SLP_v2.ipynb

slp_v2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOQtRxxiesE7Z8BuQnOWeDl",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/omayib/8f7a1732cc665143534828036ae1b7a7/slp_v2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {
        "id": "3282WLfI-pTU"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "from decimal import Decimal"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_train = [\n",
        "    [1, 5.1, 3.5, 1.4, 0.2],\n",
        "    [1, 4.9, 3.0, 1.4, 0.2],\n",
        "    [1, 4.7, 3.2, 1.3, 0.2],\n",
        "    [1, 4.6, 3.1, 1.5, 0.2],\n",
        "    [1, 5.0, 3.6, 1.4, 0.2],\n",
        "    [1, 5.4, 3.9, 1.7, 0.4],\n",
        "    [1, 4.6, 3.4, 1.4, 0.3],\n",
        "    [1, 5.0, 3.4, 1.5, 0.2],\n",
        "    [1, 4.4, 2.9, 1.4, 0.2],\n",
        "    [1, 4.9, 3.1, 1.5, 0.1],\n",
        "    [1, 5.4, 3.7, 1.5, 0.2],\n",
        "    [1, 4.8, 3.4, 1.6, 0.2],\n",
        "    [1, 4.8, 3.0, 1.4, 0.1],\n",
        "    [1, 4.3, 3.0, 1.1, 0.1],\n",
        "    [1, 5.8, 4.0, 1.2, 0.2],\n",
        "    [1, 5.7, 4.4, 1.5, 0.4],\n",
        "    [1, 5.4, 3.9, 1.3, 0.4],\n",
        "    [1, 5.1, 3.5, 1.4, 0.3],\n",
        "    [1, 5.7, 3.8, 1.7, 0.3],\n",
        "    [1, 5.1, 3.8, 1.5, 0.3],\n",
        "    [1, 5.4, 3.4, 1.7, 0.2],\n",
        "    [1, 5.1, 3.7, 1.5, 0.4],\n",
        "    [1, 4.6, 3.6, 1.0, 0.2],\n",
        "    [1, 5.1, 3.3, 1.7, 0.5],\n",
        "    [1, 4.8, 3.4, 1.9, 0.2],\n",
        "    [1, 5.0, 3.0, 1.6, 0.2],\n",
        "    [1, 5.0, 3.4, 1.6, 0.4],\n",
        "    [1, 5.2, 3.5, 1.5, 0.2],\n",
        "    [1, 5.2, 3.4, 1.4, 0.2],\n",
        "    [1, 4.7, 3.2, 1.6, 0.2],\n",
        "    [1, 4.8, 3.1, 1.6, 0.2],\n",
        "    [1, 5.4, 3.4, 1.5, 0.4],\n",
        "    [1, 5.2, 4.1, 1.5, 0.1],\n",
        "    [1, 5.5, 4.2, 1.4, 0.2],\n",
        "    [1, 4.9, 3.1, 1.5, 0.1],\n",
        "    [1, 5.0, 3.2, 1.2, 0.2],\n",
        "    [1, 5.5, 3.5, 1.3, 0.2],\n",
        "    [1, 4.9, 3.1, 1.5, 0.1],\n",
        "    [1, 4.4, 3.0, 1.3, 0.2],\n",
        "    [1, 5.1, 3.4, 1.5, 0.2],\n",
        "    [1, 5.0, 2.0, 3.5, 1.0],\n",
        "    [1, 5.9, 3.0, 4.2, 1.5],\n",
        "    [1, 6.0, 2.2, 4.0, 1.0],\n",
        "    [1, 6.1, 2.9, 4.7, 1.4],\n",
        "    [1, 5.6, 2.9, 3.6, 1.3],\n",
        "    [1, 6.7, 3.1, 4.4, 1.4],\n",
        "    [1, 5.6, 3.0, 4.5, 1.5],\n",
        "    [1, 5.8, 2.7, 4.1, 1.0],\n",
        "    [1, 6.2, 2.2, 4.5, 1.5],\n",
        "    [1, 5.6, 2.5, 3.9, 1.1],\n",
        "    [1, 5.9, 3.2, 4.8, 1.8],\n",
        "    [1, 6.1, 2.8, 4.0, 1.3],\n",
        "    [1, 6.3, 2.5, 4.9, 1.5],\n",
        "    [1, 6.1, 2.8, 4.7, 1.2],\n",
        "    [1, 6.4, 2.9, 4.3, 1.3],\n",
        "    [1, 6.6, 3.0, 4.4, 1.4],\n",
        "    [1, 6.8, 2.8, 4.8, 1.4],\n",
        "    [1, 6.7, 3.0, 5.0, 1.7],\n",
        "    [1, 6.0, 2.9, 4.5, 1.5],\n",
        "    [1, 5.7, 2.6, 3.5, 1.0],\n",
        "    [1, 5.5, 2.4, 3.8, 1.1],\n",
        "    [1, 5.5, 2.4, 3.7, 1.0],\n",
        "    [1, 5.8, 2.7, 3.9, 1.2],\n",
        "    [1, 6.0, 2.7, 5.1, 1.6],\n",
        "    [1, 5.4, 3.0, 4.5, 1.5],\n",
        "    [1, 6.0, 3.4, 4.5, 1.6],\n",
        "    [1, 6.7, 3.1, 4.7, 1.5],\n",
        "    [1, 6.3, 2.3, 4.4, 1.3],\n",
        "    [1, 5.6, 3.0, 4.1, 1.3],\n",
        "    [1, 5.5, 2.5, 4.0, 1.3],\n",
        "    [1, 5.5, 2.6, 4.4, 1.2],\n",
        "    [1, 6.1, 3.0, 4.6, 1.4],\n",
        "    [1, 5.8, 2.6, 4.0, 1.2],\n",
        "    [1, 5.0, 2.3, 3.3, 1.0],\n",
        "    [1, 5.6, 2.7, 4.2, 1.3],\n",
        "    [1, 5.7, 3.0, 4.2, 1.2],\n",
        "    [1, 5.7, 2.9, 4.2, 1.3],\n",
        "    [1, 6.2, 2.9, 4.3, 1.3],\n",
        "    [1, 5.1, 2.5, 3.0, 1.1],\n",
        "    [1, 5.7, 2.8, 4.1, 1.3]\n",
        "]\n",
        "\n",
        "target_train = [\n",
        "    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "    1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "    1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "    1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "    1, 1, 1, 1, 1, 1, 1, 1, 1, 1\n",
        "]\n",
        "\n",
        "data_validasi=[[1, 5.0, 3.5, 1.3, 0.3],\n",
        "    [1, 4.5, 2.3, 1.3, 0.3],\n",
        "    [1, 4.4, 3.2, 1.3, 0.2],\n",
        "    [1, 5.0, 3.5, 1.6, 0.6],\n",
        "    [1, 5.1, 3.8, 1.9, 0.4],\n",
        "    [1, 4.8, 3.0, 1.4, 0.3],\n",
        "    [1, 5.1, 3.8, 1.6, 0.2],\n",
        "    [1, 4.6, 3.2, 1.4, 0.2],\n",
        "    [1, 5.3, 3.7, 1.5, 0.2],\n",
        "    [1, 5.0, 3.3, 1.4, 0.2],\n",
        "    [1, 7.0, 3.2, 4.7, 1.4],\n",
        "    [1, 6.4, 3.2, 4.5, 1.5],\n",
        "    [1, 6.9, 3.1, 4.9, 1.5],\n",
        "    [1, 5.5, 2.3, 4.0, 1.3],\n",
        "    [1, 6.5, 2.8, 4.6, 1.5],\n",
        "    [1, 5.7, 2.8, 4.5, 1.3],\n",
        "    [1, 6.3, 3.3, 4.7, 1.6],\n",
        "    [1, 4.9, 2.4, 3.3, 1.0],\n",
        "    [1, 6.6, 2.9, 4.6, 1.3],\n",
        "    [1, 5.2, 2.7, 3.9, 1.4]]\n",
        "target_validasi=[0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1]\n",
        "\n",
        "dataset_train=list(zip(data_train,target_train))\n",
        "dataset_validasi=list(zip(data_validasi,target_validasi))"
      ],
      "metadata": {
        "id": "qXruYTEd-1fC"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#x and w in vector with equal length.\n",
        "def predict(x,w):\n",
        "  z = np.dot(x[:],w[:])\n",
        "  return z"
      ],
      "metadata": {
        "id": "O3wNGsjU_Nal"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def activation(prediction):\n",
        "  return 1/(1+np.exp(-prediction)) # sigmoid function"
      ],
      "metadata": {
        "id": "VU9rHGdkAn66"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def delta_weight(prediction,target,input):\n",
        "  # print(f\"delta weight p {prediction}, t {target} i {input}\")\n",
        "  return 2*(prediction-target)*(1-prediction)*prediction*input"
      ],
      "metadata": {
        "id": "TkfeiooVA6dR"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def update_weight(prev_weight,delta_weight):\n",
        "  return prev_weight-learning_rate*delta_weight"
      ],
      "metadata": {
        "id": "rDjwzIImBEem"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def sum_square_error(prediction,target):\n",
        "  return (prediction-target)**2"
      ],
      "metadata": {
        "id": "g2NjDj3lBWEK"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def treshold(prediction):\n",
        "  return 1 if prediction>0.5 else 0"
      ],
      "metadata": {
        "id": "MPSVIrcGBbYi"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epoch  = 5\n",
        "weights = [0.5,0.5,0.5,0.5,0.5]\n",
        "learning_rate=0.1\n",
        "\n",
        "correct_prediction=0\n",
        "training_results=[]\n",
        "total_iterations=0\n",
        "for i in range(epoch):\n",
        "  epoch_accuracy = []\n",
        "  epoch_sse=[]\n",
        "  for input,target in dataset_train:\n",
        "    total_iterations +=1\n",
        "    p,weights,sse,output = neuron(input,target,weights,learning_rate)\n",
        "    epoch_sse.append(sse)\n",
        "    if output==target:\n",
        "      correct_prediction+=1\n",
        "    cumulative_accuracy = correct_prediction/total_iterations\n",
        "    epoch_accuracy.append(cumulative_accuracy)\n",
        "  result = {\"epoch\":i,\"weights\":weights,\"accuracy\":np.mean(epoch_accuracy),\"sse\":np.mean(epoch_sse)}\n",
        "  training_results.append(result)\n",
        "\n",
        "print(training_results)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9qAJI1qMBhOF",
        "outputId": "e4738bbe-4e7b-43c7-9385-47f892924064",
        "collapsed": true
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'epoch': 0, 'weights': [np.float64(0.39104918588906895), np.float64(-0.01518306820531763), np.float64(0.06482340374805197), np.float64(0.530225312452814), np.float64(0.5522908294729877)], 'accuracy': np.float64(0.17561275900510495), 'sse': np.float64(0.44883507779858467)}, {'epoch': 1, 'weights': [np.float64(0.36287613383968503), np.float64(-0.08576913894341834), np.float64(-0.09003777521552055), np.float64(0.7154221768016552), np.float64(0.6379464173940884)], 'accuracy': np.float64(0.648075580336007), 'sse': np.float64(0.033556930454006435)}, {'epoch': 2, 'weights': [np.float64(0.340874166762837), np.float64(-0.14375139363770975), np.float64(-0.22694516708629578), np.float64(0.8775139488887291), np.float64(0.7115247312238249)], 'accuracy': np.float64(0.7803476091055688), 'sse': np.float64(0.022247761710662298)}, {'epoch': 3, 'weights': [np.float64(0.322651488896429), np.float64(-0.19250109796518242), np.float64(-0.3486072858625787), np.float64(1.0181069136824743), np.float64(0.7741560100010428)], 'accuracy': np.float64(0.8368050916151608), 'sse': np.float64(0.015928163028984612)}, {'epoch': 4, 'weights': [np.float64(0.306729282973312), np.float64(-0.23384832194173494), np.float64(-0.45386501100756144), np.float64(1.139814193604275), np.float64(0.8285000123419288)], 'accuracy': np.float64(0.871871977037568), 'sse': np.float64(0.012066566131025126)}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def neuron(input,target,weights,learning_rate):\n",
        "  prediction = predict(input,weights)\n",
        "  sigmoid = activation(prediction)\n",
        "  sme=sum_square_error(sigmoid,target)\n",
        "  output = treshold(sigmoid)\n",
        "  temp_w=[]\n",
        "  temp_delta_w=[]\n",
        "  for i in range(len(input)):\n",
        "    delta_w=delta_weight(sigmoid,target,input[i])\n",
        "    temp_delta_w.append(delta_w)\n",
        "    next_w = weights[i]-learning_rate*delta_w\n",
        "    temp_w.append(next_w)\n",
        "\n",
        "  weights=temp_w\n",
        "  return prediction,weights,sme,output"
      ],
      "metadata": {
        "id": "FI3vgUgBQFfj"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_perceptron(dataset,initial_weights,learning_rate,epoch):\n",
        "  weights = list(initial_weights)\n",
        "  correct_prediction=0\n",
        "  training_results=[]\n",
        "  total_iterations=0\n",
        "  for i in range(epoch):\n",
        "    epoch_accuracy = []\n",
        "    epoch_sse=[]\n",
        "    epoch_weight=[]\n",
        "    for input,target in dataset_train:\n",
        "      total_iterations +=1\n",
        "      epoch_weight = weights\n",
        "      p,weights,sse,output = neuron(input,target,weights,learning_rate)\n",
        "      epoch_sse.append(sse)\n",
        "      if output==target:\n",
        "        correct_prediction+=1\n",
        "      cumulative_accuracy = correct_prediction/total_iterations\n",
        "      epoch_accuracy.append(cumulative_accuracy)\n",
        "    result = {\"epoch\":i,\"weights\":epoch_weight,\"accuracy\":np.mean(epoch_accuracy),\"sse\":np.mean(epoch_sse)}\n",
        "    training_results.append(result)\n",
        "  return training_results"
      ],
      "metadata": {
        "id": "cX07-J4LVyWp"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def validate_model(models,validation_dataset):\n",
        "  correct_prediction=0\n",
        "  validation_results=[]\n",
        "  total_iterations=0\n",
        "  for i in range(len(models)):\n",
        "    model = models[i]\n",
        "    weights = model['weights']\n",
        "    model_sse=[]\n",
        "    model_weight=[]\n",
        "    model_accuracy = []\n",
        "    for input,target in validation_dataset:\n",
        "      total_iterations +=1\n",
        "      prediction = predict(input,weights)\n",
        "      sigmoid_output = activation(prediction)\n",
        "      output = treshold(sigmoid_output)\n",
        "      temp_sse = sum_square_error(sigmoid_output,target)\n",
        "      model_sse.append(temp_sse)\n",
        "      if output==target:\n",
        "        correct_prediction+=1\n",
        "      cumulative_accuracy = correct_prediction/total_iterations\n",
        "      # print(f\"iteration {total_iterations} input {input} target {target} weights {weights}  output {output} correct_prediction {correct_prediction} total_iterations {total_iterations} acc {cumulative_accuracy} \")\n",
        "      model_accuracy.append(cumulative_accuracy)\n",
        "\n",
        "    result = {\"epoch\":i,\"weights\":weights,\"accuracy\":np.mean(model_accuracy),\"sse\":np.mean(model_sse)}\n",
        "    validation_results.append(result)\n",
        "  return validation_results"
      ],
      "metadata": {
        "id": "N_OdCFKDy5gX"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Hyperparameters\n",
        "EPOCHS = 5\n",
        "INITIAL_WEIGHTS = [0.5, 0.5, 0.5, 0.5, 0.5]\n",
        "LEARNING_RATE = 0.1\n",
        "\n",
        "models = train_perceptron(dataset_train, INITIAL_WEIGHTS, LEARNING_RATE, EPOCHS)\n",
        "validation_result = validate_model(models,dataset_validasi)\n",
        "\n",
        "print(\"\\n--- Training Summary ---\")\n",
        "training_x=0\n",
        "training_error=[]\n",
        "training_acc=[]\n",
        "for model in models:\n",
        "    # print(f\"Epoch: {model['epoch']}, Avg SSE: {model['sse']:.6f}, Avg Accuracy: {model['accuracy']:.6f}. weights: {model['weights']}\")\n",
        "    training_x+=1\n",
        "    training_error.append(model['sse'])\n",
        "    training_acc.append(model['accuracy'])\n",
        "\n",
        "xpoints = np.array(range(training_x))\n",
        "ypoints = np.array(training_error)\n",
        "plt.plot(xpoints,ypoints)\n",
        "plt.title(\"Training Error\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Error\")\n",
        "plt.show()\n",
        "\n",
        "xpoints = np.array(range(training_x))\n",
        "ypoints = np.array(training_acc)\n",
        "plt.plot(xpoints,ypoints)\n",
        "plt.title(\"Training Accuracy\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.show()\n",
        "\n",
        "print(\"\\n--- Validation Summary ---\")\n",
        "validation_x=0\n",
        "validation_error=[]\n",
        "validation_acc=[]\n",
        "for res_val in validation_result:\n",
        "    # print(f\"Epoch: {res_val['epoch']}, Avg SSE: {res_val['sse']:.6f}, Avg Accuracy: {res_val['accuracy']:.6f}. weights: {res_val['weights']}\")\n",
        "    validation_x+=1\n",
        "    validation_error.append(res_val['sse'])\n",
        "    validation_acc.append(res_val['accuracy'])\n",
        "\n",
        "xpoints = np.array(range(validation_x))\n",
        "ypoints = np.array(validation_error)\n",
        "plt.plot(xpoints,ypoints)\n",
        "plt.title(\"Validation Error\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Error\")\n",
        "plt.show()\n",
        "\n",
        "xpoints = np.array(range(validation_x))\n",
        "ypoints = np.array(validation_acc)\n",
        "plt.plot(xpoints,ypoints)\n",
        "plt.title(\"Validation Accuracy\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1DYI__ieWFCu",
        "outputId": "39a3e8ca-78a4-45b7-9885-cffa56ef0b41"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- Training Summary ---\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- Validation Summary ---\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}