bl.en.u4cse21199 lab 5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6c9d15bf-6ff8-40c9-ab0f-dcda1377f83e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0b59c168",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing AND gate logic:\n",
      "Input: [0 0], Prediction: 0\n",
      "Input: [0 1], Prediction: 0\n",
      "Input: [1 0], Prediction: 0\n",
      "Input: [1 1], Prediction: 1\n",
      "Number of epochs needed for convergence: 186\n"
     ]
    }
   ],
   "source": [
    "'''A1. Develop the above perceptron in your own code (dont use the perceptron model available from package). Use the initial weights as \n",
    "provided below. \n",
    "W0 = 10, W1 = 0.2, w2 = -0.75, learning rate (α) = 0.05\n",
    "Use Step activation function to learn the weights of the network to implement above provided AND gate logic. The activation function is \n",
    "demonstrated below.'''\n",
    "\n",
    "\n",
    "w1 = 10\n",
    "w2 = 0.2\n",
    "bias = -0.75\n",
    "learning_rate = 0.05\n",
    "\n",
    "\n",
    "def step_activation(y):\n",
    "    return 1 if y >= 0 else 0\n",
    "\n",
    "def train_perceptron(inputs, targets, weights, bias, learning_rate, max_epochs, convergence_threshold):\n",
    "    error_history = [] \n",
    "    for epoch in range(max_epochs):\n",
    "        total_error = 0\n",
    "        for i in range(len(inputs)):\n",
    "            weighted_sum = bias + weights[0] * inputs[i][0] + weights[1] * inputs[i][1]\n",
    "            prediction = step_activation(weighted_sum)\n",
    "            error = targets[i] - prediction\n",
    "            total_error += error ** 2\n",
    "            weights[0] += learning_rate * error * inputs[i][0]\n",
    "            weights[1] += learning_rate * error * inputs[i][1]\n",
    "        error_history.append(total_error)\n",
    "        if total_error <= convergence_threshold:\n",
    "            break\n",
    "    return error_history\n",
    "\n",
    "and_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
    "and_targets = np.array([0, 0, 0, 1])\n",
    "\n",
    "weights = [w1, w2]\n",
    "\n",
    "convergence_threshold = 0.002\n",
    "max_epochs = 1000\n",
    "\n",
    "error_history = train_perceptron(and_inputs, and_targets, weights, bias, learning_rate, max_epochs, convergence_threshold)\n",
    "\n",
    "plt.plot(range(1, len(error_history) + 1), error_history)\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Sum Square Error')\n",
    "plt.title('Epochs vs. Error')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "print(\"Testing AND gate logic:\")\n",
    "for i in range(len(and_inputs)):\n",
    "    weighted_sum = bias + weights[0] * and_inputs[i][0] + weights[1] * and_inputs[i][1]\n",
    "    prediction = step_activation(weighted_sum)\n",
    "    print(f\"Input: {and_inputs[i]}, Prediction: {prediction}\")\n",
    "\n",
    "print(f\"Number of epochs needed for convergence: {len(error_history)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "02c21f4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing AND gate logic with Bi-Polar Step function:\n",
      "Input: [0 0], Prediction: -1\n",
      "Input: [0 1], Prediction: -1\n",
      "Input: [1 0], Prediction: 1\n",
      "Input: [1 1], Prediction: 1\n",
      "Number of epochs needed for convergence with Bi-Polar Step function: 1000 ,still not got corrrect outputs\n"
     ]
    }
   ],
   "source": [
    "'''A2. Repeat the above A1 experiment with following activation functions. Compare the iterations \n",
    "taken to converge against each of the activation functions. Keep the learning rate same as A1.\n",
    "• Bi-Polar Step function\n",
    "• Sigmoid function\n",
    "• ReLU function\n",
    "'''\n",
    "def step_activation_bipolar(y):\n",
    "    if y>0:\n",
    "        return 1\n",
    "    elif (y==0):\n",
    "        return 0\n",
    "    elif y<0:\n",
    "        return -1\n",
    "\n",
    "w1 = 10\n",
    "w2 = 0.2\n",
    "bias = -0.75\n",
    "learning_rate = 0.05\n",
    "\n",
    "def train_perceptron(inputs, targets, weights, bias, learning_rate, max_epochs, convergence_threshold):\n",
    "    error_history = [] \n",
    "    for epoch in range(max_epochs):\n",
    "        total_error = 0\n",
    "        for i in range(len(inputs)):\n",
    "            weighted_sum = bias + weights[0] * inputs[i][0] + weights[1] * inputs[i][1]\n",
    "            prediction = step_activation_bipolar(weighted_sum)\n",
    "            error = targets[i] - prediction\n",
    "            total_error += error ** 2\n",
    "            weights[0] += learning_rate * error * inputs[i][0]\n",
    "            weights[1] += learning_rate * error * inputs[i][1]\n",
    "        error_history.append(total_error)\n",
    "        if total_error <= convergence_threshold:\n",
    "            break\n",
    "    return error_history\n",
    "\n",
    "and_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
    "and_targets = np.array([0, 0, 0, 1])\n",
    "\n",
    "weights = [w1, w2]\n",
    "\n",
    "convergence_threshold = 0.002\n",
    "max_epochs = 1000\n",
    "\n",
    "error_history = train_perceptron(and_inputs, and_targets, weights, bias, learning_rate, max_epochs, convergence_threshold)\n",
    "\n",
    "plt.plot(range(1, len(error_history) + 1), error_history)\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Sum Square Error')\n",
    "plt.title('Epochs vs. Error')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "print(\"Testing AND gate logic with Bi-Polar Step function:\")\n",
    "for i in range(len(and_inputs)):\n",
    "    weighted_sum = bias + weights[0] * and_inputs[i][0] + weights[1] * and_inputs[i][1]\n",
    "    prediction = step_activation_bipolar(weighted_sum)\n",
    "    print(f\"Input: {and_inputs[i]}, Prediction: {prediction}\")\n",
    "\n",
    "print(f\"Number of epochs needed for convergence with Bi-Polar Step function: {len(error_history)} ,still not got corrrect outputs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "080bbba0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing AND gate logic with Sigmoid function:\n",
      "Input: [0 0], Prediction: 0.0\n",
      "Input: [0 1], Prediction: 0.0\n",
      "Input: [1 0], Prediction: 0.0\n",
      "Input: [1 1], Prediction: 1.0\n",
      "Number of epochs needed for convergence with Sigmoid function: 274 \n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "def step_activation_sigmoid(y):\n",
    "    return (1 // (1 + (math.e)**(-y)))\n",
    "\n",
    "w1 = 10\n",
    "w2 = 0.2\n",
    "bias = -0.75\n",
    "learning_rate = 0.05\n",
    "\n",
    "def train_perceptron(inputs, targets, weights, bias, learning_rate, max_epochs, convergence_threshold):\n",
    "    error_history = [] \n",
    "    for epoch in range(max_epochs):\n",
    "        total_error = 0\n",
    "        for i in range(len(inputs)):\n",
    "            weighted_sum = bias + weights[0] * inputs[i][0] + weights[1] * inputs[i][1]\n",
    "            prediction = step_activation_sigmoid(weighted_sum)\n",
    "            error = targets[i] - prediction\n",
    "            total_error += error ** 2\n",
    "            weights[0] += learning_rate * error * inputs[i][0]\n",
    "            weights[1] += learning_rate * error * inputs[i][1]\n",
    "        error_history.append(total_error)\n",
    "        if total_error <= convergence_threshold:\n",
    "            break\n",
    "    return error_history\n",
    "\n",
    "and_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
    "and_targets = np.array([0, 0, 0, 1])\n",
    "\n",
    "weights = [w1, w2]\n",
    "\n",
    "convergence_threshold = 0.002\n",
    "max_epochs = 1000\n",
    "\n",
    "error_history = train_perceptron(and_inputs, and_targets, weights, bias, learning_rate, max_epochs, convergence_threshold)\n",
    "\n",
    "plt.plot(range(1, len(error_history) + 1), error_history)\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Sum Square Error')\n",
    "plt.title('Epochs vs. Error')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "print(\"Testing AND gate logic with Sigmoid function:\")\n",
    "for i in range(len(and_inputs)):\n",
    "    weighted_sum = bias + weights[0] * and_inputs[i][0] + weights[1] * and_inputs[i][1]\n",
    "    prediction = step_activation_sigmoid(weighted_sum)\n",
    "    print(f\"Input: {and_inputs[i]}, Prediction: {prediction}\")\n",
    "\n",
    "print(f\"Number of epochs needed for convergence with Sigmoid function: {len(error_history)} \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "78cf9853",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing AND gate logic with ReLU function:\n",
      "Input: [0 0], Prediction: 0\n",
      "Input: [0 1], Prediction: 0.08620689655172287\n",
      "Input: [1 0], Prediction: 0.08620689655172475\n",
      "Input: [1 1], Prediction: 0.9224137931034476\n",
      "Number of epochs needed for convergence with ReLU function: 1000 , still not got exact corrrect outputs\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "def step_activation_relu(y):\n",
    "    if y>0:\n",
    "        return y\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "w1 = 10\n",
    "w2 = 0.2\n",
    "bias = -0.75\n",
    "learning_rate = 0.05\n",
    "\n",
    "def train_perceptron(inputs, targets, weights, bias, learning_rate, max_epochs, convergence_threshold):\n",
    "    error_history = [] \n",
    "    for epoch in range(max_epochs):\n",
    "        total_error = 0\n",
    "        for i in range(len(inputs)):\n",
    "            weighted_sum = bias + weights[0] * inputs[i][0] + weights[1] * inputs[i][1]\n",
    "            prediction = step_activation_relu(weighted_sum)\n",
    "            error = targets[i] - prediction\n",
    "            total_error += error ** 2\n",
    "            weights[0] += learning_rate * error * inputs[i][0]\n",
    "            weights[1] += learning_rate * error * inputs[i][1]\n",
    "        error_history.append(total_error)\n",
    "        if total_error <= convergence_threshold:\n",
    "            break\n",
    "    return error_history\n",
    "\n",
    "and_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
    "and_targets = np.array([0, 0, 0, 1])\n",
    "\n",
    "weights = [w1, w2]\n",
    "\n",
    "convergence_threshold = 0.002\n",
    "max_epochs = 1000\n",
    "\n",
    "error_history = train_perceptron(and_inputs, and_targets, weights, bias, learning_rate, max_epochs, convergence_threshold)\n",
    "\n",
    "plt.plot(range(1, len(error_history) + 1), error_history)\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Sum Square Error')\n",
    "plt.title('Epochs vs. Error')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "print(\"Testing AND gate logic with ReLU function:\")\n",
    "for i in range(len(and_inputs)):\n",
    "    weighted_sum = bias + weights[0] * and_inputs[i][0] + weights[1] * and_inputs[i][1]\n",
    "    prediction = step_activation_relu(weighted_sum)\n",
    "    print(f\"Input: {and_inputs[i]}, Prediction: {prediction}\")\n",
    "\n",
    "print(f\"Number of epochs needed for convergence with ReLU function: {len(error_history)} , still not got exact corrrect outputs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b5ba1c74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing AND gate logic:\n",
      "Input: [0 0], Prediction: 0\n",
      "Input: [0 1], Prediction: 1\n",
      "Input: [1 0], Prediction: 1\n",
      "Input: [1 1], Prediction: 1\n",
      "Number of epochs needed for convergence: [94, 48, 32, 26, 21, 1000, 1000, 1000, 1000, 1000]\n"
     ]
    }
   ],
   "source": [
    "'''A3. Repeat exercise A1 with varying the learning rate, keeping the initial weights same. Take learning \n",
    "rate = {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1}. Make a plot of the number of iterations taken for \n",
    "learning to converge against the learning rates'''\n",
    "\n",
    "\n",
    "w1 = 10\n",
    "w2 = 0.2\n",
    "bias = -0.75\n",
    "learning_rates = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]\n",
    "\n",
    "\n",
    "def step_activation(y):\n",
    "    return 1 if y >= 0 else 0\n",
    "\n",
    "def train_perceptron(inputs, targets, weights, bias, learning_rate, max_epochs, convergence_threshold):\n",
    "    error_history = [] \n",
    "    for epoch in range(max_epochs):\n",
    "        total_error = 0\n",
    "        for i in range(len(inputs)):\n",
    "            weighted_sum = bias + weights[0] * inputs[i][0] + weights[1] * inputs[i][1]\n",
    "            prediction = step_activation(weighted_sum)\n",
    "            error = targets[i] - prediction\n",
    "            total_error += error ** 2\n",
    "            weights[0] += learning_rate * error * inputs[i][0]\n",
    "            weights[1] += learning_rate * error * inputs[i][1]\n",
    "        error_history.append(total_error)\n",
    "        if total_error <= convergence_threshold:\n",
    "            break\n",
    "    return error_history\n",
    "\n",
    "and_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
    "and_targets = np.array([0, 0, 0, 1])\n",
    "\n",
    "weights = [w1, w2]\n",
    "\n",
    "convergence_threshold = 0.002\n",
    "max_epochs = 1000\n",
    "\n",
    "error_history = []\n",
    "\n",
    "for i in range(0,10):\n",
    "    w1 = 10\n",
    "    w2 = 0.2\n",
    "    bias = -0.75\n",
    "    weights = [w1, w2]\n",
    "    error_history.append(len(train_perceptron(and_inputs, and_targets, weights, bias, learning_rates[i], max_epochs, convergence_threshold)))\n",
    "\n",
    "plt.plot(error_history)\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Sum Square Error')\n",
    "plt.title('Epochs vs. Error')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "print(\"Testing AND gate logic:\")\n",
    "for i in range(len(and_inputs)):\n",
    "    weighted_sum = bias + weights[0] * and_inputs[i][0] + weights[1] * and_inputs[i][1]\n",
    "    prediction = step_activation(weighted_sum)\n",
    "    print(f\"Input: {and_inputs[i]}, Prediction: {prediction}\")\n",
    "\n",
    "print(f\"Number of epochs needed for convergence: {error_history}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "527dfe1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing XOR gate logic:\n",
      "Input: [0 0], Prediction: 1\n",
      "Input: [0 1], Prediction: 1\n",
      "Input: [1 0], Prediction: 1\n",
      "Input: [1 1], Prediction: 0\n",
      "Number of epochs needed for convergence: 5000\n"
     ]
    }
   ],
   "source": [
    "'''A4. Repeat the above exercises, A1 to A3, for XOR gate logic.'''\n",
    "\n",
    "\n",
    "bias = 1\n",
    "w1 = 0.2\n",
    "w2 = -0.75\n",
    "learning_rate = 0.05\n",
    "\n",
    "\n",
    "def step_activation(y):\n",
    "    return 1 if y >= 0 else 0\n",
    "\n",
    "def train_perceptron(inputs, targets, weights, bias, learning_rate, max_epochs, convergence_threshold):\n",
    "    error_history = [] \n",
    "    for epoch in range(max_epochs):\n",
    "        total_error = 0\n",
    "        for i in range(len(inputs)):\n",
    "            weighted_sum = bias + weights[0] * inputs[i][0] + weights[1] * inputs[i][1]\n",
    "            prediction = step_activation(weighted_sum)\n",
    "            error = targets[i] - prediction\n",
    "            total_error += error ** 2\n",
    "            weights[0] += learning_rate * error * inputs[i][0]\n",
    "            weights[1] += learning_rate * error * inputs[i][1]\n",
    "        error_history.append(total_error)\n",
    "        if total_error <= convergence_threshold:\n",
    "            break\n",
    "    return error_history\n",
    "\n",
    "xor_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
    "xor_outputs = np.array([0, 1, 1, 0])\n",
    "\n",
    "weights = [w1, w2]\n",
    "\n",
    "convergence_threshold = 0.002\n",
    "max_epochs = 5000\n",
    "\n",
    "error_history = train_perceptron(xor_inputs, xor_outputs, weights, bias, learning_rate, max_epochs, convergence_threshold)\n",
    "\n",
    "plt.plot(range(1, len(error_history) + 1), error_history)\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Sum Square Error')\n",
    "plt.title('Epochs vs. Error')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "print(\"Testing XOR gate logic:\")\n",
    "for i in range(len(xor_inputs)):\n",
    "    weighted_sum = bias + weights[0] * xor_inputs[i][0] + weights[1] * xor_inputs[i][1]\n",
    "    prediction = step_activation(weighted_sum)\n",
    "    print(f\"Input: {xor_inputs[i]}, Prediction: {prediction}\")\n",
    "\n",
    "print(f\"Number of epochs needed for convergence: {len(error_history)}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "220e3b80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing purchase data:\n",
      "Input: [ 20   6   2 386], Prediction: 1.0\n",
      "Input: [ 16   3   6 289], Prediction: 1.0\n",
      "Input: [ 27   6   2 393], Prediction: 1.0\n",
      "Input: [ 19   1   2 110], Prediction: 0.0\n",
      "Input: [ 24   4   2 280], Prediction: 1.0\n",
      "Input: [ 22   1   5 167], Prediction: 0.0\n",
      "Input: [ 15   4   2 271], Prediction: 1.0\n",
      "Input: [ 18   4   2 274], Prediction: 1.0\n",
      "Input: [ 21   1   4 148], Prediction: 0.0\n",
      "Input: [ 16   2   4 198], Prediction: 0.0\n",
      "Number of epochs needed for convergence: 62\n"
     ]
    }
   ],
   "source": [
    "'''A5. Use customer data provided below. Build a perceptron & learn to classify the transactions as \n",
    "high or low value as provided in the below table. Use sigmoid as the activation function. Initialize the \n",
    "weights & learning rate with your choice.'''\n",
    "\n",
    "\n",
    "purchase_data = pd.read_excel('Lab Session1 Data.xlsx',usecols=\"A:E\")\n",
    "\n",
    "def category(x):\n",
    "    if x>200:\n",
    "        return \"Yes\"\n",
    "    else:\n",
    "        return \"No\"\n",
    "    \n",
    "purchase_data['High Value']=purchase_data['Payment (Rs)'].apply(category)\n",
    "purchase_data['High Value']=purchase_data['High Value'].map({'Yes':1,'No':0})\n",
    "\n",
    "#Perceptron\n",
    "w1 = 10\n",
    "w2 = 1\n",
    "w3= 1\n",
    "w4 = 100\n",
    "bias = 1\n",
    "learning_rate = 0.5\n",
    "\n",
    "\n",
    "def step_activation(y):\n",
    "    return (1 // (1 + (math.e)**(-y)))\n",
    "\n",
    "def train_perceptron(inputs, targets, weights, bias, learning_rate, max_epochs, convergence_threshold):\n",
    "    error_history = [] \n",
    "    for epoch in range(max_epochs):\n",
    "        total_error = 0\n",
    "        for i in range(len(inputs)):\n",
    "            weighted_sum = bias + weights[0] * inputs[i][0] + weights[1] * inputs[i][1]\n",
    "            prediction = step_activation(weighted_sum)\n",
    "            error = targets[i] - prediction\n",
    "            total_error += error ** 2\n",
    "            weights[0] += learning_rate * error * inputs[i][0]\n",
    "            weights[1] += learning_rate * error * inputs[i][1]\n",
    "        error_history.append(total_error)\n",
    "        if total_error <= convergence_threshold:\n",
    "            break\n",
    "    return error_history\n",
    "\n",
    "inputs = np.array(purchase_data.iloc[:,1:5])\n",
    "targets = np.array(purchase_data['High Value'])\n",
    "\n",
    "weights = [w1, w2,w3,w4]\n",
    "\n",
    "convergence_threshold = 0.002\n",
    "max_epochs = 1000\n",
    "\n",
    "error_history = train_perceptron(inputs, targets, weights, bias, learning_rate, max_epochs, convergence_threshold)\n",
    "\n",
    "\n",
    "print(\"Testing purchase data:\")\n",
    "for i in range(len(inputs)):\n",
    "    weighted_sum = bias + weights[0] * inputs[i][0] + weights[1] * inputs[i][1]\n",
    "    prediction = step_activation(weighted_sum)\n",
    "    print(f\"Input: {inputs[i]}, Prediction: {prediction}\")\n",
    "\n",
    "print(f\"Number of epochs needed for convergence: {len(error_history)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "05057096",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost of each product:\n",
      " [[ 1.]\n",
      " [55.]\n",
      " [18.]]\n",
      "\n",
      "Enter sample no. of Candies, Mangoes, Milk packets numbers:\n",
      "\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: ''",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\SNS\\Desktop\\Sem 5\\ML\\Lab\\lab5\\Untitled.ipynb Cell 9\u001b[0m line \u001b[0;36m1\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/SNS/Desktop/Sem%205/ML/Lab/lab5/Untitled.ipynb#X10sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m S\u001b[39m=\u001b[39m[]\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/SNS/Desktop/Sem%205/ML/Lab/lab5/Untitled.ipynb#X10sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m1\u001b[39m,\u001b[39m4\u001b[39m):\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/SNS/Desktop/Sem%205/ML/Lab/lab5/Untitled.ipynb#X10sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m     a\u001b[39m=\u001b[39m\u001b[39mint\u001b[39;49m(\u001b[39minput\u001b[39;49m())\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/SNS/Desktop/Sem%205/ML/Lab/lab5/Untitled.ipynb#X10sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m     b\u001b[39m=\u001b[39m[]\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/SNS/Desktop/Sem%205/ML/Lab/lab5/Untitled.ipynb#X10sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m     b\u001b[39m.\u001b[39mappend(a)\n",
      "\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: ''"
     ]
    }
   ],
   "source": [
    "'''A6. Compare the results obtained from above perceptron learning to the ones obtained with matrix pseudo-inverse'''\n",
    "\n",
    "\n",
    "\n",
    "A = np.array(purchase_data.iloc[:, 1:4])  \n",
    "C = np.array(purchase_data.iloc[:, 4:5])  \n",
    "X = np.linalg.pinv(A) @ C\n",
    "print(f'Cost of each product:\\n {X}')\n",
    "\n",
    "print(\"\\nEnter sample no. of Candies, Mangoes, Milk packets numbers:\\n\")\n",
    "S=[]\n",
    "\n",
    "for i in range(1,4):\n",
    "    a=int(input())\n",
    "    b=[]\n",
    "    b.append(a)\n",
    "    S.append(b)\n",
    "\n",
    "Z=X*S\n",
    "if np.sum(Z)>200:\n",
    "    print(\"High value\")\n",
    "else:\n",
    "    print(\"No high value\")\n",
    "\n",
    "print(\"\\nThe results obtained from both perceptron learning and matrix pseudo-inverse are same\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fd80af7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[0.5488135  0.71518937]\n",
      " [0.60276338 0.54488318]]\n",
      "b1 = [[0.43758721]\n",
      " [0.891773  ]]\n",
      "W2 = [[0.4236548 ]\n",
      " [0.64589411]]\n",
      "b2 = [[36.76366276]]\n"
     ]
    }
   ],
   "source": [
    "''''A7. Develop the below Neural Network. Use learning rate (α) = 0.05 with a Sigmoid activation function. Learn the \n",
    "weights of the network using back-propagation algorithm to implement above provided AND gate logic. '''\n",
    "\n",
    "\n",
    "\n",
    "def sigmoid(x):\n",
    "    return (1 // (1 + (math.e)**(-x)))\n",
    "\n",
    "def sigmoid_derivative(x):\n",
    "    return x * (1 - x)\n",
    "\n",
    "np.random.seed(0) \n",
    "\n",
    "W1 = np.random.rand(2, 2) \n",
    "W2 = np.random.rand(2, 1) \n",
    "\n",
    "b1 = np.random.rand(2, 1) \n",
    "b2 = np.random.rand(1, 1) \n",
    "\n",
    "\n",
    "alpha = 0.05\n",
    "\n",
    "X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
    "Y = np.array([[0], [0], [0], [1]])\n",
    "\n",
    "for i in range(1000):  \n",
    "\n",
    "    Z1 = np.dot(X, W1) + b1.T\n",
    "    A1 = sigmoid(Z1)\n",
    "    Z2 = np.dot(A1, W2) + b2.T\n",
    "    A2 = sigmoid(Z2)\n",
    "\n",
    "    dZ2 = A2 - Y\n",
    "    dW2 = np.dot(A1.T, dZ2)\n",
    "    db2 = np.sum(dZ2, axis=0, keepdims=True)\n",
    "    dZ1 = np.dot(dZ2, W2.T) * sigmoid_derivative(A1)\n",
    "    dW1 = np.dot(X.T, dZ1)\n",
    "    db1 = np.sum(dZ1, axis=0, keepdims=True).T  \n",
    "    \n",
    "    W1 -= alpha * dW1\n",
    "    b1 -= alpha * db1  \n",
    "    W2 -= alpha * dW2\n",
    "    b2 -= alpha * db2.T\n",
    "\n",
    "\n",
    "\n",
    "print(\"W1 =\", W1)\n",
    "print(\"b1 =\", b1)\n",
    "print(\"W2 =\", W2)\n",
    "print(\"b2 =\", b2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e61c817",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[0.5488135  0.71518937]\n",
      " [0.60276338 0.54488318]]\n",
      "b1 = [[0.43758721]\n",
      " [0.891773  ]]\n",
      "W2 = [[0.4236548 ]\n",
      " [0.64589411]]\n",
      "b2 = [[36.76366276]]\n"
     ]
    }
   ],
   "source": [
    "'''A8. Repeat the above A1 experiment for XOR Gate logic. Keep the learning rate & activation function same as A1.\n",
    "'''\n",
    "def sigmoid(x):\n",
    "    return (1 // (1 + (math.e)**(-x)))\n",
    "\n",
    "def sigmoid_derivative(x):\n",
    "    return x * (1 - x)\n",
    "\n",
    "np.random.seed(0) \n",
    "\n",
    "W1 = np.random.rand(2, 2) \n",
    "W2 = np.random.rand(2, 1) \n",
    "\n",
    "b1 = np.random.rand(2, 1) \n",
    "b2 = np.random.rand(1, 1) \n",
    "\n",
    "\n",
    "alpha = 0.05\n",
    "\n",
    "X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
    "Y = np.array([[0], [1], [1], [0]])\n",
    "\n",
    "for i in range(1000):  \n",
    "\n",
    "    Z1 = np.dot(X, W1) + b1.T\n",
    "    A1 = sigmoid(Z1)\n",
    "    Z2 = np.dot(A1, W2) + b2.T\n",
    "    A2 = sigmoid(Z2)\n",
    "\n",
    "    dZ2 = A2 - Y\n",
    "    dW2 = np.dot(A1.T, dZ2)\n",
    "    db2 = np.sum(dZ2, axis=0, keepdims=True)\n",
    "    dZ1 = np.dot(dZ2, W2.T) * sigmoid_derivative(A1)\n",
    "    dW1 = np.dot(X.T, dZ1)\n",
    "    db1 = np.sum(dZ1, axis=0, keepdims=True).T  \n",
    "    \n",
    "    W1 -= alpha * dW1\n",
    "    b1 -= alpha * db1  \n",
    "    W2 -= alpha * dW2\n",
    "    b2 -= alpha * db2.T\n",
    "\n",
    "\n",
    "print(\"W1 =\", W1)\n",
    "print(\"b1 =\", b1)\n",
    "print(\"W2 =\", W2)\n",
    "print(\"b2 =\", b2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca4ef427",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[0.5488135  0.71518937]\n",
      " [0.60276338 0.54488318]]\n",
      "b1 = [[0.96366276]\n",
      " [0.38344152]]\n",
      "W2 = [[0.4236548  0.64589411]\n",
      " [0.43758721 0.891773  ]]\n",
      "b2 = [[36.79172504]\n",
      " [36.72889492]]\n"
     ]
    }
   ],
   "source": [
    "'''A9. Repeat exercise A1 & A2 with 2 output nodes (as shown below). A zero output of logic gate maps \n",
    "to [O1 O2] = [1 0] from output layer while a one output from logic gate maps to [0 1].'''\n",
    "\n",
    "\n",
    "\n",
    "def sigmoid(x):\n",
    "    return (1 // (1 + (math.e)**(-x)))\n",
    "\n",
    "def sigmoid_derivative(x):\n",
    "    return x * (1 - x)\n",
    "\n",
    "np.random.seed(0) \n",
    "\n",
    "W1 = np.random.rand(2, 2) \n",
    "W2 = np.random.rand(2, 2) \n",
    "\n",
    "b1 = np.random.rand(2, 1) \n",
    "b2 = np.random.rand(2, 1) \n",
    "\n",
    "\n",
    "alpha = 0.05\n",
    "\n",
    "X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
    "Y = np.array([[1, 0], [1, 0], [1, 0], [0, 1]])\n",
    "\n",
    "for _ in range(1000):  # number of iterations\n",
    "    # forward propagation\n",
    "    Z1 = np.dot(X, W1) + b1.T\n",
    "    A1 = sigmoid(Z1)\n",
    "    Z2 = np.dot(A1, W2) + b2.T\n",
    "    A2 = sigmoid(Z2)\n",
    "\n",
    "    # backward propagation\n",
    "    dZ2 = A2 - Y\n",
    "    dW2 = np.dot(A1.T, dZ2)\n",
    "    db2 = np.sum(dZ2, axis=0, keepdims=True)\n",
    "    dZ1 = np.dot(dZ2, W2.T) * sigmoid_derivative(A1)\n",
    "    dW1 = np.dot(X.T, dZ1)\n",
    "    db1 = np.sum(dZ1, axis=0, keepdims=True).T\n",
    "\n",
    "    # update weights and biases\n",
    "    W1 -= alpha * dW1\n",
    "    b1 -= alpha * db1\n",
    "    W2 -= alpha * dW2\n",
    "    b2 -= alpha * db2.T\n",
    "\n",
    "# print final weights and biases after training\n",
    "print(\"W1 =\", W1)\n",
    "print(\"b1 =\", b1)\n",
    "print(\"W2 =\", W2)\n",
    "print(\"b2 =\", b2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81d791f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AND gate predictions: [0 0 0 1]\n",
      "XOR gate predictions: [0 1 1 0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SNS\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "''''''\n",
    "\n",
    "#A10\n",
    "\n",
    "X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
    "\n",
    "Y_AND = np.array([0, 0, 0, 1])\n",
    "Y_XOR = np.array([0, 1, 1, 0])\n",
    "\n",
    "clf_AND = MLPClassifier(hidden_layer_sizes=(2,),  solver='adam', max_iter=2000)\n",
    "clf_AND.fit(X, Y_AND)\n",
    "\n",
    "clf_XOR = MLPClassifier(hidden_layer_sizes=(2,),  solver='adam', max_iter=2000)\n",
    "clf_XOR.fit(X, Y_XOR)\n",
    "\n",
    "print(\"AND gate predictions:\", clf_AND.predict(X))\n",
    "print(\"XOR gate predictions:\", clf_XOR.predict(X))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cbaeedff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Low' 'High' 'Low' ... 'Low' 'Low' 'Low']\n",
      "0.911\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "        High       0.56      0.76      0.65       214\n",
      "         Low       0.97      0.93      0.95      1786\n",
      "\n",
      "    accuracy                           0.91      2000\n",
      "   macro avg       0.77      0.84      0.80      2000\n",
      "weighted avg       0.93      0.91      0.92      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "'''Use the MLPClassifier() function on your project dataset'''\n",
    "\n",
    "data = pd.read_csv(\"SolarPrediction.csv\")\n",
    "\n",
    "data['Radiation_Class']=  pd.cut(data['Radiation'], \n",
    "                                 bins=[0, 800,  1610], labels=[\"Low\", \"High\"])\n",
    "\n",
    "x= data.iloc[:10000,4:9]\n",
    "y=data['Radiation_Class'].iloc[:10000]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(x,y,test_size=0.20,random_state=42)\n",
    "\n",
    "clf = MLPClassifier(hidden_layer_sizes=(5000,), max_iter=1000)\n",
    "clf.fit(X_train,y_train)\n",
    "prediction=clf.predict(X_test)\n",
    "print(clf.predict(X_test))\n",
    "print(clf.score(X_test,y_test))\n",
    "print(\"Classification Report:\\n\", classification_report(y_test, prediction))"
   ]
  }
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