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129 lines
2.5 KiB
Plaintext
129 lines
2.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Cours D3\n",
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"\n",
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"## Cours Méthodes d'analyse non supervisées\n",
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"\n",
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"Exemple de clustering complete linkage :\n",
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"On prend 4 singletons avec leur matrice de dissimilarité."
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np"
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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": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[0. 0.3 0.4 0.8 ]\n",
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" [0.3 0. 0.5 0.8 ]\n",
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" [0.4 0.5 0. 0.45]\n",
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" [0.8 0.8 0.45 0. ]]\n"
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]
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}
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],
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"source": [
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"D = np.array([[0, 0.3, 0.4, 0.7],[0, 0, 0.5, 0.8],[0, 0, 0, 0.45],[0, 0, 0, 0]])\n",
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"D += D.T\n",
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"print(D)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"On link les 2 clusters les plus proches (a,b) donc on prend le max de différence entre (a,b) et c et d."
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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": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[0. 0.5 0.8 ]\n",
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" [0.5 0. 0.45]\n",
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" [0.8 0.45 0. ]]\n"
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]
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}
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],
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"source": [
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"D2 = np.array([[0, 0.5, 0.8],[0.5, 0, 0.45],[0.8, 0.45, 0]])\n",
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"print(D2)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"On coupe au saut le plus important sur le dendrogramme (on continu jusqu'a avoir K-clusters)"
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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": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[0. 0.8]\n",
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" [0.8 0. ]]\n"
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]
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}
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],
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"source": [
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"# (a,b,c) et d\n",
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"D3 = np.array([[0, 0.8],[0.8,0]])\n",
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"print(D3)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.8.10 64-bit",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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": "3.8.10"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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