Bolum_2_3_4.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# *BULANIK MANTIK ve PYTHON UYGULAMALARI* - Öğr. Gör Ali ÇETİNKAYA\n",
        "\n",
        "***ISBN: *** 978-605-4827-98-5\n",
        "\n",
        "## **Bölüm 2.3.4. Örnek Python Kod Uygulaması**\n"
      ],
      "metadata": {
        "id": "wS90MwcjYebd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U scikit-fuzzy # scikit-fuzzy kütüphanesinin kurumunu yapıyoruz.\n",
        "!pip install numpy\n",
        "!pip install matplotlib"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7jsT0KV1cAc0",
        "outputId": "89bd0d1a-becf-46a1-9191-aa62d2afc2c0"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: scikit-fuzzy in /usr/local/lib/python3.9/dist-packages (0.4.2)\n",
            "Requirement already satisfied: networkx>=1.9.0 in /usr/local/lib/python3.9/dist-packages (from scikit-fuzzy) (3.0)\n",
            "Requirement already satisfied: numpy>=1.6.0 in /usr/local/lib/python3.9/dist-packages (from scikit-fuzzy) (1.22.4)\n",
            "Requirement already satisfied: scipy>=0.9.0 in /usr/local/lib/python3.9/dist-packages (from scikit-fuzzy) (1.10.1)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (1.22.4)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.9/dist-packages (3.7.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (1.22.4)\n",
            "Requirement already satisfied: importlib-resources>=3.2.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (5.12.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (1.0.7)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (3.0.9)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (23.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (4.39.3)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (0.11.0)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (8.4.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (1.4.4)\n",
            "Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.9/dist-packages (from importlib-resources>=3.2.0->matplotlib) (3.15.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import skfuzzy as fuzz\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "gX9xmLkEcn8o"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Numpy sürümü: \", np.__version__)\n",
        "print(\"Scikit-fuzzy sürümü: \", fuzz.__version__)\n",
        "print(\"Matplotlib sürümü: \", plt.matplotlib.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bGeAVnP6cK0e",
        "outputId": "c94e416d-f129-44f3-9925-fb999cf57aad"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Numpy sürümü:  1.22.4\n",
            "Scikit-fuzzy sürümü:  0.4.2\n",
            "Matplotlib sürümü:  3.7.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "q_PwtdCYX2jv",
        "outputId": "cf90a131-c147-4125-c441-cb757d69daec"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Bulanık mantık kurulum işlemleri gerçekleştiriliyor. Aşağıdaki kodlarda aşama aşama işlemler bulunmaktadır.\n",
        "# \"0 ile 1 aralığında trapezoidal üyelik fonksiyonu oluştur\"\n",
        "x = np.arange(0, 5.05, 0.1) # [0, 5.05] aralığında 0.1 artan x değerlerine sahip bir dizi oluştur\n",
        "mfx = fuzz.trapmf(x, [2, 2.5, 3, 4.5]) # x dizisi için üyelik fonksiyonu hesapla\n",
        "\n",
        "# # Beş farklı yöntemle üyelik fonksiyonunun üyesini hesaplama işlemi gerçekleştirilecektir.\n",
        "# Bunlar, centroid, bisector, mom, som ve lom 'dur.\n",
        "\n",
        "defuzz_centroid = fuzz.defuzz(x, mfx, 'centroid')  \n",
        "defuzz_bisector = fuzz.defuzz(x, mfx, 'bisector')\n",
        "defuzz_mom = fuzz.defuzz(x, mfx, 'mom')\n",
        "defuzz_som = fuzz.defuzz(x, mfx, 'som')\n",
        "defuzz_lom = fuzz.defuzz(x, mfx, 'lom')\n",
        "\n",
        "# Oluşturulacak Dikey çizgiler için veriler hazırlanıyor\n",
        "labels = ['centroid', 'bisector', 'mean of maximum', 'min of maximum', 'max of maximum']\n",
        "xvals = [defuzz_centroid, defuzz_bisector, defuzz_mom, defuzz_som, defuzz_lom]\n",
        "colors = ['r', 'b', 'g', 'c', 'm']\n",
        "ymax = [fuzz.interp_membership(x, mfx, i) for i in xvals]\n",
        "\n",
        "# Üyelik fonksiyonuna karşı üyelik fonksiyonunun üyesini göster ve karşılaştır\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.plot(x, mfx, 'k')  #Üyelik fonksiyonunu siyah çizgiyle çiz\n",
        "for xv, y, label, color in zip(xvals, ymax, labels, colors):\n",
        "    plt.vlines(xv, 0, y, label=label, color=color) # Dikey çizgileri ekle\n",
        "plt.ylabel('Fuzzy membership') # Y ekseninin başlığı\n",
        "plt.xlabel('Universe variable (arb)')  # X ekseninin başlığı\n",
        "plt.ylim(-0.1, 1.1) # Y ekseninin sınırları\n",
        "plt.legend(loc=2)# Etiketleri göster\n",
        "plt.show() # Sonuç Grafiğini göster"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kitabın Bulunduğu Sayfalar:\n",
        "\n",
        "https://www.pandora.com.tr/kitap/bulanik-mantik-ve-python-uygulamalari/853298\n",
        "\n",
        "https://www.kitapyurdu.com/kitap/bulanik-mantik-ve-python-uygulamalari/644153.html \n",
        "\n",
        "https://www.dr.com.tr/Kitap/Bulanik-Mantik-ve-Python-Uygulamalari/Egitim-Basvuru/Bilgisayar/urunno=0002035935001\n",
        "\n",
        "https://kddb.gelisim.edu.tr/tr/idari-duyuru-igu-yayinlarindan-106-kitap-bulanik-mantik-ve-python-uygulamalari \n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "hYQtlnhFZA2p"
      }
    }
  ]
}