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refactor: change naming and add global readme

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Aaron
2026-04-28 15:34:05 +02:00
parent 25a1acd0b1
commit 56c764d71d
16 changed files with 29 additions and 0 deletions
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ML/unterlagen/06_Kmeans_Iris.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import datasets\n",
"iris = datasets.load_iris()\n",
"\n",
"from sklearn.cluster import KMeans\n",
"from sklearn import metrics"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
" 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
" 2 2]\n"
]
}
],
"source": [
"print(iris.target)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"KMeans(algorithm='auto', copy_x=True, init='random', max_iter=300,\n",
" n_clusters=3, n_init=1, n_jobs=None, precompute_distances='auto',\n",
" random_state=None, tol=0.0001, verbose=0)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kmeans = KMeans(n_clusters=3, init='random', n_init=1)\n",
"kmeans.fit(iris.data)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (1, 0), (1, 1), (1, 0), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 0), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (2, 0), (2, 1), (2, 0), (2, 0), (2, 0), (2, 0), (2, 1), (2, 0), (2, 0), (2, 0), (2, 0), (2, 0), (2, 0), (2, 1), (2, 1), (2, 0), (2, 0), (2, 0), (2, 0), (2, 1), (2, 0), (2, 1), (2, 0), (2, 1), (2, 0), (2, 0), (2, 1), (2, 1), (2, 0), (2, 0), (2, 0), (2, 0), (2, 0), (2, 1), (2, 0), (2, 0), (2, 0), (2, 0), (2, 1), (2, 0), (2, 0), (2, 0), (2, 1), (2, 0), (2, 0), (2, 0), (2, 1), (2, 0), (2, 0), (2, 1)]\n"
]
}
],
"source": [
"print(list(zip(iris.target,kmeans.labels_)))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.7364192881252849\n",
"0.7474865805095326\n",
"0.7163421126838475\n",
"0.5511916046195915\n"
]
}
],
"source": [
"print(metrics.homogeneity_score(iris.target, kmeans.labels_))\n",
"print(metrics.completeness_score(iris.target, kmeans.labels_))\n",
"print(metrics.adjusted_rand_score(iris.target, kmeans.labels_))\n",
"print(metrics.silhouette_score(iris.data, kmeans.labels_))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"KMeans(algorithm='auto', copy_x=True, init='random', max_iter=300,\n",
" n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
" random_state=None, tol=0.0001, verbose=0)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kmeans_init = KMeans(n_clusters=3, init='random', n_init=10)\n",
"kmeans_init.fit(iris.data)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (1, 0), (1, 1), (1, 0), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 0), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (2, 0), (2, 1), (2, 0), (2, 0), (2, 0), (2, 0), (2, 1), (2, 0), (2, 0), (2, 0), (2, 0), (2, 0), (2, 0), (2, 1), (2, 1), (2, 0), (2, 0), (2, 0), (2, 0), (2, 1), (2, 0), (2, 1), (2, 0), (2, 1), (2, 0), (2, 0), (2, 1), (2, 1), (2, 0), (2, 0), (2, 0), (2, 0), (2, 0), (2, 1), (2, 0), (2, 0), (2, 0), (2, 0), (2, 1), (2, 0), (2, 0), (2, 0), (2, 1), (2, 0), (2, 0), (2, 0), (2, 1), (2, 0), (2, 0), (2, 1)]\n"
]
}
],
"source": [
"print(list(zip(iris.target,kmeans.labels_)))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.7514854021988338\n",
"0.7649861514489815\n",
"0.7302382722834697\n",
"0.5528190123564091\n"
]
}
],
"source": [
"print(metrics.homogeneity_score(iris.target, kmeans_init.labels_))\n",
"print(metrics.completeness_score(iris.target, kmeans_init.labels_))\n",
"print(metrics.adjusted_rand_score(iris.target, kmeans_init.labels_))\n",
"print(metrics.silhouette_score(iris.data, kmeans_init.labels_))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
" n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
" random_state=None, tol=0.0001, verbose=0)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kmeans2 = KMeans(n_clusters=3)\n",
"kmeans2.fit(iris.data)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (0, 2), (1, 0), (1, 1), (1, 0), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 0), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (2, 0), (2, 1), (2, 0), (2, 0), (2, 0), (2, 0), (2, 1), (2, 0), (2, 0), (2, 0), (2, 0), (2, 0), (2, 0), (2, 1), (2, 1), (2, 0), (2, 0), (2, 0), (2, 0), (2, 1), (2, 0), (2, 1), (2, 0), (2, 1), (2, 0), (2, 0), (2, 1), (2, 1), (2, 0), (2, 0), (2, 0), (2, 0), (2, 0), (2, 1), (2, 0), (2, 0), (2, 0), (2, 0), (2, 1), (2, 0), (2, 0), (2, 0), (2, 1), (2, 0), (2, 0), (2, 0), (2, 1), (2, 0), (2, 0), (2, 1)]\n"
]
}
],
"source": [
"print(list(zip(iris.target,kmeans.labels_)))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.7514854021988338\n",
"0.7649861514489815\n",
"0.7302382722834697\n",
"0.5528190123564091\n"
]
}
],
"source": [
"print(metrics.homogeneity_score(iris.target, kmeans2.labels_))\n",
"print(metrics.completeness_score(iris.target, kmeans2.labels_))\n",
"print(metrics.adjusted_rand_score(iris.target, kmeans2.labels_))\n",
"print(metrics.silhouette_score(iris.data, kmeans2.labels_))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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