Single Perceptron.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Perceptron Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [1,2,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = [4,5,6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 5, 6]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a +b "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can't multiply sequence by non-int of type 'list'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-8ce765dcfa30>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'list'"
     ]
    }
   ],
   "source": [
    "a*b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a15595d30>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l6aLeX1eX9MADxc+DkrFjxw69+OKL3RZ0c3OzXnnllQhSYdWqVdqz94TzE2KxmNauXVuw42ZV3O7e6e5jJI2QNMHMTtx/GzOba2YpM0s1Nzdnn6CzUzrY2UJXV/b76Q86O7sfd//41QTQC109/FvoPNjXHgrqUJ+X7go9X3K6q8Td35f0hKTJ3Ty3yN2T7p5saGjIfqfnnivt3n3geHm5dN55ucSL3te/3v0F1fJyafr04udByWhoaNBnPvOZbp+rq6vTCSecUOREkKSpU6eqrKzsgPHdu3frzDPPLNhxeyxuM2sws/rMx9WSJkr6e94SDB4s/eQnUjwumaXHqqulhgbpxhvzdpiimDdPGjUqPZe9Egnp8ssl/mGhj5YsWaKamhoNGDBAklReXq54PK677rpLsRh39kZhwYIFGjRokKoyJ2yxWEzxeFy33nqramtrC3Zc6+mihpmdJOkuSWVKF/197v6jQ/2ZZDLpqVQqtyTr1km33iq98440ebJ06aVSfX1u++gP2tqkZcuklSvT+S+7TJo4MepUKBGbN2/WLbfc8tHtgFdddZU+/elPRx3rsLZjxw4tXLhQa9as0THHHKMrr7xS43pxC7CZNbl7MqttC3E1ulfFDQCHsVyKm9dXABAYihsAAkNxA0BgKG4ACAzFDQCBobgBIDAUNwAEhuIGgMBQ3AAQGIobAAJDcQNAYChuAAgMxQ0AgaG4ASAwFDcABIbiBoDAUNwAEBiKGwACQ3EDQGAobgAIDMUNAIGhuAEgMBQ3AASG4gaAwFDcABAYihsAAkNxA0BgKG4ACAzFDQCBobgBIDDlUQcoSe+9Jz39tFRTI33hC1JZWdSJAJSQHs+4zWykma01s7+Z2ctmdnUxggXr5pul4cOlCy6QvvIVadgwad26qFMBKCHZnHHvkXSNuz9nZrWSmszsMXffUOBs4Xn4YemGG6SOjvRDknbulM45R9qyRaqtjTYfgJLQ4xm3u2919+cyH++U9DdJRxU6WJB++lOptfXA8c5OacWK4ucBUJJyujhpZqMkjZV0wGt/M5trZikzSzU3N+cnXWi2bOl+vK1Neued4mYBULKyLm4zq5G0UtI8d/9g/+fdfZG7J9092dDQkM+M4Tj99O4vRCYS0imnFD8PgJKUVXGbWYXSpX2Pu68qbKSAXXedVF2979iAAdJxx0mTJkWTCUDJyeauEpO0WNLf3H1B4SMFrLFReuop6YwzpFgsXeIXXpgei3HLPID8yOauktMl/Zekl8zs+czY/7r7I4WLFbCxY9NF7S6ZRZ0GQAnqsbjd/U+SaKBcUdoACoTX7wAQGIobAAJDcQNAYChuAAgMxQ0AgTF3z/9OzZolvdnLPz5Y0r/yGCdKpTKXUpmHxFz6o1KZh9S3uRzj7ln92HlBirsvzCzl7smoc+RDqcylVOYhMZf+qFTmIRVvLiyVAEBgKG4ACEx/LO5FUQfIo1KZS6nMQ2Iu/VGpzEMq0lz63Ro3AODQ+uMZNwDgEPpNcZvZnWa23cz+GnWWviilN1c2syoz+4uZvZCZyw+jztQXZlZmZuvN7OGos/SFmW0ys5fM7HkzS0Wdpy/MrN7M7jezv2f+zZwWdaZcmVlj5nOx9/GBmc0r6DH7y1KJmX1RUoukZe5+YtR5esvMhkka9sk3V5b0nyG+uXLm/2JPuHtL5s00/iTpand/NuJovWJm35WUlDTQ3adFnae3zGyTpKS7B3/vs5ndJen/3P0OMxsgKe7u70edq7fMrEzS25JOdffe/ixLj/rNGbe7PyXp3ahz9FUpvbmyp7VkfluRefSP7/Q5MrMRkqZKuiPqLEgzs4GSvqj0G7XI3XeFXNoZZ0t6rZClLfWj4i5Fh3pz5VBklheel7Rd0mPuHupcfi7p+5K6og6SBy7pUTNrMrO5UYfpg9GSmiUtySxh3WFmiahD9dFsScsLfRCKu0B6enPlULh7p7uPkTRC0gQzC24Zy8ymSdru7k1RZ8mT0919nKQpkq7ILDOGqFzSOEm/dPexklolzY82Uu9llnqmS1pR6GNR3AVQim+unHkJ+4SkyRFH6Y3TJU3PrA3fK+ksM7s72ki95+7vZH7dLukBSROiTdRrWyRt+cSruPuVLvJQTZH0nLv/s9AHorjzrJTeXNnMGsysPvNxtaSJkv4ebarcufu17j7C3Ucp/VL2cXefE3GsXjGzROaitzLLCudICvJOLHffJmmzmTVmhs6WFNxF/E84X0VYJpGye7PgojCz5ZK+JGmwmW2RdL27L442Va+U0psrD5N0V+ZKeUzSfe4e9K10JeBISQ+kzw9ULuk37v77aCP1yZWS7sksM7wu6eKI8/SKmcUlTZJ0WVGO119uBwQAZIelEgAIDMUNAIGhuAEgMBQ3AASG4gaAwFDcABAYihsAAkNxA0Bg/h94/lrVyJooEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#import sklearn.linear_model.perceptron as p\n",
    "from sklearn.linear_model import perceptron\n",
    "\n",
    "# Needed to show the plots inline\n",
    "%matplotlib inline\n",
    "\n",
    "# Data\n",
    "d = np.array([\n",
    "[2, 1, 2, 5, 7, 2, 3, 6, 1, 2, 5, 4, 6, 5],\n",
    "[2, 3, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 7]\n",
    "])\n",
    "\n",
    "# Labels\n",
    "t = np.array([0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1])\n",
    "\n",
    "colormap = np.array(['r', 'k'])\n",
    "plt.scatter(d[0], d[1], c=colormap[t], s=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction [0 0 0 1 1 0 0 1 0 0 1 1 1 1]\n",
      "Actual     [0 0 0 1 1 0 0 1 0 0 1 1 1 1]\n",
      "Accuracy   100.0%\n",
      "Coefficient 0 -0.014\n",
      "Coefficient 1 0.038\n",
      "Bias [-0.06]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/faris/anaconda3/lib/python3.5/site-packages/sklearn/linear_model/stochastic_gradient.py:117: DeprecationWarning: n_iter parameter is deprecated in 0.19 and will be removed in 0.21. Use max_iter and tol instead.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a1e5090f0>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rotate the data 180 degrees\n",
    "d90 = np.rot90(d)\n",
    "d90 = np.rot90(d90)\n",
    "d90 = np.rot90(d90)\n",
    "\n",
    "# Create the model\n",
    "net = perceptron.Perceptron(n_iter=100, verbose=0, random_state=None, fit_intercept=True, eta0=0.002)\n",
    "net.fit(d90,t)\n",
    "\n",
    "# Print the results\n",
    "print (\"Prediction \" + str(net.predict(d90)))\n",
    "print (\"Actual     \" + str(t))\n",
    "print (\"Accuracy   \" + str(net.score(d90, t)*100) + \"%\")\n",
    "\n",
    "# Plot the original data\n",
    "plt.scatter(d[0], d[1], c=colormap[t], s=40)\n",
    "\n",
    "# Output the values\n",
    "print (\"Coefficient 0 \" + str(net.coef_[0,0]))\n",
    "print (\"Coefficient 1 \" + str(net.coef_[0,1]))\n",
    "print (\"Bias \" + str(net.intercept_))\n",
    "\n",
    "# Calc the hyperplane (decision boundary)\n",
    "ymin, ymax = plt.ylim()\n",
    "w = net.coef_[0]\n",
    "a = -w[0] / w[1]\n",
    "xx = np.linspace(ymin, ymax)\n",
    "yy = a * xx - (net.intercept_[0]) / w[1]\n",
    "\n",
    "# Plot the line\n",
    "plt.plot(yy,xx, 'k-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/faris/anaconda3/lib/python3.5/site-packages/ipykernel_launcher.py:1: DeprecationWarning: This function is deprecated. Please call randint(1, 10 + 1) instead\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a1e758908>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nX = np.random.random_integers(10, size=(2,50))\n",
    "\n",
    "\n",
    "# Rotate it the same as the previous data\n",
    "nX90 = np.rot90(nX)\n",
    "nX90 = np.rot90(nX90)\n",
    "nX90 = np.rot90(nX90)\n",
    "\n",
    "prediction = net.predict(nX90)\n",
    "\n",
    "# Plot the nX random values AND the prediction as a color\n",
    "plt.scatter(nX[0],nX[1], c=colormap[prediction],s=40)\n",
    "# Now plot the hyperplane\n",
    "ymin, ymax = plt.ylim()\n",
    "# Calc\n",
    "w = net.coef_[0]\n",
    "a = -w[0] / w[1]\n",
    "xx = np.linspace(ymin, ymax)\n",
    "yy = a * xx - (net.intercept_[0]) / w[1]\n",
    "plt.plot(yy,xx, 'k-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nX.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nX90.shape"
   ]
  },
  {
   "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": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}