Kmeans From Scratch_original.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "style.use('ggplot')\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array([[1, 2],\n",
    "              [1.5, 1.8],\n",
    "              [5, 8 ],\n",
    "              [8, 8],\n",
    "              [1, 0.6],\n",
    "              [9,11]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f434ed179b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1], s=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = 10*[\"g\",\"r\",\"c\",\"b\",\"k\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "f= [1,2,3,0]\n",
    "v=f.index(min(f))\n",
    "print(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f43426f6f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class K_Means:\n",
    "    fig = plt.figure()\n",
    "    ax1 = fig.add_subplot(1,1,1)\n",
    "    def __init__(self, k=2, tol=0.001, max_iter=300):\n",
    "        self.k = k\n",
    "        self.tol = tol\n",
    "        self.max_iter = max_iter\n",
    "\n",
    "    def fit(self,data):\n",
    "        #centroid dict\n",
    "        self.centroids = {}\n",
    "        # since k=2 we will select first two points from the data and we will declare that as a centroid\n",
    "        for i in range(self.k):\n",
    "            self.centroids[i] = data[i]\n",
    "        \n",
    "        # we will run this loop for 300 times (300 iteration)\n",
    "        for i in range(self.max_iter):\n",
    "            self.classifications = {}  #{0: [], 1: []}\n",
    "\n",
    "            for i in range(self.k):\n",
    "                self.classifications[i] = []# 0 and 1 ke elements ki list\n",
    "\n",
    "            for featureset in data:  #finding distance from centroid , finding mini value , putting them in classification\n",
    "                distances = [np.linalg.norm(featureset - self.centroids[centroid]) for centroid in \n",
    "                                                                                    self.centroids]\n",
    "                classification = distances.index(min(distances)) #find the index of the min distance\n",
    "\n",
    "                self.classifications[classification].append(featureset)\n",
    "    \n",
    "\n",
    "            prev_centroids = dict(self.centroids)\n",
    "          \n",
    "            for classification in self.classifications:\n",
    "\n",
    "                self.centroids[classification] = np.average(self.classifications[classification],axis=0)\n",
    "                #print(self.centroids)\n",
    "            optimized = True\n",
    "            \n",
    "            for c in self.centroids:\n",
    "                original_centroid = prev_centroids[c]\n",
    "                current_centroid = self.centroids[c]\n",
    "                if np.sum((current_centroid-original_centroid)/original_centroid*100.0) < self.tol:\n",
    "                       break\n",
    "        \n",
    "                \n",
    "            \n",
    "    def predict(self,data):\n",
    "        distances = [np.linalg.norm(data-self.centroids[centroid]) for centroid in self.centroids]\n",
    "        classification = distances.index(min(distances))\n",
    "        return classification\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = K_Means()\n",
    "clf.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4342590a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for centroid in clf.centroids:\n",
    "    plt.scatter(clf.centroids[centroid][0], clf.centroids[centroid][1],\n",
    "                marker=\"o\", color=\"k\", s=50, linewidths=1)\n",
    "\n",
    "for classification in clf.classifications:\n",
    "    color = colors[classification]\n",
    "    for featureset in clf.classifications[classification]:\n",
    "        plt.scatter(featureset[0], featureset[1], marker=\"x\", color=color, s=50, linewidths=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f434265b518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# evenly sampled time at 200ms intervals\n",
    "t = np.arange(0., 5., 1)\n",
    "\n",
    "# red dashes, blue squares and green triangles\n",
    "plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"/home/sambbhav/anaconda3/lib/python3.6/site-packages/matplotlib/cbook/__init__.py\", line 388, in process\n",
      "    proxy(*args, **kwargs)\n",
      "  File \"/home/sambbhav/anaconda3/lib/python3.6/site-packages/matplotlib/cbook/__init__.py\", line 228, in __call__\n",
      "    return mtd(*args, **kwargs)\n",
      "  File \"/home/sambbhav/anaconda3/lib/python3.6/site-packages/matplotlib/animation.py\", line 1081, in _start\n",
      "    self._init_draw()\n",
      "  File \"/home/sambbhav/anaconda3/lib/python3.6/site-packages/matplotlib/animation.py\", line 1792, in _init_draw\n",
      "    self._draw_frame(next(self.new_frame_seq()))\n",
      "  File \"/home/sambbhav/anaconda3/lib/python3.6/site-packages/matplotlib/animation.py\", line 1814, in _draw_frame\n",
      "    self._drawn_artists = self._func(framedata, *self._args)\n",
      "  File \"<ipython-input-11-74861f0c0c73>\", line 9, in animate\n",
      "    pullData = open(\"sampleText.txt\",\"r\").read()\n",
      "FileNotFoundError: [Errno 2] No such file or directory: 'sampleText.txt'\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f43425906a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "import time\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(1,1,1)\n",
    "\n",
    "def animate(i):\n",
    "    pullData = open(\"sampleText.txt\",\"r\").read()\n",
    "    dataArray = pullData.split('\\n')\n",
    "    xar = []\n",
    "    yar = []\n",
    "    for eachLine in dataArray:\n",
    "        if len(eachLine)>1:\n",
    "            x,y = eachLine.split(',')\n",
    "            xar.append(int(x))\n",
    "            yar.append(int(y))\n",
    "    ax1.clear()\n",
    "    ax1.plot(xar,yar)\n",
    "ani = animation.FuncAnimation(fig, animate, interval=1000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}