Perceptron (ANN) (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5f4d5622",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 2)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "X, y = datasets.make_blobs(n_samples=150, n_features=2, centers=2, cluster_std=1.05, random_state=2)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "567df2f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Random Classification data with 2 classes')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "plt.plot(X[:,0][y==0], X[:,1][y==0], 'r*')\n",
    "plt.plot(X[:,0][y==1], X[:,1][y==1], 'bs')\n",
    "plt.xlabel(\"feature 1\")\n",
    "plt.ylabel(\"feature 2\")\n",
    "plt.title('Random Classification data with 2 classes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ccc3061e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def perceptron(X, y, lr, epochs):\n",
    "    \n",
    "    # X --> Inputs,\n",
    "    # y --> labels/target,\n",
    "    # lr --> learning rate,\n",
    "    # epochs --> no. of iterations.\n",
    "    \n",
    "    m, n = X.shape\n",
    "    # m --> no. of training examples\n",
    "    # n --> no. of features\n",
    "    \n",
    "    # Initializing parameters(theta) to zeros,\n",
    "    # +1 in n+1 for the bias term.\n",
    "    theta = np.zeros((n+1, 1))\n",
    "    \n",
    "    # Empty list to store how many examples were misclassified at every iteration.\n",
    "    n_miss_list = []\n",
    "    \n",
    "    # Training\n",
    "    for epoch in range(epochs):\n",
    "        n_miss = 0\n",
    "        \n",
    "        # Looping for every example\n",
    "        for idx, x_i in enumerate(X):\n",
    "            # Inserting 1 for bias, X0=1\n",
    "            x_i = np.insert(x_i, 0, 1).reshape(-1, 1)\n",
    "            # Calculating prediction/hypothesis\n",
    "            y_hat = step_func(np.dot(x_i.T, theta))\n",
    "            # Updating if the example is misclassified\n",
    "            if (np.squeeze(y_hat)-y[idx]) != 0:\n",
    "                theta += lr*((y[idx] - y_hat)*x_i)\n",
    "                # Incrementing by 1\n",
    "                n_miss += 1\n",
    "        \n",
    "        #Appending no. of misclassified examples at every iteration\n",
    "        n_miss_list.append(n_miss)\n",
    "    \n",
    "    return theta, n_miss_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "73ad4dc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def step_func(z):\n",
    "    return 1.0 if (z>0) else 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a5bd1f06",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_decision_boundary(X, theta):\n",
    "    \n",
    "    # X --> Inputs\n",
    "    # theta --> parameters\n",
    "    \n",
    "    # The line is y=mx+c\n",
    "    # So, equate mx+c = theta0.X0 + theta1.X1 + theta2.X2\n",
    "    # Solving we find m and c\n",
    "    x1 = [min(X[:,0]), max(X[:,0])]\n",
    "    m = -theta[1] / theta[2]\n",
    "    c = -theta[0] / theta[2]\n",
    "    x2 = m*x1 + c\n",
    "    \n",
    "    #Plotting\n",
    "    fig = plt.figure(figsize=(10,8))\n",
    "    plt.plot(X[:,0][y==0], X[:,1][y==0], 'r^')\n",
    "    plt.plot(X[:,0][y==1], X[:,1][y==1], 'bs')\n",
    "    plt.xlabel(\"feature 1\")\n",
    "    plt.ylabel(\"feature 2\")\n",
    "    plt.title(\"Prerceptron Algorithm\")\n",
    "    plt.plot(x1, x2, 'y-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "298d2126",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "theta, miss_1 = perceptron(X, y, 0.5, 100)\n",
    "plot_decision_boundary(X, theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7221c7fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-5.32783010e-01 -1.64847081e+00]\n",
      " [-5.50226372e-01 -1.16166152e+01]\n",
      " [ 1.38862276e+00 -1.43580590e+00]\n",
      " [ 1.37033956e+00 -6.40220712e-01]\n",
      " [-8.80606388e-01 -9.70946740e+00]\n",
      " [-2.22746033e+00 -1.01019963e+01]\n",
      " [-3.83660791e+00 -9.36311253e+00]\n",
      " [-2.61500332e-01 -1.80587922e+00]\n",
      " [ 1.18267985e+00 -7.12729660e-01]\n",
      " [-8.91135194e-01 -8.05385981e+00]\n",
      " [-3.42244116e+00 -9.43290706e+00]\n",
      " [-3.24159714e+00 -7.66373146e+00]\n",
      " [-8.14411765e-02 -1.76413571e+00]\n",
      " [ 2.82215236e+00 -1.76342807e+00]\n",
      " [ 1.90632766e+00 -2.43499725e+00]\n",
      " [ 1.12041042e+00 -2.18272234e+00]\n",
      " [ 4.12652646e-01 -9.79048994e-01]\n",
      " [-1.61986103e+00 -9.03645942e+00]\n",
      " [ 1.24676117e+00 -7.71255216e-01]\n",
      " [-2.01362140e+00 -1.04568119e+01]\n",
      " [-6.48510353e-01 -9.47763290e+00]\n",
      " [ 1.90763236e+00 -6.71105011e-01]\n",
      " [-7.57264801e-01 -9.34864598e+00]\n",
      " [ 2.18263294e+00 -9.04732063e-01]\n",
      " [ 1.53216357e+00 -1.64513848e+00]\n",
      " [ 1.12431439e+00 -4.99224897e-01]\n",
      " [ 1.19440189e+00 -1.98887161e+00]\n",
      " [-2.43377785e+00 -7.81776395e+00]\n",
      " [ 3.50550070e-01 -1.66237773e+00]\n",
      " [-7.52076469e-01 -1.07890279e+01]\n",
      " [ 8.37695007e-01  2.82956613e-01]\n",
      " [ 1.86418091e+00 -6.31399082e-01]\n",
      " [ 2.07793008e+00 -1.63900470e+00]\n",
      " [-3.16320932e+00 -1.03653101e+01]\n",
      " [ 2.09100144e+00  1.43914678e-01]\n",
      " [-1.51976189e+00 -1.16545682e+01]\n",
      " [ 1.57354644e+00 -2.17601731e+00]\n",
      " [-1.23648559e+00 -1.06552971e+01]\n",
      " [-2.89251066e-01  6.01148326e-01]\n",
      " [ 2.60959954e-01 -9.83393657e+00]\n",
      " [ 1.39785819e+00 -1.10283073e+01]\n",
      " [-8.24930811e-01 -8.13917717e+00]\n",
      " [-1.63748148e+00  6.23742032e-01]\n",
      " [-1.76520757e+00 -9.11304244e+00]\n",
      " [ 5.01485985e-01 -2.61100847e+00]\n",
      " [ 6.65365354e-01  9.58359846e-01]\n",
      " [-1.05318015e+00 -1.14330184e+01]\n",
      " [ 6.02120538e-01 -9.93193935e+00]\n",
      " [-8.79142411e-01 -8.88688615e+00]\n",
      " [ 3.69806196e-01 -8.65765515e-02]\n",
      " [-1.94963972e+00 -1.07284683e+01]\n",
      " [ 1.05673513e+00 -2.69907047e+00]\n",
      " [ 2.16363071e+00 -8.46207098e+00]\n",
      " [-4.14163801e-01 -8.17085180e+00]\n",
      " [-4.78938837e-02 -1.19422587e+01]\n",
      " [-1.47904469e+00 -9.56255496e+00]\n",
      " [-1.01070298e+00 -1.05196934e+01]\n",
      " [-1.94651523e+00 -8.94765931e+00]\n",
      " [-2.76985304e+00 -9.74583819e+00]\n",
      " [-2.15069401e+00 -9.38937963e+00]\n",
      " [ 8.09502872e-01 -2.80255275e+00]\n",
      " [ 2.56504894e+00 -2.90121881e+00]\n",
      " [-1.21184474e-01 -1.40749481e+00]\n",
      " [-1.60928556e+00 -8.67191304e+00]\n",
      " [-2.15303534e-01 -1.74140074e+00]\n",
      " [-1.57009042e+00 -1.00242035e+01]\n",
      " [ 1.37655575e+00 -4.80766013e-01]\n",
      " [-1.99166131e+00 -1.09928863e+01]\n",
      " [ 1.75487048e+00 -1.67100033e+00]\n",
      " [ 1.02744330e+00 -3.01614038e+00]\n",
      " [-1.62140407e+00 -1.00697216e+01]\n",
      " [ 5.16310310e-01 -7.53462894e-01]\n",
      " [ 6.10546511e-01 -2.21333060e+00]\n",
      " [ 1.00754280e-01 -2.18851103e+00]\n",
      " [ 8.11524834e-01 -1.70866587e+00]\n",
      " [-1.67455485e+00 -9.55966966e+00]\n",
      " [ 2.07284370e+00 -2.05152586e+00]\n",
      " [-1.06568358e+00 -8.00444431e+00]\n",
      " [ 2.39559403e+00 -9.75791345e-01]\n",
      " [ 2.00207060e+00 -4.97263283e-01]\n",
      " [ 3.02306437e+00 -2.41788829e+00]\n",
      " [-3.90196076e-01 -3.42042542e+00]\n",
      " [-9.49674615e-01 -9.46539680e-01]\n",
      " [ 1.34595337e+00  7.06042557e-01]\n",
      " [-6.38194139e-01 -9.43110624e+00]\n",
      " [ 9.06891914e-01 -1.61345206e+00]\n",
      " [-1.49987326e+00 -1.23210622e+00]\n",
      " [-1.27170922e-01 -1.97912985e+00]\n",
      " [ 6.61771687e-01 -2.71105436e+00]\n",
      " [-2.46989189e+00 -9.02672818e+00]\n",
      " [-2.20211525e+00 -9.64573124e+00]\n",
      " [ 1.05336305e+00 -1.12650924e+00]\n",
      " [ 3.93298869e-01 -9.86498477e-01]\n",
      " [ 1.63390085e+00 -8.48880402e-01]\n",
      " [-4.40962962e-01 -8.15573102e+00]\n",
      " [-7.29590706e-01 -8.70943181e+00]\n",
      " [-2.83669071e+00 -8.59284788e+00]\n",
      " [ 5.30737681e+00 -4.31375226e-01]\n",
      " [ 9.90932790e-01 -1.43980862e+00]\n",
      " [-1.28009171e+00 -8.91200516e+00]\n",
      " [-1.72038426e+00 -9.62042071e+00]\n",
      " [-1.68069426e+00 -9.41166190e+00]\n",
      " [ 2.13334628e+00 -3.46263782e-01]\n",
      " [-3.24949022e-01 -3.08480293e-01]\n",
      " [-7.53151890e-01 -1.03678998e+01]\n",
      " [ 2.60159789e+00 -3.37869021e+00]\n",
      " [-2.29717776e-01 -9.88162251e+00]\n",
      " [-1.40024432e+00 -1.00045583e+01]\n",
      " [-1.63586502e+00 -9.72946860e+00]\n",
      " [-7.41560748e-01 -2.00299703e+00]\n",
      " [-7.01075210e-01 -7.07465695e+00]\n",
      " [ 1.08288221e+00 -5.94951156e-01]\n",
      " [-1.15501512e-01 -1.13346390e+01]\n",
      " [-2.39095179e+00 -1.04359334e+01]\n",
      " [-3.10495944e+00 -8.38861021e+00]\n",
      " [-1.56261177e+00 -7.13854024e+00]\n",
      " [-4.07252389e+00 -9.57750062e+00]\n",
      " [-1.30018898e+00 -8.24772408e+00]\n",
      " [-4.90538865e-01 -1.05249244e+01]\n",
      " [ 1.40959572e+00 -1.62390046e+00]\n",
      " [-1.70418884e+00 -1.09352200e+01]\n",
      " [ 1.55495238e-01 -8.20269545e+00]\n",
      " [-1.47252136e+00 -8.41062790e+00]\n",
      " [-1.97784850e-01 -2.03634408e+00]\n",
      " [-5.49094079e-01 -8.74918828e+00]\n",
      " [-1.59939272e+00 -6.76691575e+00]\n",
      " [-2.03531849e+00 -1.00940607e+01]\n",
      " [-8.06521224e-01 -1.37402369e+00]\n",
      " [ 1.40306083e+00 -2.69267265e-01]\n",
      " [-6.05043919e-02 -9.07609341e+00]\n",
      " [-1.96601474e+00 -8.59689627e+00]\n",
      " [ 2.16703063e+00 -1.58150634e+00]\n",
      " [-1.13128258e+00 -1.01218094e+00]\n",
      " [-1.61012617e-01 -2.00915932e+00]\n",
      " [-1.32165071e+00 -9.13917176e+00]\n",
      " [-2.21284571e-01  2.18426780e-03]\n",
      " [ 4.89265190e-01 -2.36418370e+00]\n",
      " [ 8.25539221e-01 -7.17158260e-01]\n",
      " [-2.06536645e+00 -9.47199885e+00]\n",
      " [ 3.43955298e-01 -8.95817959e-01]\n",
      " [-2.90411030e+00 -1.16682599e+01]\n",
      " [ 9.85268362e-01 -2.27407434e+00]\n",
      " [-2.39394095e+00 -8.86022552e+00]\n",
      " [-2.77238005e+00 -9.64264532e+00]\n",
      " [ 6.46997402e-01 -9.01875100e+00]\n",
      " [-7.14090721e-01 -1.01074430e+01]\n",
      " [ 2.60170010e-01 -1.04160339e+00]\n",
      " [-2.35258742e+00 -9.02745988e+00]\n",
      " [ 1.69068204e+00 -5.21923247e-01]\n",
      " [ 6.95554065e-01 -7.28206672e-01]]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5fffa1d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 1 1 0 0 0 1 1 0 0 0 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 0 1 0 1 1 1 0 1 0 1\n",
      " 0 1 0 0 0 1 0 1 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 1 0 1 0 1 1 0 1 1 1\n",
      " 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1 0 0 1 1 1 0 0 0 1 1 0 0 0 1 1 0 1 0 0 0 1 0\n",
      " 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 1 0 0 1 1 1 0 1 1 1 0 1 0 1 0 0 0 0 1 0\n",
      " 1 1]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f30a6d9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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