abinashpanda/K-Means Cluster Python Sklearn.ipynb

## K-Means Cluster Python Sklearn.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "points = 10*np.random.rand(25,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0,25):\n",
    "    plt.scatter(points[i][0], points[i][1]) # Before Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans1 = KMeans(n_clusters = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=4, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans1.fit(points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "centroids1 = kmeans1.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels1 = kmeans1.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 1, 0, 1, 3, 3, 0, 2, 0, 0, 3, 1, 1, 2, 3, 1, 0, 2, 1, 3, 1,\n",
       "       2, 2, 2])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = [\"g. \",\"r. \" , \"c. \" , \"y. \"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "co-ordinate: [3.60952588 8.58957246] label: 3\n",
      "co-ordinate: [6.54835512 6.7343301 ] label: 1\n",
      "co-ordinate: [8.12319473 4.66492978] label: 1\n",
      "co-ordinate: [1.31086289 6.41261099] label: 0\n",
      "co-ordinate: [9.09795888 4.93287961] label: 1\n",
      "co-ordinate: [4.56202665 8.12287641] label: 3\n",
      "co-ordinate: [4.92459186 7.49944154] label: 3\n",
      "co-ordinate: [1.2028035  5.18322524] label: 0\n",
      "co-ordinate: [2.49644391 1.9373279 ] label: 2\n",
      "co-ordinate: [2.93104155 4.27709535] label: 0\n",
      "co-ordinate: [0.09363295 2.85667175] label: 0\n",
      "co-ordinate: [3.28561178 8.66664471] label: 3\n",
      "co-ordinate: [7.74234896 2.87303577] label: 1\n",
      "co-ordinate: [9.36721951 4.50443105] label: 1\n",
      "co-ordinate: [4.33231866 3.53099133] label: 2\n",
      "co-ordinate: [0.74125591 9.0120095 ] label: 3\n",
      "co-ordinate: [7.75008105 3.01727241] label: 1\n",
      "co-ordinate: [1.48217091 6.08393604] label: 0\n",
      "co-ordinate: [4.96405486 1.3944622 ] label: 2\n",
      "co-ordinate: [8.76859584 7.35492628] label: 1\n",
      "co-ordinate: [4.10413135 8.35598762] label: 3\n",
      "co-ordinate: [8.65596299 6.28069131] label: 1\n",
      "co-ordinate: [4.65240733 3.64405561] label: 2\n",
      "co-ordinate: [6.65303331 0.08963673] label: 2\n",
      "co-ordinate: [3.46323992 1.07051547] label: 2\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0,25):\n",
    "    print(\"co-ordinate:\" , points[i] , \"label:\" , labels1[i] )\n",
    "    plt.plot(points[i][0], points[i][1] , colors[labels1[i]] ) #After Clustering in 4 Clusters"
   ]
  },
  {
   "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",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [],
	"source": [
	"import numpy as np\n",
	"from sklearn.cluster import KMeans\n",
	"import matplotlib.pyplot as plt"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {},
	"outputs": [],
	"source": [
	"points = 10*np.random.rand(25,2)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 6,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"image/png": "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\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {
	"needs_background": "light"
	},
	"output_type": "display_data"
	}
	],
	"source": [
	"for i in range(0,25):\n",
	" plt.scatter(points[i][0], points[i][1]) # Before Clustering"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 7,
	"metadata": {},
	"outputs": [],
	"source": [
	"kmeans1 = KMeans(n_clusters = 4)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 8,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
	" n_clusters=4, n_init=10, n_jobs=None, precompute_distances='auto',\n",
	" random_state=None, tol=0.0001, verbose=0)"
	]
	},
	"execution_count": 8,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"kmeans1.fit(points)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 9,
	"metadata": {},
	"outputs": [],
	"source": [
	"centroids1 = kmeans1.cluster_centers_"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 10,
	"metadata": {},
	"outputs": [],
	"source": [
	"labels1 = kmeans1.labels_"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 11,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"array([3, 1, 1, 0, 1, 3, 3, 0, 2, 0, 0, 3, 1, 1, 2, 3, 1, 0, 2, 1, 3, 1,\n",
	" 2, 2, 2])"
	]
	},
	"execution_count": 11,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"labels1"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 12,
	"metadata": {},
	"outputs": [],
	"source": [
	"colors = [\"g. \",\"r. \" , \"c. \" , \"y. \"] "
	]
	},
	{
	"cell_type": "code",
	"execution_count": 14,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"co-ordinate: [3.60952588 8.58957246] label: 3\n",
	"co-ordinate: [6.54835512 6.7343301 ] label: 1\n",
	"co-ordinate: [8.12319473 4.66492978] label: 1\n",
	"co-ordinate: [1.31086289 6.41261099] label: 0\n",
	"co-ordinate: [9.09795888 4.93287961] label: 1\n",
	"co-ordinate: [4.56202665 8.12287641] label: 3\n",
	"co-ordinate: [4.92459186 7.49944154] label: 3\n",
	"co-ordinate: [1.2028035 5.18322524] label: 0\n",
	"co-ordinate: [2.49644391 1.9373279 ] label: 2\n",
	"co-ordinate: [2.93104155 4.27709535] label: 0\n",
	"co-ordinate: [0.09363295 2.85667175] label: 0\n",
	"co-ordinate: [3.28561178 8.66664471] label: 3\n",
	"co-ordinate: [7.74234896 2.87303577] label: 1\n",
	"co-ordinate: [9.36721951 4.50443105] label: 1\n",
	"co-ordinate: [4.33231866 3.53099133] label: 2\n",
	"co-ordinate: [0.74125591 9.0120095 ] label: 3\n",
	"co-ordinate: [7.75008105 3.01727241] label: 1\n",
	"co-ordinate: [1.48217091 6.08393604] label: 0\n",
	"co-ordinate: [4.96405486 1.3944622 ] label: 2\n",
	"co-ordinate: [8.76859584 7.35492628] label: 1\n",
	"co-ordinate: [4.10413135 8.35598762] label: 3\n",
	"co-ordinate: [8.65596299 6.28069131] label: 1\n",
	"co-ordinate: [4.65240733 3.64405561] label: 2\n",
	"co-ordinate: [6.65303331 0.08963673] label: 2\n",
	"co-ordinate: [3.46323992 1.07051547] label: 2\n"
	]
	},
	{
	"data": {
	"image/png": 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B5b993yvpDyXdZWaPhK2qWkIGd6Ej0erAzN6jPLSfdvdnQ9cTwFlJnzKza8pHZp8ws6fClhTEdUnX3f3mb1zPKA/yuvmkpJ+5+w13/62kZyV9LHBNlRIyuDkSTZKZmfKZ5lV3/2boekJw96+4+xl331b+9+CH7l67DsvdfyHpwMw+dPhWX9KrAUsK5XVJD5jZ6cN/H33V8EPakxQ6AacMHIn2O2clfUbSy2b20uF7Xz08dQj181lJTx82M69JeixwPSvn7hfN7BlJl5WvunpRPEX5Ljw5CQCR4clJAIgMwQ0AkSG4ASAyBDcARIbgBoDIENwAEBmCGwAiQ3ADQGT+H29PUz3PTD9UAAAAAElFTkSuQmCC\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {
	"needs_background": "light"
	},
	"output_type": "display_data"
	}
	],
	"source": [
	"for i in range(0,25):\n",
	" print(\"co-ordinate:\" , points[i] , \"label:\" , labels1[i] )\n",
	" plt.plot(points[i][0], points[i][1] , colors[labels1[i]] ) #After Clustering in 4 Clusters"
	]
	},
	{
	"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",
	"version": 3
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
	"pygments_lexer": "ipython3",
	"version": "3.7.2"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}