├── Ali Çetinkaya Python.jpg
├── Bolum_2_3_1.ipynb
├── Bolum_2_3_4.ipynb
├── Bolum_2_4_10.ipynb
├── Bolum_9_1.ipynb
├── Bolum_9_2.ipynb
├── Bolum_9_4.ipynb
├── Bulanık Mantık ve Python Uygulamaları - İçindekiler.pdf
├── LICENSE.txt
├── README.md
└── veriler.csv


/Ali Çetinkaya Python.jpg:
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https://raw.githubusercontent.com/acetinkaya/python-fuzzy-logic/0169f7e3be69b553e0c7d5a990ba57e87acd13c8/Ali Çetinkaya Python.jpg


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/Bolum_2_3_1.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |   "nbformat": 4,
 3 |   "nbformat_minor": 0,
 4 |   "metadata": {
 5 |     "colab": {
 6 |       "provenance": []
 7 |     },
 8 |     "kernelspec": {
 9 |       "name": "python3",
10 |       "display_name": "Python 3"
11 |     },
12 |     "language_info": {
13 |       "name": "python"
14 |     }
15 |   },
16 |   "cells": [
17 |     {
18 |       "cell_type": "markdown",
19 |       "source": [
20 |         "# *BULANIK MANTIK ve PYTHON UYGULAMALARI* - Öğr. Gör Ali ÇETİNKAYA\n",
21 |         "\n",
22 |         "***ISBN: *** 978-605-4827-98-5\n",
23 |         "\n",
24 |         "## **Bölüm 2.3.1. Örnek Python Kod Uygulaması**\n"
25 |       ],
26 |       "metadata": {
27 |         "id": "wS90MwcjYebd"
28 |       }
29 |     },
30 |     {
31 |       "cell_type": "code",
32 |       "execution_count": 1,
33 |       "metadata": {
34 |         "colab": {
35 |           "base_uri": "https://localhost:8080/"
36 |         },
37 |         "id": "q_PwtdCYX2jv",
38 |         "outputId": "111734f6-7be8-4672-cd83-4461bf8fd2aa"
39 |       },
40 |       "outputs": [
41 |         {
42 |           "output_type": "stream",
43 |           "name": "stdout",
44 |           "text": [
45 |             "(9,)\n",
46 |             "(2, 3) elemanlı\n",
47 |             "int64\n",
48 |             "int64\n",
49 |             "[[0. 0. 0. 0.]\n",
50 |             " [0. 0. 0. 0.]\n",
51 |             " [0. 0. 0. 0.]]\n",
52 |             "[[1. 1. 1.]\n",
53 |             " [1. 1. 1.]\n",
54 |             " [1. 1. 1.]]\n"
55 |           ]
56 |         }
57 |       ],
58 |       "source": [
59 |         "# Örnek Kod 1 çalışması: Numpy Dizi Uygulaması!\n",
60 |         "\n",
61 |         "import numpy as np # numpy kütüphanesini Python dosyasına çektik.\n",
62 |         "dizi_1 = np.array([1,2,3,4,5,6,7,8,9]) # birinci dizi ve elemanları\n",
63 |         "dizi_2 = np.array([[1,2,9],[3,4,6]]) # ikinci dizi ve elemanları\n",
64 |         "print(dizi_1.shape) # Birinci dizinin eleman sayısını yazdırma \n",
65 |         "print(dizi_2.shape , \"elemanlı\") # İkinci dizinin eleman sayısını yazdırma \n",
66 |         "print(dizi_1.dtype.name) # birinci dizinin eleman tipini yazdırma\n",
67 |         "print(dizi_2.dtype.name) # İkinci dizinin eleman tipini yazdırma\n",
68 |         "sifir_dizisi = np.zeros((3,4))\n",
69 |         "print(sifir_dizisi) # 3x4 boyutlu elemanlarının 0 olan bir dizi oluşur.\n",
70 |         "bir = np.ones((3,3))\n",
71 |         "print(bir) # 3x3 boyutlu tüm elemanlarının “1” olan dizi oluşturulmuştur.\n"
72 |       ]
73 |     },
74 |     {
75 |       "cell_type": "markdown",
76 |       "source": [
77 |         "Kitabın Bulunduğu Sayfalar:\n",
78 |         "\n",
79 |         "https://www.pandora.com.tr/kitap/bulanik-mantik-ve-python-uygulamalari/853298\n",
80 |         "\n",
81 |         "https://www.kitapyurdu.com/kitap/bulanik-mantik-ve-python-uygulamalari/644153.html \n",
82 |         "\n",
83 |         "https://www.dr.com.tr/Kitap/Bulanik-Mantik-ve-Python-Uygulamalari/Egitim-Basvuru/Bilgisayar/urunno=0002035935001\n",
84 |         "\n",
85 |         "https://kddb.gelisim.edu.tr/tr/idari-duyuru-igu-yayinlarindan-106-kitap-bulanik-mantik-ve-python-uygulamalari \n",
86 |         "\n",
87 |         "\n"
88 |       ],
89 |       "metadata": {
90 |         "id": "hYQtlnhFZA2p"
91 |       }
92 |     }
93 |   ]
94 | }


--------------------------------------------------------------------------------
/Bolum_2_3_4.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "provenance": []
  7 |     },
  8 |     "kernelspec": {
  9 |       "name": "python3",
 10 |       "display_name": "Python 3"
 11 |     },
 12 |     "language_info": {
 13 |       "name": "python"
 14 |     }
 15 |   },
 16 |   "cells": [
 17 |     {
 18 |       "cell_type": "markdown",
 19 |       "source": [
 20 |         "# *BULANIK MANTIK ve PYTHON UYGULAMALARI* - Öğr. Gör Ali ÇETİNKAYA\n",
 21 |         "\n",
 22 |         "***ISBN: *** 978-605-4827-98-5\n",
 23 |         "\n",
 24 |         "## **Bölüm 2.3.4. Örnek Python Kod Uygulaması**\n"
 25 |       ],
 26 |       "metadata": {
 27 |         "id": "wS90MwcjYebd"
 28 |       }
 29 |     },
 30 |     {
 31 |       "cell_type": "code",
 32 |       "source": [
 33 |         "!pip install -U scikit-fuzzy # scikit-fuzzy kütüphanesinin kurumunu yapıyoruz.\n",
 34 |         "!pip install numpy\n",
 35 |         "!pip install matplotlib"
 36 |       ],
 37 |       "metadata": {
 38 |         "colab": {
 39 |           "base_uri": "https://localhost:8080/"
 40 |         },
 41 |         "id": "7jsT0KV1cAc0",
 42 |         "outputId": "89bd0d1a-becf-46a1-9191-aa62d2afc2c0"
 43 |       },
 44 |       "execution_count": 16,
 45 |       "outputs": [
 46 |         {
 47 |           "output_type": "stream",
 48 |           "name": "stdout",
 49 |           "text": [
 50 |             "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
 51 |             "Requirement already satisfied: scikit-fuzzy in /usr/local/lib/python3.9/dist-packages (0.4.2)\n",
 52 |             "Requirement already satisfied: networkx>=1.9.0 in /usr/local/lib/python3.9/dist-packages (from scikit-fuzzy) (3.0)\n",
 53 |             "Requirement already satisfied: numpy>=1.6.0 in /usr/local/lib/python3.9/dist-packages (from scikit-fuzzy) (1.22.4)\n",
 54 |             "Requirement already satisfied: scipy>=0.9.0 in /usr/local/lib/python3.9/dist-packages (from scikit-fuzzy) (1.10.1)\n",
 55 |             "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
 56 |             "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (1.22.4)\n",
 57 |             "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
 58 |             "Requirement already satisfied: matplotlib in /usr/local/lib/python3.9/dist-packages (3.7.1)\n",
 59 |             "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (2.8.2)\n",
 60 |             "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (1.22.4)\n",
 61 |             "Requirement already satisfied: importlib-resources>=3.2.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (5.12.0)\n",
 62 |             "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (1.0.7)\n",
 63 |             "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (3.0.9)\n",
 64 |             "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (23.0)\n",
 65 |             "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (4.39.3)\n",
 66 |             "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (0.11.0)\n",
 67 |             "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (8.4.0)\n",
 68 |             "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (1.4.4)\n",
 69 |             "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",
 70 |             "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
 71 |           ]
 72 |         }
 73 |       ]
 74 |     },
 75 |     {
 76 |       "cell_type": "code",
 77 |       "source": [
 78 |         "import numpy as np\n",
 79 |         "import skfuzzy as fuzz\n",
 80 |         "import matplotlib.pyplot as plt"
 81 |       ],
 82 |       "metadata": {
 83 |         "id": "gX9xmLkEcn8o"
 84 |       },
 85 |       "execution_count": 17,
 86 |       "outputs": []
 87 |     },
 88 |     {
 89 |       "cell_type": "code",
 90 |       "source": [
 91 |         "print(\"Numpy sürümü: \", np.__version__)\n",
 92 |         "print(\"Scikit-fuzzy sürümü: \", fuzz.__version__)\n",
 93 |         "print(\"Matplotlib sürümü: \", plt.matplotlib.__version__)"
 94 |       ],
 95 |       "metadata": {
 96 |         "colab": {
 97 |           "base_uri": "https://localhost:8080/"
 98 |         },
 99 |         "id": "bGeAVnP6cK0e",
100 |         "outputId": "c94e416d-f129-44f3-9925-fb999cf57aad"
101 |       },
102 |       "execution_count": 21,
103 |       "outputs": [
104 |         {
105 |           "output_type": "stream",
106 |           "name": "stdout",
107 |           "text": [
108 |             "Numpy sürümü:  1.22.4\n",
109 |             "Scikit-fuzzy sürümü:  0.4.2\n",
110 |             "Matplotlib sürümü:  3.7.1\n"
111 |           ]
112 |         }
113 |       ]
114 |     },
115 |     {
116 |       "cell_type": "code",
117 |       "execution_count": 24,
118 |       "metadata": {
119 |         "colab": {
120 |           "base_uri": "https://localhost:8080/",
121 |           "height": 465
122 |         },
123 |         "id": "q_PwtdCYX2jv",
124 |         "outputId": "cf90a131-c147-4125-c441-cb757d69daec"
125 |       },
126 |       "outputs": [
127 |         {
128 |           "output_type": "display_data",
129 |           "data": {
130 |             "text/plain": [
131 |               "<Figure size 800x500 with 1 Axes>"
132 |             ],
133 |             "image/png": 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\n"
134 |           },
135 |           "metadata": {}
136 |         }
137 |       ],
138 |       "source": [
139 |         "# Bulanık mantık kurulum işlemleri gerçekleştiriliyor. Aşağıdaki kodlarda aşama aşama işlemler bulunmaktadır.\n",
140 |         "# \"0 ile 1 aralığında trapezoidal üyelik fonksiyonu oluştur\"\n",
141 |         "x = np.arange(0, 5.05, 0.1) # [0, 5.05] aralığında 0.1 artan x değerlerine sahip bir dizi oluştur\n",
142 |         "mfx = fuzz.trapmf(x, [2, 2.5, 3, 4.5]) # x dizisi için üyelik fonksiyonu hesapla\n",
143 |         "\n",
144 |         "# # Beş farklı yöntemle üyelik fonksiyonunun üyesini hesaplama işlemi gerçekleştirilecektir.\n",
145 |         "# Bunlar, centroid, bisector, mom, som ve lom 'dur.\n",
146 |         "\n",
147 |         "defuzz_centroid = fuzz.defuzz(x, mfx, 'centroid')  \n",
148 |         "defuzz_bisector = fuzz.defuzz(x, mfx, 'bisector')\n",
149 |         "defuzz_mom = fuzz.defuzz(x, mfx, 'mom')\n",
150 |         "defuzz_som = fuzz.defuzz(x, mfx, 'som')\n",
151 |         "defuzz_lom = fuzz.defuzz(x, mfx, 'lom')\n",
152 |         "\n",
153 |         "# Oluşturulacak Dikey çizgiler için veriler hazırlanıyor\n",
154 |         "labels = ['centroid', 'bisector', 'mean of maximum', 'min of maximum', 'max of maximum']\n",
155 |         "xvals = [defuzz_centroid, defuzz_bisector, defuzz_mom, defuzz_som, defuzz_lom]\n",
156 |         "colors = ['r', 'b', 'g', 'c', 'm']\n",
157 |         "ymax = [fuzz.interp_membership(x, mfx, i) for i in xvals]\n",
158 |         "\n",
159 |         "# Üyelik fonksiyonuna karşı üyelik fonksiyonunun üyesini göster ve karşılaştır\n",
160 |         "plt.figure(figsize=(8, 5))\n",
161 |         "plt.plot(x, mfx, 'k')  #Üyelik fonksiyonunu siyah çizgiyle çiz\n",
162 |         "for xv, y, label, color in zip(xvals, ymax, labels, colors):\n",
163 |         "    plt.vlines(xv, 0, y, label=label, color=color) # Dikey çizgileri ekle\n",
164 |         "plt.ylabel('Fuzzy membership') # Y ekseninin başlığı\n",
165 |         "plt.xlabel('Universe variable (arb)')  # X ekseninin başlığı\n",
166 |         "plt.ylim(-0.1, 1.1) # Y ekseninin sınırları\n",
167 |         "plt.legend(loc=2)# Etiketleri göster\n",
168 |         "plt.show() # Sonuç Grafiğini göster"
169 |       ]
170 |     },
171 |     {
172 |       "cell_type": "markdown",
173 |       "source": [
174 |         "Kitabın Bulunduğu Sayfalar:\n",
175 |         "\n",
176 |         "https://www.pandora.com.tr/kitap/bulanik-mantik-ve-python-uygulamalari/853298\n",
177 |         "\n",
178 |         "https://www.kitapyurdu.com/kitap/bulanik-mantik-ve-python-uygulamalari/644153.html \n",
179 |         "\n",
180 |         "https://www.dr.com.tr/Kitap/Bulanik-Mantik-ve-Python-Uygulamalari/Egitim-Basvuru/Bilgisayar/urunno=0002035935001\n",
181 |         "\n",
182 |         "https://kddb.gelisim.edu.tr/tr/idari-duyuru-igu-yayinlarindan-106-kitap-bulanik-mantik-ve-python-uygulamalari \n",
183 |         "\n",
184 |         "\n"
185 |       ],
186 |       "metadata": {
187 |         "id": "hYQtlnhFZA2p"
188 |       }
189 |     }
190 |   ]
191 | }


--------------------------------------------------------------------------------
/Bolum_2_4_10.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "provenance": []
  7 |     },
  8 |     "kernelspec": {
  9 |       "name": "python3",
 10 |       "display_name": "Python 3"
 11 |     },
 12 |     "language_info": {
 13 |       "name": "python"
 14 |     }
 15 |   },
 16 |   "cells": [
 17 |     {
 18 |       "cell_type": "code",
 19 |       "execution_count": 21,
 20 |       "metadata": {
 21 |         "colab": {
 22 |           "base_uri": "https://localhost:8080/"
 23 |         },
 24 |         "id": "7TFmhWwRkhTs",
 25 |         "outputId": "7526120a-fb1a-4b3d-928e-5c9aa68a9010"
 26 |       },
 27 |       "outputs": [
 28 |         {
 29 |           "output_type": "stream",
 30 |           "name": "stdout",
 31 |           "text": [
 32 |             "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
 33 |             "Requirement already satisfied: pandas in /usr/local/lib/python3.9/dist-packages (1.4.4)\n",
 34 |             "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.9/dist-packages (from pandas) (1.22.4)\n",
 35 |             "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas) (2022.7.1)\n",
 36 |             "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas) (2.8.2)\n",
 37 |             "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n",
 38 |             "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
 39 |             "Requirement already satisfied: matplotlib in /usr/local/lib/python3.9/dist-packages (3.7.1)\n",
 40 |             "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (3.0.9)\n",
 41 |             "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (1.4.4)\n",
 42 |             "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (23.0)\n",
 43 |             "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (8.4.0)\n",
 44 |             "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (1.0.7)\n",
 45 |             "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (2.8.2)\n",
 46 |             "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (0.11.0)\n",
 47 |             "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (1.22.4)\n",
 48 |             "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (4.39.3)\n",
 49 |             "Requirement already satisfied: importlib-resources>=3.2.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (5.12.0)\n",
 50 |             "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",
 51 |             "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
 52 |           ]
 53 |         }
 54 |       ],
 55 |       "source": [
 56 |         "!pip install pandas\n",
 57 |         "!pip install matplotlib\n",
 58 |         "import pandas as pd\n",
 59 |         "import matplotlib.pyplot as plt"
 60 |       ]
 61 |     },
 62 |     {
 63 |       "cell_type": "code",
 64 |       "source": [
 65 |         "print(\"Matplotlib sürümü: \", plt.matplotlib.__version__)"
 66 |       ],
 67 |       "metadata": {
 68 |         "colab": {
 69 |           "base_uri": "https://localhost:8080/"
 70 |         },
 71 |         "id": "XEOlJIkLlK8C",
 72 |         "outputId": "55957284-f54f-4918-e8bd-f50959748639"
 73 |       },
 74 |       "execution_count": 22,
 75 |       "outputs": [
 76 |         {
 77 |           "output_type": "stream",
 78 |           "name": "stdout",
 79 |           "text": [
 80 |             "Matplotlib sürümü:  3.7.1\n"
 81 |           ]
 82 |         }
 83 |       ]
 84 |     },
 85 |     {
 86 |       "cell_type": "code",
 87 |       "source": [
 88 |         "# Google Drive'a bağlanın\n",
 89 |         "from google.colab import drive\n",
 90 |         "drive.mount('/content/drive')"
 91 |       ],
 92 |       "metadata": {
 93 |         "colab": {
 94 |           "base_uri": "https://localhost:8080/"
 95 |         },
 96 |         "id": "7_CxA6A5lNxL",
 97 |         "outputId": "a9f7fc34-c672-40da-f03d-8a2c42cacedf"
 98 |       },
 99 |       "execution_count": 23,
100 |       "outputs": [
101 |         {
102 |           "output_type": "stream",
103 |           "name": "stdout",
104 |           "text": [
105 |             "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
106 |           ]
107 |         }
108 |       ]
109 |     },
110 |     {
111 |       "cell_type": "code",
112 |       "source": [
113 |         "# Veriler dosyasının yolu\n",
114 |         "veriler_path = '/content/drive/MyDrive/Colab Notebooks/veriler.csv'"
115 |       ],
116 |       "metadata": {
117 |         "id": "3X-HXC6-lPd9"
118 |       },
119 |       "execution_count": 24,
120 |       "outputs": []
121 |     },
122 |     {
123 |       "cell_type": "code",
124 |       "source": [
125 |         "veri = pd.read_csv(veriler_path)\n",
126 |         "print(veri.columns)\n",
127 |         "\n",
128 |         "boy = veri['boy']\n",
129 |         "print(boy)\n",
130 |         "\n",
131 |         "boy = veri[['boy']]\n",
132 |         "print(boy)\n",
133 |         "\n",
134 |         "boykilo = veri[['boy', 'kilo']]\n",
135 |         "boykilo.columns = ['Boy D. (cm)', 'Kilo D. (kg)']\n",
136 |         "print(boykilo)\n",
137 |         "\n",
138 |         "plt.plot(boykilo)\n",
139 |         "plt.legend(('Boy D. (cm)', 'Kilo D. (kg)'), loc='upper left') \n",
140 |         "plt.title('Veri Tablosu - Boy & Kilo')\n",
141 |         "plt.ylabel('Y Ekseni - Veri Ölçüm Değeri')\n",
142 |         "plt.xlabel('X Ekseni - Dizi İçerisinde Kişi Sıra Bilgisi')\n",
143 |         "plt.figure(figsize=(10,5))\n",
144 |         "plt.show()"
145 |       ],
146 |       "metadata": {
147 |         "colab": {
148 |           "base_uri": "https://localhost:8080/",
149 |           "height": 1000
150 |         },
151 |         "id": "dDwNszYwkh9n",
152 |         "outputId": "917832a9-3abe-42fc-b833-bace1475b5ac"
153 |       },
154 |       "execution_count": 25,
155 |       "outputs": [
156 |         {
157 |           "output_type": "stream",
158 |           "name": "stdout",
159 |           "text": [
160 |             "Index(['ID', 'boy', 'kilo'], dtype='object')\n",
161 |             "0     130\n",
162 |             "1     125\n",
163 |             "2     135\n",
164 |             "3     133\n",
165 |             "4     129\n",
166 |             "5     180\n",
167 |             "6     190\n",
168 |             "7     175\n",
169 |             "8     177\n",
170 |             "9     185\n",
171 |             "10    165\n",
172 |             "11    155\n",
173 |             "12    160\n",
174 |             "13    162\n",
175 |             "14    167\n",
176 |             "15    174\n",
177 |             "16    193\n",
178 |             "17    187\n",
179 |             "18    183\n",
180 |             "19    159\n",
181 |             "20    164\n",
182 |             "21    166\n",
183 |             "Name: boy, dtype: int64\n",
184 |             "    boy\n",
185 |             "0   130\n",
186 |             "1   125\n",
187 |             "2   135\n",
188 |             "3   133\n",
189 |             "4   129\n",
190 |             "5   180\n",
191 |             "6   190\n",
192 |             "7   175\n",
193 |             "8   177\n",
194 |             "9   185\n",
195 |             "10  165\n",
196 |             "11  155\n",
197 |             "12  160\n",
198 |             "13  162\n",
199 |             "14  167\n",
200 |             "15  174\n",
201 |             "16  193\n",
202 |             "17  187\n",
203 |             "18  183\n",
204 |             "19  159\n",
205 |             "20  164\n",
206 |             "21  166\n",
207 |             "    Boy D. (cm)  Kilo D. (kg)\n",
208 |             "0           130            30\n",
209 |             "1           125            36\n",
210 |             "2           135            34\n",
211 |             "3           133            30\n",
212 |             "4           129            38\n",
213 |             "5           180            90\n",
214 |             "6           190            80\n",
215 |             "7           175            90\n",
216 |             "8           177            60\n",
217 |             "9           185           105\n",
218 |             "10          165            55\n",
219 |             "11          155            50\n",
220 |             "12          160            58\n",
221 |             "13          162            59\n",
222 |             "14          167            62\n",
223 |             "15          174            70\n",
224 |             "16          193            90\n",
225 |             "17          187            80\n",
226 |             "18          183            88\n",
227 |             "19          159            40\n",
228 |             "20          164            66\n",
229 |             "21          166            56\n"
230 |           ]
231 |         },
232 |         {
233 |           "output_type": "display_data",
234 |           "data": {
235 |             "text/plain": [
236 |               "<Figure size 640x480 with 1 Axes>"
237 |             ],
238 |             "image/png": 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\n"
239 |           },
240 |           "metadata": {}
241 |         },
242 |         {
243 |           "output_type": "display_data",
244 |           "data": {
245 |             "text/plain": [
246 |               "<Figure size 1000x500 with 0 Axes>"
247 |             ]
248 |           },
249 |           "metadata": {}
250 |         }
251 |       ]
252 |     }
253 |   ]
254 | }


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/Bulanık Mantık ve Python Uygulamaları - İçindekiler.pdf:
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https://raw.githubusercontent.com/acetinkaya/python-fuzzy-logic/0169f7e3be69b553e0c7d5a990ba57e87acd13c8/Bulanık Mantık ve Python Uygulamaları - İçindekiler.pdf


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/LICENSE.txt:
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 1 | MIT License
 2 | 
 3 | Copyright (c) 2023 Lect. Ali Cetinkaya (MSc.) 
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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/README.md:
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 1 | ## BULANIK MANTIK ve PYTHON UYGULAMALARI
 2 | 
 3 | Gelişen ve değişen teknoloji ile cihazların kontrolü üzerindeki ihtiyaç günden güne artmaktadır. Mevcut sistemlerin kontrolünde, yazılım dilleri üzerinde geliştirilen algoritmaların önemi büyüktür. Bulanık mantık (BM) sistemlerin kontrolü hayatımızın ayrılmaz bir parçası haline gelmiştir. Bulanık mantık hakkında yabancı dillerde çok sayıda çalışma mevcuttur. Bunlar arge araştırmaları, kitaplar, ders notları, makaleler ve bildiri çalışmalarıdır. Bu yayınlar arasında günümüz programlama dillerinden olan Python programlama dili ile hazırlanmış kapsamlı bir Türkçe Bulanık mantık kaynağı yer almamaktadır. Bu kitap Python programlama dili üzerinde geliştirilmiş bulanık mantık çalışmaları konusunda Türkçe literatüre katkı sağlayacaktır.  
 4 | 
 5 | Kitaptaki uygulamalar ve anlatımlar çok sayıda kişinin faydalanabilmesi açısından öğrenilmesi en kolay olan Python programlama dilinde hazırlanmıştır. Bu kitap 9 bölümden oluşmaktadır. Kitap içeriği örnek uygulamalara ağırlık verilerek Python programlama dili ile hazırlanmıştır. Uygulamaların açıklamaları Python kod blokları içerisinde ayrıntılı olarak verilmiştir. Kitabın birinci bölümde kitap ve bulanık mantık hakkında bilgilerin bulunduğu giriş bulunmaktadır. İkinci bölümde Python geliştirme ortamı anlatımı, kurulumu ve kütüphanelerinden bahsedilmiştir. Üçüncü bölümde bulanık mantığın avantajı, dezavantajı ve uygulama alanları verilmiştir. Dördüncü bölümde bulanık küme kavramı ve kural tabanı anlatılmıştır. Beşinci bölümde üyelik fonksiyonlarına yer verilmiştir. Altıncı bölümde sözel değişkenler ve IF-THEN kural yapısından bahsedilmiştir. Yedinci bölümde bulanık mantık uygulaması gerçekleştirirken izlenecek aşamalar anlatılmıştır. Sekizinci bölümde bulanık mantık sisteminin akış şeması yer almaktadır. Son olarak dokuzuncu bölümde bulanık mantık uygulamaları verilerek örnek Python kodları ile açıklamaları yer almaktadır.
 6 | 
 7 | Kitabın hedef kitlesini Fen, Sosyal ve Sözel alanlarında araştırma yapan tüm öğrenciler oluşturmaktadır. Bu yüzden Yazılım, Bilgisayar, Elektronik, Mekatronik, İktisat ve İşletme bölümlerinde ön lisans, lisans ve yüksek lisans öğrencileri için hazırlanmıştır. Özellikle ders içi proje ödevlerinde yardımcı kaynak olarak kullanılması amaçlanmıştır. Okuyucular bu kitaptan ve kodlardan yararlanarak kendi alanlarında Python programlama dilini kullanarak bulanık mantık sistemlerini geliştirebilirler. 
 8 | 
 9 | Bu kitabın hazırlanmasında kullanılan Python programlama dili ile bulanık mantık uygulamalarının bilimsel, akademik ve araştırma amaçlı tüm projelerinizde faydalı olmasını temenni ederim. Kitabın geliştirilme sürecinde gelecek baskılarında örnekler çoğaltılarak olası hata ve yanlışlıklarını giderilmesi planlanmaktadır. İlgi gösterecek olan herkese desteklerinden dolayı teşekkür ve saygılarımı iletiyorum.
10 | 
11 | Öğr. Gör. Ali ÇETİNKAYA  
12 | İstanbul, 2023
13 | 
14 | ---
15 | 
16 | ## Author Info.:
17 | 
18 | - [**Ali Çetinkaya**](https://scholar.google.com.tr/citations?user=XSEW-NcAAAAJ)    
19 |   İstanbul Gelişim Üniversitesi, İstanbul Gelişim Meslek Yüksekokulu, Elektronik Teknolojisi Programı, İstanbul / Türkiye  
20 |   Istanbul Gelisim University, Istanbul Gelisim Vocational School, Electronics Technology Program, Istanbul / Turkey  
21 |   
22 | *For Correspondence: alcetinkaya@gelisim.edu.tr*
23 | 
24 | ---
25 | 
26 | ** Book Title / Kitap İsmi: BULANIK MANTIK ve PYTHON UYGULAMALARI
27 | 
28 | ** ISBN No: 978-605-4827-98-5
29 | 
30 | ** Basım Yılı / Published: 2023
31 | 
32 | ** Yayınevi: İstanbul Gelişim Üniversitesi
33 | 
34 | ** Kitap Yayın Bilgisi: [İGÜ Yayınları İnternet Sayfası](https://iguyayinlari.gelisim.edu.tr/tr/idari-duyuru-igu-yayinlarindan-106-kitap-bulanik-mantik-ve-python-uygulamalari)
35 | 
36 | ** Kitap [Google Scholar](https://scholar.google.com.tr/citations?view_op=view_citation&hl=tr&user=XSEW-NcAAAAJ&sortby=pubdate&citation_for_view=XSEW-NcAAAAJ:9ZlFYXVOiuMC) ve [İGÜ Avesis Sayfası](https://avesis.gelisim.edu.tr/yayin/2d914227-34cf-492c-a9cd-86e10745f0b5/bulanik-mantik-ve-python-uygulamalari) 
37 | 
38 | ---
39 | 
40 | ## How to Cite / Nasıl Referans-Alıntı Yapılır?
41 | 
42 | - **IEEE**: A. Çetinkaya. Bulanık Mantık ve Python Uygulamaları. İstanbul: İstanbul Gelişim Üniversitesi 2023, pp.144
43 | 
44 | - **APA**: Çetinkaya, A., (2023). Bulanık Mantık ve Python Uygulamaları . İstanbul: İstanbul Gelişim Üniversitesi.
45 | 
46 | - **MLA**: Çetinkaya, ALİ. Bulanık Mantık ve Python Uygulamaları . İstanbul Gelişim Üniversitesi , 2023.
47 | 
48 | --- 
49 | 
50 | ![alternatif metin](https://github.com/acetinkaya/python-fuzzy-logic/blob/main/Ali%20%C3%87etinkaya%20Python.jpg)
51 | 
52 | ---
53 | 
54 | Proje Durumu:
55 | İlgili paylaşımlar ve Python programlama dilinde yazılmış yazılım kodlarına sürüm güncellemeleri yaptıkça bu paylaşımları güncelleyeceğiz. GitHub bölümünden beğeni bildirimi olarak bir yıldız vererek çalışmalarımı destekleyebilirsiniz. Bilgi paylaşıldıkça büyür ve gelişir.
56 | 
57 | Katkıda Bulunma:
58 | Çekme istekleri memnuniyetle karşılanır. Büyük değişiklikler için lütfen önce neyi değiştirmek istediğinizi görüşmek üzere ilgili Python kodunu belirttiğiniz bir soru - yanıt bölümü açın. 
59 |  
60 | Lisans: 
61 | [MIT Lisansı](http://mit-license.org/) altında yayımlandı
62 | 
63 | Yazar ve Güncelleme Yapan: [Öğr. Gör. Ali Çetinkaya (MSc.)](https://github.com/acetinkaya) - 2023
64 | 
65 | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
66 | 
67 | Project Status:
68 | We will update these shares as we make version updates to the related dependencies and software code written in Python programming language. You can support my work by giving a star as a like notification from the GitHub section. Knowledge grows and develops as it is shared.
69 | 
70 | Contributing:
71 | Pull requests are welcome. For major changes, please open a question-and-answer section indicating the relevant Python code to discuss what you'd like to change first.
72 | 
73 | License:
74 | Released under the [MIT License](http://mit-license.org/)
75 | 
76 | Authored and Maintained by [Lect. Ali Cetinkaya (MSc.)](https://github.com/acetinkaya) - 2023
77 | 


--------------------------------------------------------------------------------
/veriler.csv:
--------------------------------------------------------------------------------
 1 | "ID","boy","kilo"
 2 | "0","130","30"
 3 | "1","125","36"
 4 | "2","135","34"
 5 | "3","133","30"
 6 | "4","129","38"
 7 | "5","180","90"
 8 | "6","190","80"
 9 | "7","175","90"
10 | "8","177","60"
11 | "9","185","105"
12 | "10","165","55"
13 | "11","155","50"
14 | "12","160","58"
15 | "13","162","59"
16 | "14","167","62"
17 | "15","174","70"
18 | "16","193","90"
19 | "17","187","80"
20 | "18","183","88"
21 | "19","159","40"
22 | "20","164","66"
23 | "21","166","56"
24 | 


--------------------------------------------------------------------------------