gates_perceptron_1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "gates_perceptron_1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOx1jMFVK9OSxeTCm3x32aK",
      "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/github/irhallac/SisLab/blob/main/gates_perceptron_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ibrahim R Hallac\n",
        "# https://github.com/irhallac\n",
        "# Lojik kapıların Perceptron öğrenme algoritmasıyla modellenmesi"
      ],
      "metadata": {
        "id": "I7sz4vaJQpvM"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "8MkfMisUP8f6"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "giris = np.array([[1, 1], [1, 0], [0, 1], [0, 0]])\n",
        "cikis = np.array([1, 1, 1, 0]) #or\n",
        "\n",
        "# np.array([1, 0, 0, 0]) # and\n",
        "#np.array([1, 1, 1, 0]) #or\n",
        "#np.array([0, 1, 1, 0]) #ex_or"
      ],
      "metadata": {
        "id": "oV822TTdP-Sb"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.title('OR KAPISI', fontsize=16)\n",
        "plt.scatter(giris[:,0], giris[:,1], s=400, c = cikis)\n",
        "plt.grid() \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "NReZcr26P6wD",
        "outputId": "90a277d4-ccab-402f-9722-21c6ee2658c5"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ogrenme_orani = 0.1\n",
        "iter_sayisi = 10\n",
        "\n",
        "\n",
        "w = np.zeros(1 + giris.shape[1])\n",
        "hatalar = []\n",
        "for _ in range(iter_sayisi):\n",
        "  hata = 0\n",
        "  for xi, hedef in zip(giris, cikis):\n",
        "    hesap = np.dot(xi, w[1:]) + w[0]\n",
        "    tahmin = np.where(hesap >= 0.0, 1, 0)\n",
        "    degisim = ogrenme_orani * (hedef - tahmin)\n",
        "    w[1:] += degisim * xi\n",
        "    w[0] += degisim\n",
        "    hata += int(degisim != 0.0)\n",
        "  hatalar.append(hata)"
      ],
      "metadata": {
        "id": "QDrUFqZW6p5v"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hatalar"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QVZKIuxF45H-",
        "outputId": "ecb5692a-a180-4ff4-80c9-7ec56f7dc385"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 0, 0, 0, 0, 0, 0, 0, 0]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "w"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PFywv_8O472b",
        "outputId": "981caeba-8a9c-43ef-dcee-5269eba21147"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.1,  0.1,  0.1])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(1, len(hatalar) + 1), hatalar)\n",
        "plt.xlabel('Deneme No')\n",
        "plt.ylabel('Hatalı tahmin sayısı')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "R_4EIZEVXJpT",
        "outputId": "f19f823f-bc7a-4e66-f02c-94705a8fe261"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "DuyQLjKa5ep8"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}