Single Layer Perceptons.ipynb

single-layer-perceptons.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPXAVSFKImnYLfH4bnc6R43",
      "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/336ea42790af9e07bc1b623a40d716af/single-layer-perceptons.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "from decimal import Decimal"
      ],
      "metadata": {
        "id": "DuKzI8C0KfKZ"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MNS3YQPgm3ew"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "pc-GrtQ5IJek"
      },
      "outputs": [],
      "source": [
        "data = [\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",
        "weights = [[0.5,0.5,0.5,0.5,0.5]]\n",
        "targets = [\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",
        "learning_rate=0.1"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = list(zip(data, targets))\n",
        "test_dataset=list(zip(data_validasi,target_validasi))\n",
        "print(len(train_dataset))\n",
        "print(len(test_dataset))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LtaQIJFIlFeA",
        "outputId": "88f1cabd-c908-414e-b554-fbed40893338"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "80\n",
            "20\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def dotproduct_z(vec1,vec2):\n",
        "  product = 0\n",
        "  for i in range(len(vec1)):\n",
        "    product += vec1[i]*vec2[i]\n",
        "  return product\n",
        "\n",
        "prediction = dotproduct_z(data[0],weights[0])\n",
        "print(prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-lKnCH_XJ10z",
        "outputId": "a261f3ed-7f24-4b44-f790-9a1a6031374f"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5.6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def sigmoid(x):\n",
        "  return 1/(1+np.exp(-x))\n",
        "\n",
        "prediction_normalized = sigmoid(prediction)\n",
        "print(prediction_normalized)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bLksdBSeKRPJ",
        "outputId": "8b1d5881-ce09-4beb-c71c-999d4b6568f2"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9963157601005641\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def delta_bias(prediction,target,input):\n",
        "  return 2*(prediction-target)*(1-prediction)*prediction\n",
        "\n",
        "print(delta_bias(prediction_normalized,targets[0],data[0][0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MtCFycv1Or1i",
        "outputId": "c5b2a1c2-768c-437f-93c4-c3a6b6eded6e"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.0073142853212969676\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def delta_weight(prediction,target,input):\n",
        "  return 2*(prediction-target)*(1-prediction)*prediction*input\n",
        "\n",
        "dweight=delta_weight(prediction_normalized,targets[0],data[0][1])\n",
        "print(dweight)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oqFJwzHDbRJm",
        "outputId": "b928429c-e15d-4070-fa24-e4775ea5597f"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.03730285513861453\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def update_weight(prev_weight,delta_weight):\n",
        "  return prev_weight-learning_rate*delta_weight\n",
        "\n",
        "print(update_weight(weights[0][0],dweight))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7mCzA7Ftb35z",
        "outputId": "20385f72-7691-461f-d76b-a2d6ff9fa4a3"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.49626971448613855\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def sum_square_error(prediction,target):\n",
        "  return (prediction-target)**2\n",
        "\n",
        "print(sum_square_error(prediction_normalized,targets[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2lsKCjMiXq0Z",
        "outputId": "4997d50a-b0d0-4b7d-d290-865b3acfb351"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9926450938247648\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def prediction_output(prediction):\n",
        "  return 1 if prediction>0.5 else 0"
      ],
      "metadata": {
        "id": "VOWlwWJxQdpW"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jamL1zY6QyQ_"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the weight\n",
        "dweights =[]\n",
        "weight=[0.5,0.5,0.5,0.5,0.5] #put initial weight for default\n",
        "iterasi = 5\n",
        "errors, y_true,y_score,y_pred,acc=[],[],[],[],[]\n",
        "\n",
        "def evaluasi():\n",
        "  y_true_arr = np.array(y_true, dtype=np.int16)\n",
        "  y_score_arr = np.array(y_score, dtype=np.float32)\n",
        "  y_pred_arr = np.array(y_pred, dtype=np.int16)\n",
        "\n",
        "\n",
        "  tp = int(np.sum((y_pred_arr==1)& (y_true_arr==1)))\n",
        "  tn = int(np.sum((y_pred_arr==0)& (y_true_arr==0)))\n",
        "  fp = int(np.sum((y_pred_arr==1)& (y_true_arr==0)))\n",
        "  fn = int(np.sum((y_pred_arr==0)& (y_true_arr==1)))\n",
        "\n",
        "  accuracy  = (tp+tn)/len(y_true)\n",
        "  precision =  tp/(tp+fp) if (tp+fp)>0 else 0\n",
        "  recall    =  tp/(tp+fn) if (tp+fn)>0 else 0\n",
        "  f1_score  =  2*precision*recall/(precision+recall) if (precision+recall)>0 else 0\n",
        "\n",
        "  # print(f\"accuracy {accuracy}\")\n",
        "  # print(f\"precision {precision}\")\n",
        "  # print(f\"recall {recall}\")\n",
        "  # print(f\"f1_score {f1_score}\")\n",
        "  return accuracy\n",
        "\n",
        "akurasi=[]\n",
        "for putaran in range(iterasi):\n",
        "  for i in range(len(train_dataset)):\n",
        "    # print(f\"===== putaran ke {putaran} data  ke {i} ===========\")\n",
        "    input,target=train_dataset[i]\n",
        "    # print(f\"weight {weight}\")\n",
        "    prediction = dotproduct_z(input,weight)\n",
        "    prediction_normalized = sigmoid(prediction)\n",
        "    prediction_label = prediction_output(prediction_normalized)\n",
        "    # print(f\"prediction_normalized {prediction_normalized}\")\n",
        "    error = sum_square_error(prediction_normalized,target)\n",
        "    w  = []\n",
        "    for j in  range(len(input)):\n",
        "      dweight=delta_weight(prediction_normalized,target,input[j])\n",
        "      dweights.append(dweight)\n",
        "      updated_weight=update_weight(weight[j],dweight)\n",
        "      w.append(updated_weight)\n",
        "    weight=w\n",
        "    # print(f\"error {error}\")\n",
        "    errors.append(error)\n",
        "    #=== evaluate\n",
        "    score = prediction\n",
        "    y_true.append(target)\n",
        "    y_score.append(score)\n",
        "    y_pred.append(prediction_label)\n",
        "    acc=evaluasi()\n",
        "    akurasi.append(acc)\n",
        "\n",
        "\n",
        "xpoints = np.array(range(len(errors)))\n",
        "ypoints = np.array(errors)\n",
        "plt.plot(xpoints,ypoints)\n",
        "plt.show()\n",
        "\n",
        "xpoints = np.array(range(len(akurasi)))\n",
        "ypoints = np.array(akurasi)\n",
        "plt.plot(xpoints,ypoints)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        },
        "id": "lqqUy8fbneUu",
        "outputId": "583123aa-d88b-4db7-9847-b520ace7a07e",
        "collapsed": true
      },
      "execution_count": 87,
      "outputs": [
        {
          "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": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_errors=[]\n",
        "y_true_validasi,y_score_validasi,y_pred_validasi,acc_validasi=[],[],[],[]\n",
        "akurasi_validasi=[]\n",
        "\n",
        "def evaluasi_validasi():\n",
        "  y_true_arr = np.array(y_true_validasi, dtype=np.int16)\n",
        "  y_score_arr = np.array(y_score_validasi, dtype=np.float32)\n",
        "  y_pred_arr = np.array(y_pred_validasi, dtype=np.int16)\n",
        "\n",
        "\n",
        "  tp = int(np.sum((y_pred_arr==1)& (y_true_arr==1)))\n",
        "  tn = int(np.sum((y_pred_arr==0)& (y_true_arr==0)))\n",
        "  fp = int(np.sum((y_pred_arr==1)& (y_true_arr==0)))\n",
        "  fn = int(np.sum((y_pred_arr==0)& (y_true_arr==1)))\n",
        "\n",
        "  accuracy  = (tp+tn)/len(y_true_validasi)\n",
        "  precision =  tp/(tp+fp) if (tp+fp)>0 else 0\n",
        "  recall    =  tp/(tp+fn) if (tp+fn)>0 else 0\n",
        "  f1_score  =  2*precision*recall/(precision+recall) if (precision+recall)>0 else 0\n",
        "\n",
        "  # print(f\"accuracy {accuracy}\")\n",
        "  # print(f\"precision {precision}\")\n",
        "  # print(f\"recall {recall}\")\n",
        "  # print(f\"f1_score {f1_score}\")\n",
        "  return accuracy\n",
        "\n",
        "print(f\"weight {weight}\")\n",
        "for i in range(len(test_dataset)):\n",
        "  data,target=test_dataset[i]\n",
        "  print(f\"data {data}\")\n",
        "  print(f\"target {target}\")\n",
        "  prediction = dotproduct_z(data,weight)\n",
        "  prediction_normalized = sigmoid(prediction)\n",
        "  prediction_validasi_label = prediction_output(prediction_normalized)\n",
        "  error = sum_square_error(prediction_normalized,target)\n",
        "  test_errors.append(Decimal(error))\n",
        "  print(f\" prediction_normalized {prediction_normalized}\")\n",
        "  print(f\" error {Decimal(error)}\")\n",
        "  #=== evaluate\n",
        "  score = prediction\n",
        "  y_true_validasi.append(target)\n",
        "  y_score_validasi.append(score)\n",
        "  y_pred_validasi.append(prediction_validasi_label)\n",
        "  acc=evaluasi_validasi()\n",
        "  akurasi_validasi.append(acc)\n",
        "\n",
        "print(f\"y_true_validasi {y_true_validasi}\")\n",
        "print(f\"y_score_validasi {y_score_validasi}\")\n",
        "print(f\"y_pred_validasi {y_pred_validasi}\")\n",
        "print(f\"akurasi_validasi {akurasi_validasi}\")\n",
        "xpoints = np.array(range(len(test_errors)))\n",
        "ypoints = np.array(test_errors)\n",
        "plt.plot(xpoints,ypoints)\n",
        "plt.show()\n",
        "\n",
        "xpoints_validasi = np.array(range(len(akurasi_validasi)))\n",
        "ypoints_validasi = np.array(akurasi_validasi)\n",
        "plt.plot(xpoints_validasi,ypoints_validasi)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ZXH4HjUAl4dX",
        "outputId": "a30fabe0-5147-405b-929f-f2dd41a460d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "weight [np.float64(0.306729282973312), np.float64(-0.23384832194173494), np.float64(-0.45386501100756144), np.float64(1.139814193604275), np.float64(0.8285000123419288)]\n",
            "data [1, 5.0, 3.5, 1.3, 0.3]\n",
            "target 0\n",
            " prediction_normalized 0.3272319418296006\n",
            " error 0.1070807437535711004183980321613489650189876556396484375\n",
            "data [1, 4.5, 2.3, 1.3, 0.3]\n",
            "target 0\n",
            " prediction_normalized 0.4852120052204085\n",
            " error 0.2354306900100097410533095398932346142828464508056640625\n",
            "data [1, 4.4, 3.2, 1.3, 0.2]\n",
            "target 0\n",
            " prediction_normalized 0.3711905062064745\n",
            " error 0.137782391897818767834138498074025847017765045166015625\n",
            "data [1, 5.0, 3.5, 1.6, 0.6]\n",
            "target 0\n",
            " prediction_normalized 0.46748665682869067\n",
            " error 0.2185437743128660070812685489727300591766834259033203125\n",
            "data [1, 5.1, 3.8, 1.9, 0.4]\n",
            "target 0\n",
            " prediction_normalized 0.47164614488987483\n",
            " error 0.22245008598948079470147831671056337654590606689453125\n",
            "data [1, 4.8, 3.0, 1.4, 0.3]\n",
            "target 0\n",
            " prediction_normalized 0.4174992267724376\n",
            " error 0.174305604355583287912168088951148092746734619140625\n",
            "data [1, 5.1, 3.8, 1.6, 0.2]\n",
            "target 0\n",
            " prediction_normalized 0.3495127880214362\n",
            " error 0.12215918899051740609262850512095610611140727996826171875\n",
            "data [1, 4.6, 3.2, 1.4, 0.2]\n",
            "target 0\n",
            " prediction_normalized 0.3870092717965653\n",
            " error 0.149776176456507759127134704613126814365386962890625\n",
            "data [1, 5.3, 3.7, 1.5, 0.2]\n",
            "target 0\n",
            " prediction_normalized 0.32375988415520024\n",
            " error 0.10482046258818868145201719244141713716089725494384765625\n",
            "data [1, 5.0, 3.3, 1.4, 0.2]\n",
            "target 0\n",
            " prediction_normalized 0.35461210590912945\n",
            " error 0.1257497456573076488606233169775805436074733734130859375\n",
            "data [1, 7.0, 3.2, 4.7, 1.4]\n",
            "target 1\n",
            " prediction_normalized 0.9766709337653247\n",
            " error 0.00054424533138186926943535848266719767707400023937225341796875\n",
            "data [1, 6.4, 3.2, 4.5, 1.5]\n",
            "target 1\n",
            " prediction_normalized 0.9765612280337876\n",
            " error 0.0005493760312841023361885017806116593419574201107025146484375\n",
            "data [1, 6.9, 3.1, 4.9, 1.5]\n",
            "target 1\n",
            " prediction_normalized 0.9839210373063811\n",
            " error 0.0002585330413027870093307936105730959752690978348255157470703125\n",
            "data [1, 5.5, 2.3, 4.0, 1.3]\n",
            "target 1\n",
            " prediction_normalized 0.9737370162475637\n",
            " error 0.0006897443155807329341622224916363848024047911167144775390625\n",
            "data [1, 6.5, 2.8, 4.6, 1.5]\n",
            "target 1\n",
            " prediction_normalized 0.9820453520168368\n",
            " error 0.0003223693841993046705664272000291248332359828054904937744140625\n",
            "data [1, 5.7, 2.8, 4.5, 1.3]\n",
            "target 1\n",
            " prediction_normalized 0.9803375399511719\n",
            " error 0.0003866123351717611587863299149603335536085069179534912109375\n",
            "data [1, 6.3, 3.3, 4.7, 1.6]\n",
            "target 1\n",
            " prediction_normalized 0.9823369076507695\n",
            " error 0.00031198483133744393976105602206416733679361641407012939453125\n",
            "data [1, 4.9, 2.4, 3.3, 1.0]\n",
            "target 1\n",
            " prediction_normalized 0.9347152417201442\n",
            " error 0.00426209966365920060737959573771149734966456890106201171875\n",
            "data [1, 6.6, 2.9, 4.6, 1.3]\n",
            "target 1\n",
            " prediction_normalized 0.9774081948557947\n",
            " error 0.000510389659673742587635281164892830929602496325969696044921875\n",
            "data [1, 5.2, 2.7, 3.9, 1.4]\n",
            "target 1\n",
            " prediction_normalized 0.9698354156956747\n",
            " error 0.000909902146252750446651924587371240704669617116451263427734375\n",
            "y_true_validasi [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "y_score_validasi [np.float64(-0.7207314098736916), np.float64(-0.05916923569375043), np.float64(-0.5271129146405754), np.float64(-0.13023714808983056), np.float64(-0.11353722797337595), np.float64(-0.3330478206211366), np.float64(-0.6211814885230441), np.float64(-0.4599011596684949), np.float64(-0.7365460711710624), np.float64(-0.5988269895459447), np.float64(3.7344497213757633), np.float64(3.729645877054142), np.float64(4.1140338946257415), np.float64(3.6129807774379863), np.float64(4.00178846862342), np.float64(3.909185704347996), np.float64(4.018457048102608), np.float64(2.661483330276699), np.float64(3.7673171328601054), np.float64(3.470457851491247)]\n",
            "y_pred_validasi [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "akurasi_validasi [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\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": {}
        }
      ]
    }
  ]
}