10",
+   "metadata": {},
+   "source": [
+    "## Premier entraînement et scores"
    ]
   },
   {
@@ -111,6 +132,14 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "id": "d5d7d4c1",
+   "metadata": {},
+   "source": [
+    "## Nombre de couches cachées optimal"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 14,
    "id": "44b6cf69",
@@ -150,6 +179,14 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "id": "e4b13f21",
+   "metadata": {},
+   "source": [
+    "## Nombre de couches cachées et de neurones par couche optimaux"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 46,
    "id": "7a889d9e",
@@ -232,6 +269,14 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "id": "11adbe03",
+   "metadata": {},
+   "source": [
+    "## Algorithme d'optimisation optimal"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 47,
    "id": "4356994c",
@@ -371,6 +416,14 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "id": "cf7095a5",
+   "metadata": {},
+   "source": [
+    "## Fonction d'activation optimale"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 5,
    "id": "11f49da3",
@@ -518,6 +571,14 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "id": "569f6393",
+   "metadata": {},
+   "source": [
+    "## Valeur optimale d'alpha"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 62,
    "id": "e568c62e",
@@ -632,6 +693,14 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "id": "c46d042a",
+   "metadata": {},
+   "source": [
+    "## Matrice de confusion"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 6,
    "id": "9fab621a",

knn.ipynb

@@ -1,9 +1,17 @@
 {
  "cells": [
   {
+   "cell_type": "markdown",
+   "id": "87214e92",
+   "metadata": {},
+   "source": [
+    "## Import du dataset MNIST"
+   ]
+  },
+  {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "b4978985",
+   "execution_count": null,
+   "id": "c2e6fa5e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,6 +20,14 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "id": "a36d996c",
+   "metadata": {},
+   "source": [
+    "## Manpulation du jeu de données"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 8,
    "id": "30516666",
@@ -704,6 +720,14 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "id": "73194d9b",
+   "metadata": {},
+   "source": [
+    "## Premier entraînement"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 45,
    "id": "1992f78a",
@@ -748,6 +772,14 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "id": "d7d80cd2",
+   "metadata": {},
+   "source": [
+    "## Vérification de la prédiction faite avec l'algorithme entraîné"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 68,
    "id": "de3692e9",
@@ -782,6 +814,14 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "id": "8eb362f6",
+   "metadata": {},
+   "source": [
+    "## Scores obtenus"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 71,
    "id": "5ad97acb",
@@ -806,6 +846,14 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "id": "aac45aac",
+   "metadata": {},
+   "source": [
+    "## Nombre de voisins optimal"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 3,
    "id": "29bae5b3",
@@ -861,6 +909,14 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "id": "411f6841",
+   "metadata": {},
+   "source": [
+    "## Séparation optimale entre l'entraînement et le test "
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 18,
    "id": "aa975189",
@@ -925,6 +981,14 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "id": "b3563ad5",
+   "metadata": {},
+   "source": [
+    "## Echantillonnage optimal du dataset"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 20,
    "id": "551cdf20",
@@ -986,6 +1050,14 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "id": "e387a386",
+   "metadata": {},
+   "source": [
+    "## Distance optimale"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 25,
    "id": "6b7b2aec",
@@ -1056,6 +1128,14 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "id": "c863ff41",
+   "metadata": {},
+   "source": [
+    "## Meilleure valeur de n_jobs "
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 31,
    "id": "6575833c",
@@ -1130,6 +1210,14 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "id": "1a26da41",
+   "metadata": {},
+   "source": [
+    "## Matrice de confusion"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 7,
    "id": "94d331a4",

svm.ipynb

@@ -1,6 +1,14 @@
 {
  "cells": [
   {
+   "cell_type": "markdown",
+   "id": "557db042",
+   "metadata": {},
+   "source": [
+    "## Import du dataset"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 15,
    "id": "f574b533",
@@ -12,6 +20,14 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "id": "2295fc5b",
+   "metadata": {},
+   "source": [
+    "## Premier entraînement et scores"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 16,
    "id": "18715d1a",
@@ -73,6 +89,14 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "id": "32caafa6",
+   "metadata": {},
+   "source": [
+    "## Meilleur kernel à utiliser"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 7,
    "id": "57f74503",
@@ -196,6 +220,8 @@
     }
    ],
    "source": [
+    "## Il est nécessaire de donner à l'algorithme en entrée une matrice carrée. \n",
+    "## Ne sachant pas comment faire nous avons décidé de ne pas prendre en compte ce type. \n",
     "start = time.time()\n",
     "clsvm = SVC(kernel='precomputed')\n",
     "clsvm.fit(xtrain, ytrain) \n",
@@ -244,6 +270,14 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "id": "218f2c18",
+   "metadata": {},
+   "source": [
+    "## Valeur optimale du paramètre C"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 8,
    "id": "79cc2019",
@@ -305,6 +339,14 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "id": "2ddd41c7",
+   "metadata": {},
+   "source": [
+    "## Matrice de confusion"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 19,
    "id": "7a7ecd10",
@@ -339,7 +381,7 @@
   {
    "cell_type": "code",
    "execution_count": 58,
-   "id": "14cd493b",
+   "id": "7ea957d9",
    "metadata": {},
    "outputs": [
     {