{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# K-means examples\n",
"\n",
"***"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Numerical arrays.\n",
"import numpy as np\n",
"\n",
"# Machine learning - KMeans.\n",
"import sklearn.cluster as skcl\n",
"\n",
"# Plotting.\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"\n",
"#### From the sklearn documentation\n",
"\n",
"[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans)\n",
"\n",
"***"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Data set.\n",
"X = np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(-2.0, 6.0)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plot the data set.\n",
"plt.plot(X[:,0], X[:,1], 'x')\n",
"# Set reasonable limits.\n",
"plt.xlim([-2,14])\n",
"plt.ylim([-2,6])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Perform kmeans fitting.\n",
"kmeans = skcl.KMeans(n_clusters=2, random_state=0).fit(X)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 1, 0, 0, 0])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# See the labels of the points.\n",
"kmeans.labels_"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(-2.0, 6.0)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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7i4ifSPpn9Z7J7ZL0vxHxeL1THdQHI2KX1HtiIunkmuc5HIPUoz1sXy3pJxHx/FLut6yRt/1kf99w//+u2ec2N6u39XDvcs6yBF7guoE9z9T2+yR9Q9LnI+KtuufZn+2rJO2OiG11z3IIqyVdIGkyIs6X9EsNxtbCPP097WskDUtaK2mN7U/VO1UeA9gjSZLtEyTdLOmWpd635BuUHWCFviXCDkmn7XN5nQbkr8P7s32ceoG/NyIeqHueRVwi6WrbH5d0vKQTbd8TEYMWph2SdkTE3N+GpjSAkZd0haSZiJiVJNsPSPoDSffUOtXi/sf2KRGxy/YpknbXPdBiBrRHc35XvT/Yn7ct9br0rO0LI+KnB7tjnWfXzL0lwtUD9pYI05LOtD1s+z3q/aPWwzXPdAD3/k/fIWl7RHyl7nkWExE3RcS6iDhdvV/L7wxg4NX/jfKG7bl3+btc0is1jrSYH0u6yPYJ/cfA5RrAfyDex8OSru9/f72kh2qcZVED3CNJUkS8GBEnR8Tp/d9LOyRdcKjAS/XuyR/pWyIsq/4/vvyVpG+p95unHREv1zvVgi6R9BeSLuv/+j3Xf7aMI/c5SffafkHSRyT9Y73jHKj/N40pSc9KelG938MD8ZJ82/dJ+p6ks2zvsP0ZSV+W9FHbP1TvrJAv1zmjtOicA9WjRWY8srUG728lAIBSBuXsGgDAMiDyAJAYkQeAxIg8ACRG5AEgMSIPAIkReQBI7P8BQfK09BdC8uEAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plot the data set.\n",
"plt.plot(X[kmeans.labels_ == 0][:,0], X[kmeans.labels_ == 0][:,1], 'gx')\n",
"plt.plot(X[kmeans.labels_ == 1][:,0], X[kmeans.labels_ == 1][:,1], 'rx')\n",
"# Set reasonable limits.\n",
"plt.xlim([-2,14])\n",
"plt.ylim([-2,6])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 0])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Predict the cluster for two points.\n",
"newvals = np.array([[0, 0], [12, 3]])\n",
"predictions = kmeans.predict(newvals)\n",
"predictions"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(-2.0, 6.0)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plot the data set.\n",
"plt.plot(X[kmeans.labels_ == 0][:,0], X[kmeans.labels_ == 0][:,1], 'gx')\n",
"plt.plot(X[kmeans.labels_ == 1][:,0], X[kmeans.labels_ == 1][:,1], 'rx')\n",
"plt.plot(newvals[:,0], newvals[:,1], 'bo')\n",
"# Set reasonable limits.\n",
"plt.xlim([-2,14])\n",
"plt.ylim([-2,6])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(-2.0, 6.0)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plot the data set.\n",
"plt.plot(X[kmeans.labels_ == 0][:,0], X[kmeans.labels_ == 0][:,1], 'gx')\n",
"plt.plot(X[kmeans.labels_ == 1][:,0], X[kmeans.labels_ == 1][:,1], 'rx')\n",
"plt.plot(newvals[predictions == 0][:,0], newvals[predictions == 0][:,1], 'go')\n",
"plt.plot(newvals[predictions == 1][:,0], newvals[predictions == 1][:,1], 'ro')\n",
"# Set reasonable limits.\n",
"plt.xlim([-2,14])\n",
"plt.ylim([-2,6])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[10., 2.],\n",
" [ 1., 2.]])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cent = kmeans.cluster_centers_\n",
"cent"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(-2.0, 6.0)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plot the data set.\n",
"plt.plot(X[kmeans.labels_ == 0][:,0], X[kmeans.labels_ == 0][:,1], 'gx')\n",
"plt.plot(X[kmeans.labels_ == 1][:,0], X[kmeans.labels_ == 1][:,1], 'rx')\n",
"plt.plot(newvals[predictions == 0][:,0], newvals[predictions == 0][:,1], 'go')\n",
"plt.plot(newvals[predictions == 1][:,0], newvals[predictions == 1][:,1], 'ro')\n",
"plt.plot(cent[:,0], cent[:,1], 'k.')\n",
"# Set reasonable limits.\n",
"plt.xlim([-2,14])\n",
"plt.ylim([-2,6])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"\n",
"#### My own dataset\n",
"\n",
"***"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[-0.60350908, 0.80600677],\n",
" [ 5.79766518, 10.527845 ],\n",
" [ 2.93118138, -2.27208718],\n",
" [ 6.25648216, 12.45203601],\n",
" [ 5.79633889, 8.5543678 ],\n",
" [ 6.59360945, 9.0939594 ],\n",
" [ 0.10517133, 2.36381443],\n",
" [ 2.30112082, 0.29770193],\n",
" [ 2.48206805, 11.06734928],\n",
" [ 4.02188676, 16.24798429],\n",
" [ 2.92692029, -0.29308455],\n",
" [ 2.70377258, 4.6306797 ],\n",
" [-0.25069006, -0.30494735],\n",
" [-0.4425126 , 3.44875223],\n",
" [ 2.27559788, 5.89615065],\n",
" [ 5.24168866, 14.26724582],\n",
" [-4.02183793, 1.87040957],\n",
" [-1.48560208, 2.56505284],\n",
" [ 2.3880615 , 13.0276387 ],\n",
" [ 4.94471303, 11.85252002]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Data set.\n",
"\n",
"# Two centre points.\n",
"c1 = np.array([1.0, 2.0])\n",
"c2 = np.array([5.0, 12.0])\n",
"\n",
"# Create points randomly around the centre points.\n",
"c1x = np.random.normal(c1[0], 2.0, 10)\n",
"c1y = np.random.normal(c1[1], 2.0, 10)\n",
"c1p = np.vstack([c1x, c1y]).T\n",
"c2x = np.random.normal(c2[0], 2.0, 10)\n",
"c2y = np.random.normal(c2[1], 2.0, 10)\n",
"c2p = np.vstack([c2x, c2y]).T\n",
"\n",
"# Merge the two lists of values.\n",
"myX = np.concatenate([c1p, c2p])\n",
"# Shuffle the points.\n",
"np.random.shuffle(myX)\n",
"\n",
"myX"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plot the data set.\n",
"plt.plot(myX[:,0], myX[:,1], 'x')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# Perform kmeans fitting.\n",
"mykmeans = skcl.KMeans(n_clusters=2, random_state=0).fit(myX)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plot the data set.\n",
"plt.plot(myX[mykmeans.labels_ == 0][:,0], myX[mykmeans.labels_ == 0][:,1], 'gx')\n",
"plt.plot(myX[mykmeans.labels_ == 1][:,0], myX[mykmeans.labels_ == 1][:,1], 'rx')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[-1. , -1. ],\n",
" [-0.11111111, 0.77777778],\n",
" [ 0.77777778, 2.55555556],\n",
" [ 1.66666667, 4.33333333],\n",
" [ 2.55555556, 6.11111111],\n",
" [ 3.44444444, 7.88888889],\n",
" [ 4.33333333, 9.66666667],\n",
" [ 5.22222222, 11.44444444],\n",
" [ 6.11111111, 13.22222222],\n",
" [ 7. , 15. ]])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create new dummy points for classification.\n",
"# mynewvals = np.array([[0, 0], [6, 10]])\n",
"myxvals = np.linspace(-1.0, 7.0, 10)\n",
"myyvals = np.linspace(-1.0, 15.0, 10)\n",
"mynewvals = np.vstack([myxvals, myyvals]).T\n",
"mynewvals"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Predict the cluster for two points.\n",
"mypredictions = mykmeans.predict(mynewvals)\n",
"mypredictions"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plot the data set.\n",
"plt.plot(myX[mykmeans.labels_ == 0][:,0], myX[mykmeans.labels_ == 0][:,1], 'gx')\n",
"plt.plot(myX[mykmeans.labels_ == 1][:,0], myX[mykmeans.labels_ == 1][:,1], 'rx')\n",
"plt.plot(mynewvals[mypredictions == 0][:,0], mynewvals[mypredictions == 0][:,1], 'go')\n",
"plt.plot(mynewvals[mypredictions == 1][:,0], mynewvals[mypredictions == 1][:,1], 'ro')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.58541932, 1.72804082],\n",
" [ 4.83583485, 11.89899404]])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The centres of clusters.\n",
"mycent = mykmeans.cluster_centers_\n",
"mycent"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plot the data set.\n",
"plt.plot(myX[mykmeans.labels_ == 0][:,0], myX[mykmeans.labels_ == 0][:,1], 'gx')\n",
"plt.plot(myX[mykmeans.labels_ == 1][:,0], myX[mykmeans.labels_ == 1][:,1], 'rx')\n",
"plt.plot(mynewvals[mypredictions == 0][:,0], mynewvals[mypredictions == 0][:,1], 'go')\n",
"plt.plot(mynewvals[mypredictions == 1][:,0], mynewvals[mypredictions == 1][:,1], 'ro')\n",
"plt.plot(mycent[:,0], mycent[:,1], 'k.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***\n",
"\n",
"### End"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}