├── .ipynb_checkpoints
    ├── Adalin_regression-checkpoint.ipynb
    ├── boston_AdalineRegressor-checkpoint.ipynb
    ├── classification-checkpoint.ipynb
    ├── iris_Knn&confusion-checkpoint.ipynb
    └── iris_classifier-checkpoint.ipynb
├── Adalin_regression.ipynb
├── Persepron
    ├── .ipynb_checkpoints
    │   └── perceptron-classification_class-checkpoint.ipynb
    ├── linear_data_test.csv
    ├── linear_data_train.csv
    ├── perceptron-classification.py
    ├── perceptron-classification_class.ipynb
    └── perceptron-classification_class.py
├── Titanic.ipynb
├── WeatherHistory
    ├── .ipynb_checkpoints
    │   └── Weather-checkpoint.ipynb
    ├── W.npy
    ├── Weather.ipynb
    ├── b.npy
    └── weatherHistory.csv
├── boston_AdalineRegressor.ipynb
├── classification.ipynb
├── iris_Knn&confusion.ipynb
└── iris_classifier.ipynb


/.ipynb_checkpoints/classification-checkpoint.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 24,
  6 |    "id": "4d3e23f7",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import numpy as np\n",
 11 |     "import matplotlib.pyplot as plt\n",
 12 |     "from numpy.linalg import inv"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 9,
 18 |    "id": "9706111e",
 19 |    "metadata": {},
 20 |    "outputs": [],
 21 |    "source": [
 22 |     "def generate_data(n):\n",
 23 |     "    hair_length_female = np.random.normal(40,7,n)\n",
 24 |     "    hair_length_man=np.random.normal(10,3,n)\n",
 25 |     "    \n",
 26 |     "    label_female=np.zeros(n,dtype='int')\n",
 27 |     "    label_man=np.ones(n,dtype='int')\n",
 28 |     "    \n",
 29 |     "    hair_length=np.concatenate((hair_length_female,hair_length_man))\n",
 30 |     "    gender=np.concatenate((label_female,label_man))\n",
 31 |     "    return hair_length,gender"
 32 |    ]
 33 |   },
 34 |   {
 35 |    "cell_type": "code",
 36 |    "execution_count": 10,
 37 |    "id": "91c456e2",
 38 |    "metadata": {},
 39 |    "outputs": [],
 40 |    "source": [
 41 |     "n=100\n",
 42 |     "X,Y=generate_data(n)\n",
 43 |     "X=X.reshape(-1,1)\n",
 44 |     "Y=Y.reshape(-1,1)\n"
 45 |    ]
 46 |   },
 47 |   {
 48 |    "cell_type": "code",
 49 |    "execution_count": 13,
 50 |    "id": "2e9aa9c6",
 51 |    "metadata": {},
 52 |    "outputs": [
 53 |     {
 54 |      "data": {
 55 |       "text/plain": [
 56 |        "<matplotlib.legend.Legend at 0xd117a08>"
 57 |       ]
 58 |      },
 59 |      "execution_count": 13,
 60 |      "metadata": {},
 61 |      "output_type": "execute_result"
 62 |     },
 63 |     {
 64 |      "data": {
 65 |       "image/png": 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3bUTcFhHtEdHe2upj1GZmlZRmEOwExuUNtyVt3UYBbwd+Jmk7MB1Y6gPGZmbVlWYQrAQmSpogaQRwGXlfTouIvRExJiLGR8R44HHgoojoSLEmMzPrJbUgiIgDwKeB5cAmYHFEbJC0QJJvcWlmNkSUeqvKAYmIZcCyXm03FJn27DRrMTOzwmp2sNjMzIYGB4GZWcY5CMzMMs5BYGaWcQ4CM7OMcxCYmWWcg8DMLOMcBGZmGecgMDPLOAeBmVnGOQjMzDLOQWBmlnEOAjOzjHMQmJllnIPAzCzjHARmZhnnIDAzyzgHgZlZxjkIzMwyzkFgZpZxDgIzs4xzEJiZZZyDwMws4xwEZmYZ5yAwM8s4B4GZWcY5CMzMMs5BYGaWcQ4CM7OMcxCYmWVcqkEgaZakLZK2Srq2wPjPS9ooaZ2khyW9Oc16zMzsSKkFgaRG4GbgfGAycLmkyb0mWw20R8SpwH3AX6dVj5mZFZbmFsEZwNaI2BYRrwB3AxfnTxARj0bES8ng40BbivWYmVkBaQbBWGBH3nBn0lbMx4B/LjRC0lxJHZI6du3aVcESzcxsSBwslvRRoB1YWGh8RNwWEe0R0d7a2lrd4szMhrmjUux7JzAub7gtaTuMpPcB1wP/JSJeTrEeMzMrIM0tgpXAREkTJI0ALgOW5k8gaRrw98BFEfFsirWYmVkRqQVBRBwAPg0sBzYBiyNig6QFki5KJlsIvAa4V9IaSUuLdGdmZilJc9cQEbEMWNar7Ya8x+9L8/XNzKx/Q+JgsZmZ1Y6DwMws4xwEZmYZ5yAwM8s4B4GZWcY5CMzMMs5BYGaWcQ4CM7OMcxCYmWWcg8DMLOMcBGZmGecgMDPLOAeBmVnGOQjMzDLOQWBmlnEOAjOzjHMQmJllnIPAzCzjHARmZhnnIDAzyzgHgZlZxjkIzMwyzkFgZpZxDgIzs4xzEJiZZZyDwMws4xwEZmYZ5yAwM8s4B4GZWcY5CMzMMs5BYGaWcUel2bmkWcA3gEbgHyLia73GHw3cAZwO7AY+EhHb06zJ6s+S1TtZuHwLz+zZx/Etzcw77yRmTxtbsX4KtXc8/Rx3PbGDgxEIOGZEIy+9crBnPMDC5VvYuWcfjRIHIxjbq88vP7CB51/qKlpPc1MDI5saef6lLgRE0t4gOOstr2fj71/oeX7++G5vGjWCP7zYxcHoPaZyJr7xWF565RDP7NlHyzFNvLi/i65DA+urpbmJD0w9jkc37+KZPfsY2dTAvhI7671s5y/dwJ59ry7bluYm5l90Ss96UWz550/X+30/Z1JrT22lrGf5zx/d3ETXwUP88ZWDBevp67nlvtZg/gaKUaS0EklqBH4LnAt0AiuByyNiY940VwGnRsQnJF0G/ElEfKSvftvb26OjoyOVmm3oWbJ6J9fd/yT7ug72tDU3NfJXl0wp6w+hWD8fPH0s/7Rq52HtjQ3i4KHifxdNjYKArgLTdPd5z8oddB1M7wM6i3qW7YodBZd9U4NYeOlUAObdt7bo8m9qEB85Y9wR73uh1yu2nhVan4rV0/v55a7TlfobkLQqItoLjUtz19AZwNaI2BYRrwB3Axf3muZi4B+Tx/cBMyUpxZqszixcvuWIP7Z9XQdZuHxLRfq564kdR7T3FQIAXQej4AdRfp8OgcrrWbZFln3XoWDh8i0sXL6lz+XfdSgKvu+FXq/YelZofSpWTynPLfe1BvI30Jc0g2AssCNvuDNpKzhNRBwA9gJv6N2RpLmSOiR17Nq1K6VybSh6Zs++strL7SeN3Spp7qrJuv6W7TN79pW0bpT6Hg12/Ss0Xbl9VupvoC91cbA4Im6LiPaIaG9tba11OVZFx7c0l9Vebj+NKWyAptGn5fS3bI9vaS5p3Sj1PRrs+ldounL7rNTfQF/SDIKdwLi84bakreA0ko4CRpM7aGwGwLzzTqK5qfGwtuamxp4DtoPt5/Izxx3R3tjQ94dEU6NoKjJNd59NjQ6DSutZtkWWfVODmHfeScw776Q+l39Tgwq+74Ver9h6Vmh9KlZPKc8t97UG8jfQlzSDYCUwUdIESSOAy4ClvaZZClyZPP4Q8EikdfTa6tLsaWP5q0umMLalGZE7e6Tcg2R99fM/Z085ov1vLp3KR6ef0PNfo4BjRzT2jF/4oaksvHQqY5P/yLqny+9z4Yem8rpjmvqsqbmpoWea/I+tBsGMt77+sOcX+lh706gRqW99THzjsT3L5nXHNNE0iE+MluYmPjr9hJ7+msvo7LBle+lUWpoPX7YtzU09B2ZnTxtbdPl3T1fofc+vrb/1rPf61NLcxLEjGo94nULPL3edrtTfQF9SO2sIQNIFwN+RO330exHxvyQtADoiYqmkkcD3gWnAc8BlEbGtrz591pCZWfn6Omso1e8RRMQyYFmvthvyHu8HLk2zBjMz61tdHCw2M7P0OAjMzDLOQWBmlnEOAjOzjEv1rKE0SNoFPF3hbscAf6hwn9XmeRg6hsN8eB6GhkrOw5sjouA3cusuCNIgqaPYaVX1wvMwdAyH+fA8DA3VmgfvGjIzyzgHgZlZxjkIcm6rdQEV4HkYOobDfHgehoaqzIOPEZiZZZy3CMzMMs5BYGaWcZkPAkmzJG2RtFXStbWupxSSvifpWUnr89peL+lBSU8lv19Xyxr7I2mcpEclbZS0QdJnk/a6mQ9JIyWtkLQ2mYcvJ+0TJD2RrFP3JJdhH9IkNUpaLenHyXA9zsN2SU9KWiOpI2mrm/UJQFKLpPskbZa0SdJZ1ZiHTAeBpEbgZuB8YDJwuaTJta2qJIuAWb3argUejoiJwMPJ8FB2ALg6IiYD04FPJcu+nubjZeC9ETEVOA2YJWk68HXgbyPibcDzwMdqV2LJPgtsyhuux3kAOCciTss7976e1ieAbwA/jYhJwFRy70n68xARmf0BzgKW5w1fB1xX67pKrH08sD5veAtwXPL4OGBLrWssc35+BJxbr/MBHAP8BjiT3DdBj0raD1vHhuIPubsHPgy8F/gxufvg1NU8JHVuB8b0aqub9YncHRr/heQknmrOQ6a3CICxwI684c6krR69KSJ+nzz+N+BNtSymHJLGk7s50RPU2Xwku1TWAM8CDwK/A/ZExIFkknpYp/4OuAY4lAy/gfqbB4AA/q+kVZLmJm31tD5NAHYB/zvZTfcPko6lCvOQ9SAYliL3r0NdnBcs6TXAPwGfi4j/yB9XD/MREQcj4jRy/1WfAUyqbUXlkfQB4NmIWFXrWirg3RHxDnK7ej8l6T/nj6yD9eko4B3AdyJiGvBHeu0GSmsesh4EO4FxecNtSVs9+ndJxwEkv5+tcT39ktRELgTujIj7k+a6mw+AiNgDPEpuN0qLpO67/w31dWoGcJGk7cDd5HYPfYP6mgcAImJn8vtZ4Ifkgrme1qdOoDMinkiG7yMXDKnPQ9aDYCUwMTlDYgRwGbC0xjUN1FLgyuTxleT2uQ9ZkgTcDmyKiJvyRtXNfEhqldSSPG4md4xjE7lA+FAy2ZCeh4i4LiLaImI8ufX/kYi4gjqaBwBJx0oa1f0YeD+wnjpanyLi34Adkk5KmmYCG6nGPNT6AEmtf4ALgN+S27d7fa3rKbHmu4DfA13k/ov4GLn9ug8DTwEPAa+vdZ39zMO7yW3irgPWJD8X1NN8AKcCq5N5WA/ckLS/BVgBbAXuBY6uda0lzs/ZwI/rcR6SetcmPxu6/5braX1K6j0N6EjWqSXA66oxD77EhJlZxmV915CZWeY5CMzMMs5BYGaWcQ4CM7OMcxCYmWWcg8CGLUkv9hqeI+nb/TznolKuQivp7O4rdaYhuQrlVdV6Pcs2B4FZnohYGhFf692e9y3bamkBrupvIrNKcBBYJkm6MLne/mpJD0l6U9Les9UgaZGkWyU9Afx1if2+X9KvJf1G0r3JtZS6r5X/5aT9SUmTkvbW5BrzG5KLjD0taQzwNeCtybX1FybdvybvWvV3Jt/ONhs0B4ENZ83JB+ma5AqhC/LGPQZMj9zFve4md/XNQtqAd0XE5/t7seQD/IvA+yJ38bMOIP95f0javwP896TtL8ld1uEUcteWOSFpvxb4XeSurT8vaZsGfI7cvTPeQu46QWaDVu3NXbNq2he5K4MCuf/2ge4blrQB9yQX8RpB7jrwhdwbEQdLfL3p5D6kf5n8sz4C+HXe+O4L660CLkkevxv4E4CI+Kmk5/vof0VEdCbzsobcPSkeK7E2s6IcBJZV3wJuioilks4G5heZ7o9l9CngwYi4vMj4l5PfBxnY397LeY8H2ofZEbxryLJqNK9eWvnKviYsw+PADElvg54rYp7Yz3N+CXw4mf795C4yBvACMKpCdZn1yUFgWTUfuFfSKnK3ZRyImZI6u3+AtwFzgLskrSO3W6i/G9V8GXi/pPXApeTuQPVCROwmt4tpfd7BYrNU+OqjZjUk6WjgYEQckHQWubtTnVbjsixjvI/RrLZOABZLagBeAT5e43osg7xFYGaWcT5GYGaWcQ4CM7OMcxCYmWWcg8DMLOMcBGZmGff/AS/F9MXz64K7AAAAAElFTkSuQmCC\n",
 66 |       "text/plain": [
 67 |        "<Figure size 432x288 with 1 Axes>"
 68 |       ]
 69 |      },
 70 |      "metadata": {
 71 |       "needs_background": "light"
 72 |      },
 73 |      "output_type": "display_data"
 74 |     }
 75 |    ],
 76 |    "source": [
 77 |     "plt.scatter(X[:n],Y[:n],label='famale')\n",
 78 |     "plt.scatter(X[n:],Y[n:],label='man')\n",
 79 |     "plt.xlabel(\"Hair Length\")\n",
 80 |     "plt.ylabel('Gender')\n",
 81 |     "plt.legend()"
 82 |    ]
 83 |   },
 84 |   {
 85 |    "cell_type": "code",
 86 |    "execution_count": 22,
 87 |    "id": "916ee0e7",
 88 |    "metadata": {},
 89 |    "outputs": [
 90 |     {
 91 |      "data": {
 92 |       "image/png": 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\n",
 93 |       "text/plain": [
 94 |        "<Figure size 432x288 with 1 Axes>"
 95 |       ]
 96 |      },
 97 |      "metadata": {
 98 |       "needs_background": "light"
 99 |      },
100 |      "output_type": "display_data"
101 |     }
102 |    ],
103 |    "source": [
104 |     "#plt random slope\n",
105 |     "plt.scatter(X[:n],Y[:n],label='famale')\n",
106 |     "plt.scatter(X[n:],Y[n:],label='man')\n",
107 |     "plt.xlabel(\"Hair Length\")\n",
108 |     "plt.ylabel('Gender')\n",
109 |     "plt.legend()\n",
110 |     "\n",
111 |     "for m in range(3):\n",
112 |     "    y_pred =np.matmul(X,[m])\n",
113 |     "    plt.plot(X,y_pred,c='red')"
114 |    ]
115 |   },
116 |   {
117 |    "cell_type": "code",
118 |    "execution_count": 26,
119 |    "id": "5985a6a1",
120 |    "metadata": {},
121 |    "outputs": [
122 |     {
123 |      "data": {
124 |       "text/plain": [
125 |        "<matplotlib.legend.Legend at 0xd2e3388>"
126 |       ]
127 |      },
128 |      "execution_count": 26,
129 |      "metadata": {},
130 |      "output_type": "execute_result"
131 |     },
132 |     {
133 |      "data": {
134 |       "image/png": 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\n",
135 |       "text/plain": [
136 |        "<Figure size 432x288 with 1 Axes>"
137 |       ]
138 |      },
139 |      "metadata": {
140 |       "needs_background": "light"
141 |      },
142 |      "output_type": "display_data"
143 |     }
144 |    ],
145 |    "source": [
146 |     "# Adaline \n",
147 |     "\n",
148 |     "m=np.matmul(inv(np.matmul(X.T,X)),np.matmul(X.T,Y))\n",
149 |     "y_pred=np.matmul(X,m)\n",
150 |     "\n",
151 |     "plt.scatter(X[:n],Y[:n],label='famale')\n",
152 |     "plt.scatter(X[n:],Y[n:],label='man')\n",
153 |     "plt.plot(X,y_pred,c='red',lw=3)\n",
154 |     "plt.xlabel('Hair Length')\n",
155 |     "plt.ylabel('Gender')\n",
156 |     "plt.legend()"
157 |    ]
158 |   },
159 |   {
160 |    "cell_type": "code",
161 |    "execution_count": null,
162 |    "id": "cb608d7d",
163 |    "metadata": {},
164 |    "outputs": [],
165 |    "source": []
166 |   }
167 |  ],
168 |  "metadata": {
169 |   "kernelspec": {
170 |    "display_name": "Python 3 (ipykernel)",
171 |    "language": "python",
172 |    "name": "python3"
173 |   },
174 |   "language_info": {
175 |    "codemirror_mode": {
176 |     "name": "ipython",
177 |     "version": 3
178 |    },
179 |    "file_extension": ".py",
180 |    "mimetype": "text/x-python",
181 |    "name": "python",
182 |    "nbconvert_exporter": "python",
183 |    "pygments_lexer": "ipython3",
184 |    "version": "3.7.10"
185 |   }
186 |  },
187 |  "nbformat": 4,
188 |  "nbformat_minor": 5
189 | }
190 | 


--------------------------------------------------------------------------------
/.ipynb_checkpoints/iris_classifier-checkpoint.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 98,
  6 |    "id": "121a4f69",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import numpy as np\n",
 11 |     "from sklearn.datasets import load_iris\n",
 12 |     "from sklearn.model_selection import train_test_split\n",
 13 |     "from numpy.linalg import inv"
 14 |    ]
 15 |   },
 16 |   {
 17 |    "cell_type": "code",
 18 |    "execution_count": 99,
 19 |    "id": "8a32a739",
 20 |    "metadata": {},
 21 |    "outputs": [
 22 |     {
 23 |      "data": {
 24 |       "text/plain": [
 25 |        "{'data': array([[5.1, 3.5, 1.4, 0.2],\n",
 26 |        "        [4.9, 3. , 1.4, 0.2],\n",
 27 |        "        [4.7, 3.2, 1.3, 0.2],\n",
 28 |        "        [4.6, 3.1, 1.5, 0.2],\n",
 29 |        "        [5. , 3.6, 1.4, 0.2],\n",
 30 |        "        [5.4, 3.9, 1.7, 0.4],\n",
 31 |        "        [4.6, 3.4, 1.4, 0.3],\n",
 32 |        "        [5. , 3.4, 1.5, 0.2],\n",
 33 |        "        [4.4, 2.9, 1.4, 0.2],\n",
 34 |        "        [4.9, 3.1, 1.5, 0.1],\n",
 35 |        "        [5.4, 3.7, 1.5, 0.2],\n",
 36 |        "        [4.8, 3.4, 1.6, 0.2],\n",
 37 |        "        [4.8, 3. , 1.4, 0.1],\n",
 38 |        "        [4.3, 3. , 1.1, 0.1],\n",
 39 |        "        [5.8, 4. , 1.2, 0.2],\n",
 40 |        "        [5.7, 4.4, 1.5, 0.4],\n",
 41 |        "        [5.4, 3.9, 1.3, 0.4],\n",
 42 |        "        [5.1, 3.5, 1.4, 0.3],\n",
 43 |        "        [5.7, 3.8, 1.7, 0.3],\n",
 44 |        "        [5.1, 3.8, 1.5, 0.3],\n",
 45 |        "        [5.4, 3.4, 1.7, 0.2],\n",
 46 |        "        [5.1, 3.7, 1.5, 0.4],\n",
 47 |        "        [4.6, 3.6, 1. , 0.2],\n",
 48 |        "        [5.1, 3.3, 1.7, 0.5],\n",
 49 |        "        [4.8, 3.4, 1.9, 0.2],\n",
 50 |        "        [5. , 3. , 1.6, 0.2],\n",
 51 |        "        [5. , 3.4, 1.6, 0.4],\n",
 52 |        "        [5.2, 3.5, 1.5, 0.2],\n",
 53 |        "        [5.2, 3.4, 1.4, 0.2],\n",
 54 |        "        [4.7, 3.2, 1.6, 0.2],\n",
 55 |        "        [4.8, 3.1, 1.6, 0.2],\n",
 56 |        "        [5.4, 3.4, 1.5, 0.4],\n",
 57 |        "        [5.2, 4.1, 1.5, 0.1],\n",
 58 |        "        [5.5, 4.2, 1.4, 0.2],\n",
 59 |        "        [4.9, 3.1, 1.5, 0.2],\n",
 60 |        "        [5. , 3.2, 1.2, 0.2],\n",
 61 |        "        [5.5, 3.5, 1.3, 0.2],\n",
 62 |        "        [4.9, 3.6, 1.4, 0.1],\n",
 63 |        "        [4.4, 3. , 1.3, 0.2],\n",
 64 |        "        [5.1, 3.4, 1.5, 0.2],\n",
 65 |        "        [5. , 3.5, 1.3, 0.3],\n",
 66 |        "        [4.5, 2.3, 1.3, 0.3],\n",
 67 |        "        [4.4, 3.2, 1.3, 0.2],\n",
 68 |        "        [5. , 3.5, 1.6, 0.6],\n",
 69 |        "        [5.1, 3.8, 1.9, 0.4],\n",
 70 |        "        [4.8, 3. , 1.4, 0.3],\n",
 71 |        "        [5.1, 3.8, 1.6, 0.2],\n",
 72 |        "        [4.6, 3.2, 1.4, 0.2],\n",
 73 |        "        [5.3, 3.7, 1.5, 0.2],\n",
 74 |        "        [5. , 3.3, 1.4, 0.2],\n",
 75 |        "        [7. , 3.2, 4.7, 1.4],\n",
 76 |        "        [6.4, 3.2, 4.5, 1.5],\n",
 77 |        "        [6.9, 3.1, 4.9, 1.5],\n",
 78 |        "        [5.5, 2.3, 4. , 1.3],\n",
 79 |        "        [6.5, 2.8, 4.6, 1.5],\n",
 80 |        "        [5.7, 2.8, 4.5, 1.3],\n",
 81 |        "        [6.3, 3.3, 4.7, 1.6],\n",
 82 |        "        [4.9, 2.4, 3.3, 1. ],\n",
 83 |        "        [6.6, 2.9, 4.6, 1.3],\n",
 84 |        "        [5.2, 2.7, 3.9, 1.4],\n",
 85 |        "        [5. , 2. , 3.5, 1. ],\n",
 86 |        "        [5.9, 3. , 4.2, 1.5],\n",
 87 |        "        [6. , 2.2, 4. , 1. ],\n",
 88 |        "        [6.1, 2.9, 4.7, 1.4],\n",
 89 |        "        [5.6, 2.9, 3.6, 1.3],\n",
 90 |        "        [6.7, 3.1, 4.4, 1.4],\n",
 91 |        "        [5.6, 3. , 4.5, 1.5],\n",
 92 |        "        [5.8, 2.7, 4.1, 1. ],\n",
 93 |        "        [6.2, 2.2, 4.5, 1.5],\n",
 94 |        "        [5.6, 2.5, 3.9, 1.1],\n",
 95 |        "        [5.9, 3.2, 4.8, 1.8],\n",
 96 |        "        [6.1, 2.8, 4. , 1.3],\n",
 97 |        "        [6.3, 2.5, 4.9, 1.5],\n",
 98 |        "        [6.1, 2.8, 4.7, 1.2],\n",
 99 |        "        [6.4, 2.9, 4.3, 1.3],\n",
100 |        "        [6.6, 3. , 4.4, 1.4],\n",
101 |        "        [6.8, 2.8, 4.8, 1.4],\n",
102 |        "        [6.7, 3. , 5. , 1.7],\n",
103 |        "        [6. , 2.9, 4.5, 1.5],\n",
104 |        "        [5.7, 2.6, 3.5, 1. ],\n",
105 |        "        [5.5, 2.4, 3.8, 1.1],\n",
106 |        "        [5.5, 2.4, 3.7, 1. ],\n",
107 |        "        [5.8, 2.7, 3.9, 1.2],\n",
108 |        "        [6. , 2.7, 5.1, 1.6],\n",
109 |        "        [5.4, 3. , 4.5, 1.5],\n",
110 |        "        [6. , 3.4, 4.5, 1.6],\n",
111 |        "        [6.7, 3.1, 4.7, 1.5],\n",
112 |        "        [6.3, 2.3, 4.4, 1.3],\n",
113 |        "        [5.6, 3. , 4.1, 1.3],\n",
114 |        "        [5.5, 2.5, 4. , 1.3],\n",
115 |        "        [5.5, 2.6, 4.4, 1.2],\n",
116 |        "        [6.1, 3. , 4.6, 1.4],\n",
117 |        "        [5.8, 2.6, 4. , 1.2],\n",
118 |        "        [5. , 2.3, 3.3, 1. ],\n",
119 |        "        [5.6, 2.7, 4.2, 1.3],\n",
120 |        "        [5.7, 3. , 4.2, 1.2],\n",
121 |        "        [5.7, 2.9, 4.2, 1.3],\n",
122 |        "        [6.2, 2.9, 4.3, 1.3],\n",
123 |        "        [5.1, 2.5, 3. , 1.1],\n",
124 |        "        [5.7, 2.8, 4.1, 1.3],\n",
125 |        "        [6.3, 3.3, 6. , 2.5],\n",
126 |        "        [5.8, 2.7, 5.1, 1.9],\n",
127 |        "        [7.1, 3. , 5.9, 2.1],\n",
128 |        "        [6.3, 2.9, 5.6, 1.8],\n",
129 |        "        [6.5, 3. , 5.8, 2.2],\n",
130 |        "        [7.6, 3. , 6.6, 2.1],\n",
131 |        "        [4.9, 2.5, 4.5, 1.7],\n",
132 |        "        [7.3, 2.9, 6.3, 1.8],\n",
133 |        "        [6.7, 2.5, 5.8, 1.8],\n",
134 |        "        [7.2, 3.6, 6.1, 2.5],\n",
135 |        "        [6.5, 3.2, 5.1, 2. ],\n",
136 |        "        [6.4, 2.7, 5.3, 1.9],\n",
137 |        "        [6.8, 3. , 5.5, 2.1],\n",
138 |        "        [5.7, 2.5, 5. , 2. ],\n",
139 |        "        [5.8, 2.8, 5.1, 2.4],\n",
140 |        "        [6.4, 3.2, 5.3, 2.3],\n",
141 |        "        [6.5, 3. , 5.5, 1.8],\n",
142 |        "        [7.7, 3.8, 6.7, 2.2],\n",
143 |        "        [7.7, 2.6, 6.9, 2.3],\n",
144 |        "        [6. , 2.2, 5. , 1.5],\n",
145 |        "        [6.9, 3.2, 5.7, 2.3],\n",
146 |        "        [5.6, 2.8, 4.9, 2. ],\n",
147 |        "        [7.7, 2.8, 6.7, 2. ],\n",
148 |        "        [6.3, 2.7, 4.9, 1.8],\n",
149 |        "        [6.7, 3.3, 5.7, 2.1],\n",
150 |        "        [7.2, 3.2, 6. , 1.8],\n",
151 |        "        [6.2, 2.8, 4.8, 1.8],\n",
152 |        "        [6.1, 3. , 4.9, 1.8],\n",
153 |        "        [6.4, 2.8, 5.6, 2.1],\n",
154 |        "        [7.2, 3. , 5.8, 1.6],\n",
155 |        "        [7.4, 2.8, 6.1, 1.9],\n",
156 |        "        [7.9, 3.8, 6.4, 2. ],\n",
157 |        "        [6.4, 2.8, 5.6, 2.2],\n",
158 |        "        [6.3, 2.8, 5.1, 1.5],\n",
159 |        "        [6.1, 2.6, 5.6, 1.4],\n",
160 |        "        [7.7, 3. , 6.1, 2.3],\n",
161 |        "        [6.3, 3.4, 5.6, 2.4],\n",
162 |        "        [6.4, 3.1, 5.5, 1.8],\n",
163 |        "        [6. , 3. , 4.8, 1.8],\n",
164 |        "        [6.9, 3.1, 5.4, 2.1],\n",
165 |        "        [6.7, 3.1, 5.6, 2.4],\n",
166 |        "        [6.9, 3.1, 5.1, 2.3],\n",
167 |        "        [5.8, 2.7, 5.1, 1.9],\n",
168 |        "        [6.8, 3.2, 5.9, 2.3],\n",
169 |        "        [6.7, 3.3, 5.7, 2.5],\n",
170 |        "        [6.7, 3. , 5.2, 2.3],\n",
171 |        "        [6.3, 2.5, 5. , 1.9],\n",
172 |        "        [6.5, 3. , 5.2, 2. ],\n",
173 |        "        [6.2, 3.4, 5.4, 2.3],\n",
174 |        "        [5.9, 3. , 5.1, 1.8]]),\n",
175 |        " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
176 |        "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
177 |        "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
178 |        "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
179 |        "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
180 |        "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
181 |        "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
182 |        " 'frame': None,\n",
183 |        " 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),\n",
184 |        " 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n                \\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...',\n",
185 |        " 'feature_names': ['sepal length (cm)',\n",
186 |        "  'sepal width (cm)',\n",
187 |        "  'petal length (cm)',\n",
188 |        "  'petal width (cm)'],\n",
189 |        " 'filename': '/home/deep/anaconda3/envs/py36/lib/python3.6/site-packages/sklearn/datasets/data/iris.csv'}"
190 |       ]
191 |      },
192 |      "execution_count": 99,
193 |      "metadata": {},
194 |      "output_type": "execute_result"
195 |     }
196 |    ],
197 |    "source": [
198 |     "data=load_iris()\n",
199 |     "data"
200 |    ]
201 |   },
202 |   {
203 |    "cell_type": "code",
204 |    "execution_count": 168,
205 |    "id": "0d943064",
206 |    "metadata": {},
207 |    "outputs": [],
208 |    "source": [
209 |     "X=data.data\n",
210 |     "Y=data.target\n",
211 |     "indx = 0\n",
212 |     "\n",
213 |     "X= X[np.where((Y == 1) | (Y == 2))]\n",
214 |     "Y = Y[np.where((Y == 1) | (Y == 2))]\n"
215 |    ]
216 |   },
217 |   {
218 |    "cell_type": "code",
219 |    "execution_count": 175,
220 |    "id": "9f9b662d",
221 |    "metadata": {},
222 |    "outputs": [],
223 |    "source": [
224 |     "class AdalineClassifier:\n",
225 |     "    def __init__(self):\n",
226 |     "        pass\n",
227 |     "    def fit(self,x_train,y_train):\n",
228 |     "        self.m=np.matmul(inv(np.matmul(x_train.T,x_train)),np.matmul(x_train.T,y_train))\n",
229 |     "    def predict(self,x_test):\n",
230 |     "        y_pred=np.matmul(x_test,self.m)\n",
231 |     "        y_pred=np.round(y_pred)\n",
232 |     "        return y_pred\n",
233 |     "    def evaluate(self,x_test,y_test):\n",
234 |     "        y_pred=np.matmul(x_test,self.m)\n",
235 |     "#         y_pred=np.round(y_pred\n",
236 |     "        y_pred[np.where(y_pred < 1)] = 1\n",
237 |     "        y_pred[np.where(y_pred >= 2)] = 2\n",
238 |     "        loss = np.mean(np.abs(np.subtract(y_test, y_pred)))\n",
239 |     "        return loss \n",
240 |     "#         if np.min(y_test)==0\n",
241 |     "        "
242 |    ]
243 |   },
244 |   {
245 |    "cell_type": "code",
246 |    "execution_count": 176,
247 |    "id": "d5b7e556",
248 |    "metadata": {},
249 |    "outputs": [
250 |     {
251 |      "name": "stdout",
252 |      "output_type": "stream",
253 |      "text": [
254 |       "m [-0.01087176 -0.34079226  0.288308    0.67914941]\n"
255 |      ]
256 |     }
257 |    ],
258 |    "source": [
259 |     "x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.5)\n",
260 |     "model=AdalineClassifier()\n",
261 |     "model.fit(x_train,y_train)\n",
262 |     "print('m',model.m)\n",
263 |     "y_pred=model.predict(x_test)\n",
264 |     "loss = model.evaluate(x_test, y_test)"
265 |    ]
266 |   },
267 |   {
268 |    "cell_type": "code",
269 |    "execution_count": 181,
270 |    "id": "5a16c6b1",
271 |    "metadata": {},
272 |    "outputs": [
273 |     {
274 |      "name": "stdout",
275 |      "output_type": "stream",
276 |      "text": [
277 |       "loss: 0.15998411915144156\n"
278 |      ]
279 |     }
280 |    ],
281 |    "source": [
282 |     "print('loss:',loss)"
283 |    ]
284 |   },
285 |   {
286 |    "cell_type": "code",
287 |    "execution_count": 178,
288 |    "id": "292aedaa",
289 |    "metadata": {},
290 |    "outputs": [],
291 |    "source": [
292 |     "class KNearestNeighbors:\n",
293 |     "    def __init__(self,k):\n",
294 |     "        self.k=k\n",
295 |     "    def fit(self,x_train,y_train):\n",
296 |     "        self.x_train=x_train\n",
297 |     "        self.y_train=y_train\n",
298 |     "        self.number_classes=len(np.unique(y_train))\n",
299 |     "    def NearNeighbors(self,x_test):\n",
300 |     "        distance=np.sum(np.subtract(x_test,self.x_train)**2, axis=1)\n",
301 |     "        near_neighbors=np.argsort(distance)[:self.k]\n",
302 |     "        return near_neighbors\n",
303 |     "    def predict(self,x_test):\n",
304 |     "        near_neighbors=self.NearNeighbors(x_test)\n",
305 |     "        y=np.argmax(np.bincount(self.y_train[near_neighbors]))\n",
306 |     "        return y\n",
307 |     "    def evaluate(self,x_test,y_test):\n",
308 |     "        self.x_test=x_test\n",
309 |     "        self.y_test=y_test\n",
310 |     "        y_predict=np.zeros(len(self.x_test))\n",
311 |     "        \n",
312 |     "        for i,test in enumerate(self.x_test):\n",
313 |     "            y_predict[i]=self.predict(test)\n",
314 |     "        evaluatation=(y_predict==self.y_test).sum()/len(self.y_test)\n",
315 |     "        return evaluatation"
316 |    ]
317 |   },
318 |   {
319 |    "cell_type": "code",
320 |    "execution_count": 182,
321 |    "id": "6b1514ca",
322 |    "metadata": {},
323 |    "outputs": [
324 |     {
325 |      "name": "stdout",
326 |      "output_type": "stream",
327 |      "text": [
328 |       "acc:  0.94\n"
329 |      ]
330 |     }
331 |    ],
332 |    "source": [
333 |     "knn=KNearestNeighbors(k=5)\n",
334 |     "knn.fit(x_train,y_train)\n",
335 |     "\n",
336 |     "y_pred=knn.predict(x_test)\n",
337 |     "acc=knn.evaluate(x_test,y_test)\n",
338 |     "print('acc: ',acc)"
339 |    ]
340 |   },
341 |   {
342 |    "cell_type": "code",
343 |    "execution_count": null,
344 |    "id": "f4d9e968",
345 |    "metadata": {},
346 |    "outputs": [],
347 |    "source": []
348 |   }
349 |  ],
350 |  "metadata": {
351 |   "kernelspec": {
352 |    "display_name": "Python 3 (ipykernel)",
353 |    "language": "python",
354 |    "name": "python3"
355 |   },
356 |   "language_info": {
357 |    "codemirror_mode": {
358 |     "name": "ipython",
359 |     "version": 3
360 |    },
361 |    "file_extension": ".py",
362 |    "mimetype": "text/x-python",
363 |    "name": "python",
364 |    "nbconvert_exporter": "python",
365 |    "pygments_lexer": "ipython3",
366 |    "version": "3.6.12"
367 |   }
368 |  },
369 |  "nbformat": 4,
370 |  "nbformat_minor": 5
371 | }
372 | 


--------------------------------------------------------------------------------
/Adalin_regression.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "372cf13d",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import numpy as np\n",
 11 |     "import matplotlib.pyplot as plt\n",
 12 |     "from numpy.linalg import inv"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 2,
 18 |    "id": "b3488187",
 19 |    "metadata": {},
 20 |    "outputs": [],
 21 |    "source": [
 22 |     "def generate_data(n):\n",
 23 |     "    height=np.random.randint(50,220,n)\n",
 24 |     "    slope=np.random.uniform(0.2,0.4,n)\n",
 25 |     "    b=np.random.uniform(4,1,n)\n",
 26 |     "    weight=height*slope +b\n",
 27 |     "    return height,weight"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "code",
 32 |    "execution_count": 3,
 33 |    "id": "11b92d51",
 34 |    "metadata": {},
 35 |    "outputs": [
 36 |     {
 37 |      "name": "stdout",
 38 |      "output_type": "stream",
 39 |      "text": [
 40 |       "[109  75 112 210  62 193  98 134 209 164 102  53  93 146 151 122  89 156\n",
 41 |       " 180 131 125 186 176 188 164 165  88  91 208  58  79 116 160  78 156  96\n",
 42 |       "  90  90 218  84  67 115 112 139 125 169 175 116 173 147 134  68 104  89\n",
 43 |       " 181  90 216 176  53 195 140  55 211 180 202 187 213 105  89 189 172 176\n",
 44 |       "  75 158  94 164 141  93 120  71 143 124  65  59  80 142  91  76 182 111\n",
 45 |       " 174 150  53  68  75  87 213  90  68 126] [36.3977915  31.54686663 40.51379964 45.37390566 15.47114049 59.43099519\n",
 46 |       " 41.56068986 51.13252228 64.67333642 47.49706703 38.99071841 16.28730129\n",
 47 |       " 31.6721524  38.66862487 41.61483221 39.97304113 25.24729156 59.80797077\n",
 48 |       " 42.0554569  36.96016043 31.30334308 46.8253739  45.50485639 54.18757285\n",
 49 |       " 47.51342866 37.02129223 25.34485204 34.71408016 82.02859921 16.24574291\n",
 50 |       " 19.60901712 29.63715189 60.20475079 26.42834559 62.76904348 23.7724737\n",
 51 |       " 27.05557581 32.3053621  61.19868254 31.67924341 18.74377753 28.21144289\n",
 52 |       " 37.02295539 41.8019504  36.12166755 53.28901213 68.70599677 31.32528922\n",
 53 |       " 47.18372852 51.42296949 35.34912087 20.25637485 33.1682802  22.9732632\n",
 54 |       " 49.4066249  20.30240921 79.34776585 51.43355884 12.3818858  76.87887857\n",
 55 |       " 54.98045532 20.79937134 44.91789657 48.36297634 79.38434838 73.96484785\n",
 56 |       " 68.47675089 25.75556149 36.41515799 53.97169744 57.28708513 48.18365596\n",
 57 |       " 17.71297386 38.11052636 34.73144675 48.41576002 56.15605186 25.84484126\n",
 58 |       " 47.00836033 22.96800062 54.96928881 39.43958413 17.18177709 15.07005758\n",
 59 |       " 25.77528741 47.10466407 29.02265372 17.48129141 65.62663679 36.27506304\n",
 60 |       " 54.14906716 61.65806469 19.34408494 25.90738236 33.80775739 22.66501397\n",
 61 |       " 65.31191352 25.95990476 26.21953401 31.05527433]\n"
 62 |      ]
 63 |     }
 64 |    ],
 65 |    "source": [
 66 |     "height,weight=generate_data(n=100)\n",
 67 |     "print(height,weight)"
 68 |    ]
 69 |   },
 70 |   {
 71 |    "cell_type": "code",
 72 |    "execution_count": 4,
 73 |    "id": "6f55a0b0",
 74 |    "metadata": {},
 75 |    "outputs": [
 76 |     {
 77 |      "name": "stdout",
 78 |      "output_type": "stream",
 79 |      "text": [
 80 |       "(100, 1)\n",
 81 |       "(100, 1)\n"
 82 |      ]
 83 |     }
 84 |    ],
 85 |    "source": [
 86 |     "X=height.reshape(-1,1)\n",
 87 |     "Y=weight.reshape(-1,1)\n",
 88 |     "print(X.shape)\n",
 89 |     "print(Y.shape)\n"
 90 |    ]
 91 |   },
 92 |   {
 93 |    "cell_type": "code",
 94 |    "execution_count": 5,
 95 |    "id": "c2ffba6b",
 96 |    "metadata": {},
 97 |    "outputs": [
 98 |     {
 99 |      "data": {
100 |       "text/plain": [
101 |        "Text(0, 0.5, 'Weight')"
102 |       ]
103 |      },
104 |      "execution_count": 5,
105 |      "metadata": {},
106 |      "output_type": "execute_result"
107 |     },
108 |     {
109 |      "data": {
110 |       "image/png": 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\n",
111 |       "text/plain": [
112 |        "<Figure size 432x288 with 1 Axes>"
113 |       ]
114 |      },
115 |      "metadata": {
116 |       "needs_background": "light"
117 |      },
118 |      "output_type": "display_data"
119 |     }
120 |    ],
121 |    "source": [
122 |     "# plt random slope\n",
123 |     "\n",
124 |     "for m in range(1,4):\n",
125 |     "    Y_pred=np.matmul(X,[m])\n",
126 |     "    plt.plot(X,Y_pred,c='red')\n",
127 |     "    \n",
128 |     "\n",
129 |     "plt.scatter(X,Y)\n",
130 |     "plt.xlabel('Height')\n",
131 |     "plt.ylabel('Weight')"
132 |    ]
133 |   },
134 |   {
135 |    "cell_type": "code",
136 |    "execution_count": 6,
137 |    "id": "7f0e94ec",
138 |    "metadata": {},
139 |    "outputs": [
140 |     {
141 |      "data": {
142 |       "text/plain": [
143 |        "[<matplotlib.lines.Line2D at 0xd5f3608>]"
144 |       ]
145 |      },
146 |      "execution_count": 6,
147 |      "metadata": {},
148 |      "output_type": "execute_result"
149 |     },
150 |     {
151 |      "data": {
152 |       "image/png": 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\n",
153 |       "text/plain": [
154 |        "<Figure size 432x288 with 1 Axes>"
155 |       ]
156 |      },
157 |      "metadata": {
158 |       "needs_background": "light"
159 |      },
160 |      "output_type": "display_data"
161 |     }
162 |    ],
163 |    "source": [
164 |     "#plt slope Adaline\n",
165 |     "m=np.matmul(inv(np.matmul(X.T,X)),np.matmul(X.T,Y))\n",
166 |     "y_pred=np.matmul(X,m)\n",
167 |     "plt.scatter(X,Y)\n",
168 |     "plt.plot(X,y_pred,c='red')\n"
169 |    ]
170 |   },
171 |   {
172 |    "cell_type": "code",
173 |    "execution_count": 10,
174 |    "id": "d3d63e6f",
175 |    "metadata": {},
176 |    "outputs": [
177 |     {
178 |      "name": "stdout",
179 |      "output_type": "stream",
180 |      "text": [
181 |       "0.30758292198692483\n"
182 |      ]
183 |     },
184 |     {
185 |      "data": {
186 |       "text/plain": [
187 |        "Text(0, 0.5, 'Weight')"
188 |       ]
189 |      },
190 |      "execution_count": 10,
191 |      "metadata": {},
192 |      "output_type": "execute_result"
193 |     },
194 |     {
195 |      "data": {
196 |       "image/png": 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\n",
197 |       "text/plain": [
198 |        "<Figure size 432x288 with 1 Axes>"
199 |       ]
200 |      },
201 |      "metadata": {
202 |       "needs_background": "light"
203 |      },
204 |      "output_type": "display_data"
205 |     }
206 |    ],
207 |    "source": [
208 |     "#linregress\n",
209 |     "from scipy.stats import linregress\n",
210 |     "result=linregress(X[:,0],Y[:,0])\n",
211 |     "print(result.slope)\n",
212 |     "y_pred=result.intercept + np.matmul(X,np.array([[result.slope]]))\n",
213 |     "plt.scatter(X,Y)\n",
214 |     "plt.plot(X,y_pred,c='red')\n",
215 |     "plt.xlabel('Height')\n",
216 |     "plt.ylabel('Weight')"
217 |    ]
218 |   },
219 |   {
220 |    "cell_type": "code",
221 |    "execution_count": null,
222 |    "id": "7d9e2b78",
223 |    "metadata": {},
224 |    "outputs": [],
225 |    "source": []
226 |   }
227 |  ],
228 |  "metadata": {
229 |   "kernelspec": {
230 |    "display_name": "Python 3 (ipykernel)",
231 |    "language": "python",
232 |    "name": "python3"
233 |   },
234 |   "language_info": {
235 |    "codemirror_mode": {
236 |     "name": "ipython",
237 |     "version": 3
238 |    },
239 |    "file_extension": ".py",
240 |    "mimetype": "text/x-python",
241 |    "name": "python",
242 |    "nbconvert_exporter": "python",
243 |    "pygments_lexer": "ipython3",
244 |    "version": "3.7.10"
245 |   }
246 |  },
247 |  "nbformat": 4,
248 |  "nbformat_minor": 5
249 | }
250 | 


--------------------------------------------------------------------------------
/Persepron/linear_data_test.csv:
--------------------------------------------------------------------------------
  1 | X1, X2, Y
  2 | 0.147562141,0.243518271,-1
  3 | 0.17986899,0.092253703,-1
  4 | 0.754244046,0.523874856,1
  5 | 0.248663781,0.175587276,-1
  6 | 0.39721749,0.034213495,-1
  7 | 0.450981608,0.032885898,-1
  8 | 0.335532917,0.16654443,-1
  9 | 0.37137205,0.167201755,-1
 10 | 0.280985655,0.214982886,-1
 11 | 0.313304894,0.005219768,-1
 12 | 0.638465839,0.590446627,1
 13 | 0.431591143,0.047083073,-1
 14 | 0.207774186,0.08197187,-1
 15 | 0.741609489,0.484712762,1
 16 | 0.953514363,0.63962583,1
 17 | 0.642944532,0.561453315,1
 18 | 0.226494744,0.054182458,-1
 19 | 0.303693007,0.023117489,-1
 20 | 0.442909335,0.264587381,-1
 21 | 0.689641361,0.441606458,1
 22 | 0.808516873,0.460988844,1
 23 | 0.265847527,-0.02176123,-1
 24 | 0.277512671,-0.03519462,-1
 25 | 0.650996505,0.281211164,1
 26 | 0.604564151,0.581607218,1
 27 | 0.361767991,0.088941627,-1
 28 | 0.666911464,0.65838218,1
 29 | 0.230633128,-0.018832998,-1
 30 | 0.137952214,0.113629399,-1
 31 | 0.736452219,0.491761591,1
 32 | 0.597108275,0.550563582,1
 33 | 0.602609505,0.427255883,1
 34 | 0.259748077,0.083086457,-1
 35 | 0.6593677,0.398195252,1
 36 | 0.684338148,0.243306417,1
 37 | 0.855013781,0.552123366,1
 38 | 0.960752242,0.589326479,1
 39 | 0.218532689,0.149970477,-1
 40 | 0.340460188,0.163897052,-1
 41 | 0.355834203,0.336747248,-1
 42 | 0.692211516,0.577457902,1
 43 | 0.66215496,0.570323145,1
 44 | 0.372511986,0.08571986,-1
 45 | 0.229005358,0.026734503,-1
 46 | 0.309531718,0.144331016,-1
 47 | 0.263227254,0.127925335,-1
 48 | 0.308466277,0.091035136,-1
 49 | 0.09307746,0.113987149,-1
 50 | 0.622936923,0.456717037,1
 51 | 0.706148123,0.490803326,1
 52 | 0.758732482,0.456626656,1
 53 | 0.694577645,0.622832561,1
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--------------------------------------------------------------------------------
/Persepron/linear_data_train.csv:
--------------------------------------------------------------------------------
   1 | X1, X2, Y
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1000 | 0.50038409,0.579511796,1
1001 | 0.790973981,0.563663507,1
1002 | 


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/Persepron/perceptron-classification.py:
--------------------------------------------------------------------------------
 1 | import matplotlib.pyplot as plt
 2 | import numpy as np
 3 | import pandas as pd
 4 | 
 5 | 
 6 | train_data=np.array(pd.read_csv('linear_data_train.csv'))
 7 | test_data=np.array(pd.read_csv('linear_data_test.csv'))
 8 | 
 9 | X_train =train_data[:, 0:2]
10 | Y_train = train_data[:, 2]
11 | X_test =test_data[:,0:2]
12 | Y_test= test_data[:,2]
13 | 
14 | 
15 | N=X_train.shape[0]
16 | lr=0.01
17 | epochs=3
18 | 
19 | m = np.random.rand(2,1)
20 | b = np.random.rand(1,1)
21 | 
22 | fig=plt.figure()
23 | ax=fig.add_subplot(projection='3d')
24 | 
25 | x_0_range=np.arange(X_train[:,0].min(),X_train[:,0].max(),0.1)
26 | x_1_range=np.arange(X_train[:,1].min(),X_train[:,1].max(),0.1)
27 | 
28 | errors=[]
29 | 
30 | for i in range(epochs):
31 |     for n in range(N):
32 | 
33 |         y_pred = np.matmul(X_train[n:n+1],m)+b
34 |         e= np.subtract(Y_train[n], y_pred)
35 | 
36 |         #update
37 |         m = m+ lr*X_train[n:n+1,:].T*e
38 |         b +=lr*e
39 | 
40 |         # Error
41 |         y_pred=np.matmul(X_train,m)+b
42 |         error=np.mean(Y_train - y_pred)
43 |         errors.append(error)
44 |         print('error',error)
45 | 
46 |         #plot data
47 |         ax.clear()
48 |         x_0, x_1 = np.meshgrid(x_0_range, x_1_range)
49 |         z = x_0 * m[0] + x_1 * m[1] + b
50 |         ax.plot_surface(x_0, x_1, z, rstride=1, cstride=1, alpha = 0.4)
51 |         ax.scatter(X_train[Y_train == 1,0], X_train[Y_train == 1,1], Y_train[Y_train == 1], c='r', marker='o')
52 |         ax.scatter(X_train[Y_train == -1,0], X_train[Y_train == -1,1], Y_train[Y_train == -1], c='g', marker='o')
53 |         ax.set_xlabel('X0')
54 |         ax.set_ylabel('X1')
55 |         ax.set_zlabel('Y')
56 |         plt.pause(0.001)
57 | 
58 | 


--------------------------------------------------------------------------------
/Persepron/perceptron-classification_class.py:
--------------------------------------------------------------------------------
 1 | import matplotlib.pyplot as plt
 2 | import numpy as np
 3 | import pandas as pd
 4 | 
 5 | 
 6 | train_data = np.array(pd.read_csv('linear_data_train.csv'))
 7 | test_data = np.array(pd.read_csv('linear_data_test.csv'))
 8 | 
 9 | X_train = train_data[0:250, 0:2]
10 | Y_train = train_data[:250, 2]
11 | X_test = test_data[:, 0:2]
12 | Y_test = test_data[:, 2]
13 | 
14 | # fig=plt.figure()
15 | # ax=fig.add_subplot(projection='3d')
16 | # ax.view_init(20,25)
17 | 
18 | 
19 | 
20 | def fit(X_train,Y_train):
21 |     lr = 0.01
22 |     epochs = 8
23 |     N = X_train.shape[0]
24 | 
25 |     m = np.random.rand(2, 1)
26 |     b = np.random.rand(1, 1)
27 | 
28 |     x_0_range=np.arange(X_train[:,0].min(),X_train[:,0].max(),0.1)
29 |     x_1_range=np.arange(X_train[:,1].min(),X_train[:,1].max(),0.1)
30 | 
31 |     Error = []
32 |     for i in range(epochs):
33 |         errors = []
34 | 
35 |         for n in range(N):
36 | 
37 |             y_pred = np.matmul(X_train[n:n+1],m)+b
38 |             e= np.subtract(Y_train[n], y_pred)
39 | 
40 |             Y_pred = np.matmul(X_train, m) + b
41 |             error = np.mean(np.abs(Y_train - Y_pred))
42 |             errors.append(np.abs(e[0,0]))
43 | 
44 |             #update
45 |             m = m + lr*X_train[n:n+1,:].T* e
46 |             b = b + lr * e
47 | 
48 |             # plot data
49 |             ax = plt.subplot(1, 2, 1, projection='3d')
50 | 
51 |             # ax.clear()
52 |             x_0, x_1 = np.meshgrid(x_0_range, x_1_range)
53 |             z = x_0 * m[0] + x_1 * m[1] + b
54 |             ax.plot_surface(x_0, x_1, z, rstride=1, cstride=1, alpha=0.4)
55 |             ax.scatter(X_train[Y_train == 1, 0], X_train[Y_train == 1, 1], Y_train[Y_train == 1], c='r', marker='o')
56 |             ax.scatter(X_train[Y_train == -1, 0], X_train[Y_train == -1, 1], Y_train[Y_train == -1], c='g', marker='o')
57 |             ax.set_xlabel('X0')
58 |             ax.set_ylabel('X1')
59 |             ax.set_zlabel('Y')
60 | 
61 |         # Plot Error
62 |         Error.append(np.mean(errors))
63 |         ax2 = plt.subplot(1, 2, 2)
64 |         ax2.set_title('Loss')
65 |         x = np.arange(0, len(Error))
66 |         ax2.plot(x, Error, marker='o')
67 |         plt.pause(0.01)
68 |         # ax2.show()
69 |     return m,b
70 | 
71 | def predict(X_test):
72 |     y_pred=np.matmul(X_test,m)+b
73 |     return y_pred
74 | 
75 | def evaluate(X,Y):
76 |     y_pred = np.matmul(X, m) + b
77 |     y_predic = np.zeros(len(y_pred))
78 |     for i ,test in enumerate(X):
79 |         y_predic[i]=predict(test)
80 |     y_predic[np.where(y_predic<0)] =  -1
81 |     y_predic[np.np.where(y_predic>0)] = 1
82 |     accuracy = (y_predic == Y).sum() / len(Y)
83 |     loss = np.mean(np.abs(np.subtract(Y, y_pred)))
84 |     return loss, accuracy
85 | 
86 | m,b = fit(X_train,Y_train)
87 | 
88 | y_pred=predict(X_test)
89 | loss, accuracy = evaluate(X_test, Y_test)
90 | loss_train, accuracy_train = evaluate(X_train, Y_train)
91 | print('error',loss, 'accuracy',accuracy)


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/WeatherHistory/W.npy:
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https://raw.githubusercontent.com/zahra-zarrabi/MachineLearning/2c69a5c445d6a11381707d2f87e2f36e47aaa1e4/WeatherHistory/W.npy


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/WeatherHistory/b.npy:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/zahra-zarrabi/MachineLearning/2c69a5c445d6a11381707d2f87e2f36e47aaa1e4/WeatherHistory/b.npy


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/classification.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 24,
  6 |    "id": "4d3e23f7",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import numpy as np\n",
 11 |     "import matplotlib.pyplot as plt\n",
 12 |     "from numpy.linalg import inv"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 9,
 18 |    "id": "9706111e",
 19 |    "metadata": {},
 20 |    "outputs": [],
 21 |    "source": [
 22 |     "def generate_data(n):\n",
 23 |     "    hair_length_female = np.random.normal(40,7,n)\n",
 24 |     "    hair_length_man=np.random.normal(10,3,n)\n",
 25 |     "    \n",
 26 |     "    label_female=np.zeros(n,dtype='int')\n",
 27 |     "    label_man=np.ones(n,dtype='int')\n",
 28 |     "    \n",
 29 |     "    hair_length=np.concatenate((hair_length_female,hair_length_man))\n",
 30 |     "    gender=np.concatenate((label_female,label_man))\n",
 31 |     "    return hair_length,gender"
 32 |    ]
 33 |   },
 34 |   {
 35 |    "cell_type": "code",
 36 |    "execution_count": 10,
 37 |    "id": "91c456e2",
 38 |    "metadata": {},
 39 |    "outputs": [],
 40 |    "source": [
 41 |     "n=100\n",
 42 |     "X,Y=generate_data(n)\n",
 43 |     "X=X.reshape(-1,1)\n",
 44 |     "Y=Y.reshape(-1,1)\n"
 45 |    ]
 46 |   },
 47 |   {
 48 |    "cell_type": "code",
 49 |    "execution_count": 13,
 50 |    "id": "2e9aa9c6",
 51 |    "metadata": {},
 52 |    "outputs": [
 53 |     {
 54 |      "data": {
 55 |       "text/plain": [
 56 |        "<matplotlib.legend.Legend at 0xd117a08>"
 57 |       ]
 58 |      },
 59 |      "execution_count": 13,
 60 |      "metadata": {},
 61 |      "output_type": "execute_result"
 62 |     },
 63 |     {
 64 |      "data": {
 65 |       "image/png": 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\n",
 66 |       "text/plain": [
 67 |        "<Figure size 432x288 with 1 Axes>"
 68 |       ]
 69 |      },
 70 |      "metadata": {
 71 |       "needs_background": "light"
 72 |      },
 73 |      "output_type": "display_data"
 74 |     }
 75 |    ],
 76 |    "source": [
 77 |     "plt.scatter(X[:n],Y[:n],label='famale')\n",
 78 |     "plt.scatter(X[n:],Y[n:],label='man')\n",
 79 |     "plt.xlabel(\"Hair Length\")\n",
 80 |     "plt.ylabel('Gender')\n",
 81 |     "plt.legend()"
 82 |    ]
 83 |   },
 84 |   {
 85 |    "cell_type": "code",
 86 |    "execution_count": 22,
 87 |    "id": "916ee0e7",
 88 |    "metadata": {},
 89 |    "outputs": [
 90 |     {
 91 |      "data": {
 92 |       "image/png": 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\n",
 93 |       "text/plain": [
 94 |        "<Figure size 432x288 with 1 Axes>"
 95 |       ]
 96 |      },
 97 |      "metadata": {
 98 |       "needs_background": "light"
 99 |      },
100 |      "output_type": "display_data"
101 |     }
102 |    ],
103 |    "source": [
104 |     "#plt random slope\n",
105 |     "plt.scatter(X[:n],Y[:n],label='famale')\n",
106 |     "plt.scatter(X[n:],Y[n:],label='man')\n",
107 |     "plt.xlabel(\"Hair Length\")\n",
108 |     "plt.ylabel('Gender')\n",
109 |     "plt.legend()\n",
110 |     "\n",
111 |     "for m in range(3):\n",
112 |     "    y_pred =np.matmul(X,[m])\n",
113 |     "    plt.plot(X,y_pred,c='red')"
114 |    ]
115 |   },
116 |   {
117 |    "cell_type": "code",
118 |    "execution_count": 26,
119 |    "id": "5985a6a1",
120 |    "metadata": {},
121 |    "outputs": [
122 |     {
123 |      "data": {
124 |       "text/plain": [
125 |        "<matplotlib.legend.Legend at 0xd2e3388>"
126 |       ]
127 |      },
128 |      "execution_count": 26,
129 |      "metadata": {},
130 |      "output_type": "execute_result"
131 |     },
132 |     {
133 |      "data": {
134 |       "image/png": 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ZPP9DwEMAlZWV+kaziEgOZfqFsmysBfokLfdOrKvTFfgC8DczWw2MBGbohrGISOuKMhHMBSrMrNzMOgIXkvSdBHff7O7d3b2vu/cFZgFnuntVhDGJiEgDkSUCd98FXA3MBJYD09x9qZlNMjPNbCYi0kZkOkNZVtz9OeC5ButuS7HtmChjERGRxkXZNCQiIgVAiUBEJOaUCEREYk6JQEQk5pQIRERiTolARCTmlAhERNqyN9+Ea6+FK6+EpUsjeYpIv0cgIiLN8Nln8NhjMHkyrFix/+NPPw3vvAMH5HZeMCUCEZF8cIdXXw2V/u9/n9k+O3ZA5845D0WJQESkNXzwAfziF6Hi//TT7MqYOxeKi3MbF0oEIiK5V1sLzzwTKv3XXsuujL594brr4DvfgW7dchpeQ0oEIiIttXhxqPQffjj7Mi67DK65BgYNyl1cGVIiEBFpjs2b4ZFHQsX/7rvZlXHiieFq/5xzoKQkt/FlQYlARCSVPXvghRdCpf+Xv2RXRllZqPQvvxw+//mchpcrSgQiInVWr4b77gsV/65d2ZXxjW+Ein/0aDDLaXhRUSIQkXjavh0efzxU+gsXZlfGgAGh0v/Wt6BLl9zG14qUCEQkHmbPDpX+449nX8a118LVV0NFRe7iagOUCESk/Vm/Hh56KFT869dnV8bJJ4er/dNPh6L2PRqPEoGIFLbdu2HGjFDp//3v2ZXRs2e42v/e9+CQQ3IbXwFQIhCRwrJiBdx7LzzwQPZlfPvboeIfNix3cRUwJQIRabu2boXf/jZc7b/5ZnZlVFaGSv+CC6BTp9zG104oEYhI2+AemnYmT4bp07Mro0uXfUM29+mT0/DaMyUCEcmPtWtD887kyWH45WyceWa4oXvSSQXTZ78tUiIQkejt3BnG0p88GebMya6Mo48OV/vjx0PXrjkNL+6UCEQk9xYsCJX+lCnZl3H55WEQtgEDchWVpKBEICIt88knYdTNyZOhujq7Mr785XC1f9ZZ0EHVUmvTKy4imduzB55/PlT6f/1rdmUcckho1//+9+HQQ3Mbn2RFiUBEUtu4MbTpz54dfrIZgfPCC8PV/siRuqHbRikRiEhQWxsmWJk1K1T6s2bBG280r4xBg8LV/sUXQ2lpNHFKzikRiMSRe2jPr6v0Z8+GqqowImemiotDpX/VVXDUUdHFKpGLNBGY2ThgMlAM/Nrd72jw+A+B7wG7gPXA/3D3LKf8EZGUtm6FefPqX+1/8EH6/Tp0gCFDYMSI0LQzfHjoxtnOB2GLm8gSgZkVA/cDpwDVwFwzm+Huy5I2mw9Uuvs2M7sC+C/gm1HFJBILe/aE8XiSr/YXLw7r0znyyH2V/ogRMHSomnhiIMpPBMOBVe7+NoCZPQGcBexNBO7+StL2s4BvRRiPSPu0bt2+Cn/27HBz99NP0+934IFwwgn7Kv0RI9rsVIoSrSgTQS9gTdJyNTCiie2/CzTaJcHMJgATAI444ohcxSdSeHbsCF/WSm7ieeed9PuZwcCB9a/2BwwI7fwSe23iZrGZfQuoBL7S2OPu/hDwEEBlZaW3Ymgi+eMeKvnkJp7588NwDekcemio8Osq/cpKOOig6GOWghRlIlgLJA//1zuxrh4zOxm4BfiKu++IMB6Rtm3zZpg7t37Fn8nsWp06wfHH17/aP/JI9dmXjEWZCOYCFWZWTkgAFwIXJ29gZkOBXwLj3H1dhLGItC27dsHSpfUr/eXLw6eAdI4+el+b/siRMHgwdOwYfczSbkWWCNx9l5ldDcwkdB99xN2XmtkkoMrdZwB3AgcCT1m4ennP3c+MKiaRvHn//X1t+rNnhyv/bdvS79etW/1Kf/hw6N49+nglViK9R+DuzwHPNVh3W9LfJ0f5/CJ5sW0bvP56/av9NWvS71dcDMcdV7/iP+YY9dmXyLWJm8UiBWvPnjCFYvLV/sKFYUL1dHr12temP3JkaOc/4IDoYxZpQIlApDk2bAj99Osq/TlzwjDM6XTpEnruJN/Q7dUr+nhFMqBEIJLKzp2waFH9q/1MJ1Dv379+E88XvqBx9qXN0pkpAqG3znvv1a/0X389s0HYDjmkfhPPCSdAWVnkIYvkihKBxNOWLWG0zeQbuh9+mH6/kpIw/k7y1f5RR6nPvhQ0JQJp/3bvDn30k6/2ly7NbBC2vn3rX+0PGQKdO0cdsUirUiKQ9uejj/bvs79lS/r9unYN/fTrrvZHjIDDDos+XpE8UyKQwrZ9exh/J7niX706/X5FRWEQtuSr/X79NAibxJISgRQOd3jrrfqV/oIFYYrFdD7/+frDLVdWhk8AIqJEIG3Ypk37Jk6vq/g3bEi/X+fO4ctZyVf7ffrohq5ICkoE0jbs2hVm0Uqu9FesyGzfior6V/vHHadB2ESaQYlA8mPt2vqTq8ybl9kgbJ/7XLihW1fxDx8e+vGLSNaUCCR6n30WKvrkq/21+01Nsb8OHcLVfXITT0WFmnhEckyJQHJrzx5YuXLfl7RmzQpNPpkMwtanT/0mnuOPD2P0iEiklAikZT7+uH6lP2dOmGkrnQMOCD13kiv+nj2jj1dE9qNEIJnbuTN010xu4nnrrfT7mYVB2JIr/YEDNQibSBuhd6I0zh3efbf+Dd3582FHBtNK9+hRf7jlE04IM22JSJukRCDBp5+GoRiSr/bXZTCNdMeOYRC25Kv98nLd0BUpIEoEcbR7NyxbVv9qf9myzCZOP+qo+lf7Q4ZAp06Rhywi0VEiiIMPP6xf6VdVwdat6fc76KD6ffZHjAjNPiLSrigRtDc1NaEtP7nif++99PsVFcGgQfWv9vv108TpIjGgRFDI3GHVqvqTqyxYEIZrSOfww0OFX1fpDxsGBx4Yecgi0vYoERSSTz6pP3H67NmwcWP6/UpLQ0Wf3MTTu7du6IoIoETQdtXUwNSpcM89YXKU996DN97IbN9jj63fxDNoUJhiUUSkEUoEbYF7uMqfPBmefHL/xxcvTr3vwQfXr/SHDw8Ds4mIZEiJIB/WrYMHHwwVfyZNO3U6dAjdNZMr/qOPVhOPiLSIEkHUdu2C6dNDE88//pFdGcOHw89/Hr64VVqa0/BERJQIcm3ZslDp//KX2Zfxne/AtdeGq38RkYgpEbTEli3wm9+EJp63386ujBEj4Lrr4LzzNKuWiOSFEkGm3OHll0Ol/+yz2ZVx4IGh0r/iCujVK7fxiYhkSYkglTVr4P77Q8W/fXt2ZZxzTqj4v/xl3dAVkTYr0kRgZuOAyUAx8Gt3v6PB452AR4FhwAbgm+6+OsqYGrVjB0ybFir9efOyK+OYY0Kl/+1v6xu6OTZ9/lrunLmS9zfV0LOslOtPPZazhzb/E1WqchpbX/XuRh6fvYbd7hjQpWMx23bu3vs4wJ0zV7J2Uw3FZux2p1eDMn/y7FI+2VabMp7SkiI6lxTzybZaDKgb8q/IYNRRB7Psgy17909+vM5hXTvy8dZadmcyWGCWKg49gG079/D+phrKupSwdXsttXuyK6ustISvDz6cV1as5/1NNXQuKaImw8IavrYTZyxlU82+17astISJZw7ce16kev2Tt2v4fz+pX4+9sWVyniXv3620hNrde/hs5+5G42lq3+Y+V0veA6mYR3QSmVkx8AZwClANzAUucvdlSdtcCRzn7peb2YXAOe7+zabKrays9KqqqpYFN29eqPQfeyz7Mq66Cq65Jnx5SyIzff5abnpmMTW1+6a6LC0p5j/PHdSsN0Kqcs4b1ovfz1tbb31xkbF7T+r3RUmxgUNtI9vUlfnk3DXU7o6ugo6jva/tnDWNvvYlRcad5w8G4PqnF6Z8/UuKjG8O77Pf/72x50t1njV2PqWKp+H+zT2nc/UeMLN57l7Z6GMRJoJRwER3PzWxfBOAu/9n0jYzE9u8ZmYdgA+BHt5EUFkngldfhS99qfn7AYwZE672zzgDiouzK0OyMvqOl1m7qWa/9b3KSvnnjV9tcTl1V/O5FEWZEqR7bXuVhe7Vjf2vm1NOcnmNnWepzqdM9m/uOZ2r90BTiSDKpqFewJqk5WpgRKpt3H2XmW0GDgE+Tt7IzCYAEwCOOOKI5keyZQt85SuZbXvooaHSv+wyDbncBryf4s2Wan1zy4miwlYSiE661zbT8yLT/1FLz7/Gtmtumbl6DzSlIMYYdveH3L3S3St7ZFM579kD3bs3/tjFF4fhHdzDz0cfwc03Kwm0ET3LGv8CXar1zS2nOIKb+FGUKUG617ZnWWlG50am/6OWnn+NbdfcMnP1HmhKlIlgLdAnabl3Yl2j2ySahroRbhrnVrduobK//fbQ77+mZl/FP3Vq6MsvbdL1px5LaUn95rjSkuK9N2xbWs5FI/rst764qOlKoqTYKEmxTV2ZJcVKBrm297VN8dqXFBnXn3os1596bJOvf0mRNfp/b+z5Up1njZ1PqeLJZN/mPlc274GmRNk0NBeoMLNyQoV/IXBxg21mAJcCrwHfAF5u6v5Ai5SXw623RlK0RKfuZlhLe0w0VU7lkQfnvNdQ5ZEHq9dQA7nqNVR55MFpew0BaXsNNfy/N6fXUMPzqTm9hpp7TufqPdCUyG4WA5jZ6cB/E7qPPuLu/9vMJgFV7j7DzDoDjwFDgY3Ahe7e5Fd0c9JrSEQkZvJ1sxh3fw54rsG625L+3g6cH2UMIiLStIK4WSwiItFRIhARiTklAhGRmFMiEBGJOSUCEZGYi7T7aBTMbD3wbo6L7U6DYS0KkI6h7WgPx6FjaBtyeQxHunujQyYUXCKIgplVpepfWyh0DG1HezgOHUPb0FrHoKYhEZGYUyIQEYk5JYLgoXwHkAM6hrajPRyHjqFtaJVj0D0CEZGY0ycCEZGYUyIQEYm52CcCMxtnZivNbJWZ3ZjveDJhZo+Y2TozW5K07mAze8HM3kz8/lw+Y0zHzPqY2StmtszMlprZdYn1BXMcZtbZzOaY2cLEMfwksb7czGYnzqknzaxjvmNNx8yKzWy+mf0psVyIx7DazBab2QIzq0qsK5jzCcDMyszsaTNbYWbLzWxUaxxDrBOBmRUD9wOnAQOAi8xsQH6jysgUYFyDdTcCL7l7BfBSYrkt2wX8yN0HACOBqxKvfSEdxw7gq+4+GBgCjDOzkcDPgJ+7+9HAJ8B38xdixq4DlictF+IxAJzk7kOS+t4X0vkEMBl43t37AYMJ/5Poj8HdY/sDjAJmJi3fBNyU77gyjL0vsCRpeSVweOLvw4GV+Y6xmcfzR+CUQj0OoAvwOjCC8E3QDon19c6xtvhDmEb2JeCrwJ8IE6IV1DEk4lwNdG+wrmDOJ8JUve+Q6MTTmscQ608EQC9gTdJydWJdITrM3T9I/P0hcFg+g2kOM+tLmKVuNgV2HIkmlQXAOuAF4C1gk7vvSmxSCOfUfwM3AHXzRh5C4R0DhNk8/2pm88xsQmJdIZ1P5cB64DeJZrpfm9kBtMIxxD0RtEseLh0Kol+wmR0I/B74gbt/mvxYIRyHu+929yGEq+rhQL/8RtQ8ZvZ1YJ27z8t3LDnwRXc/ntDUe5WZfTn5wQI4nzoAxwO/cPehwGc0aAaK6hjingjWAn2Slnsn1hWij8zscIDE73V5jictMyshJIGp7v5MYnXBHQeAu28CXiE0o5SZWd00sG39nBoNnGlmq4EnCM1DkymsYwDA3dcmfq8D/kBIzIV0PlUD1e4+O7H8NCExRH4McU8Ec4GKRA+JjsCFwIw8x5StGcClib8vJbS5t1lmZsDDwHJ3vzvpoYI5DjPrYWZlib9LCfc4lhMSwjcSm7XpY3D3m9y9t7v3JZz/L7v7JRTQMQCY2QFm1rXub+BrwBIK6Hxy9w+BNWZ2bGLVWGAZrXEM+b5Bku8f4HTgDULb7i35jifDmB8HPgBqCVcR3yW0674EvAm8CByc7zjTHMMXCR9xFwELEj+nF9JxAMcB8xPHsAS4LbH+KGAOsAp4CuiU71gzPJ4xwJ8K8RgS8S5M/Cytey8X0vmUiHcIUJU4p6YDn2uNY9AQEyIiMRf3piERkdhTIhARiTklAhGRmFMiEBGJOSUCEZGYUyKQdsvMtjZYHm9m96XZ58xMRqE1szF1I3VGITEK5ZWt9XwSb0oEIkncfYa739FwfdK3bFtLGXBluo1EckGJQGLJzM5IjLc/38xeNLPDEuv3fmowsylm9qCZzQb+K8Nyv2Zmr5nZ62b2VGIspbqx8n+SWL/YzPol1vdIjDG/NDHI2Ltm1h24A/i3xNj6dyaKPzBprPqpiW9ni7SYEoG0Z6WJinRBYoTQSUmPvQqM9DC41xOE0Tcb0xs40d1/mO7JEhX4rcDJHgY/qwKS9/s4sf4XwP9MrPsPwrAOAwljyxyRWH8j8JaHsfWvT6wbCvyAMHfGUYRxgkRarLU/7oq0phoPI4MC4WofqJuwpDfwZGIQr46EceAb85S7787w+UYSKul/Ji7WOwKvJT1eN7DePODcxN9fBM4BcPfnzeyTJsqf4+7ViWNZQJiT4tUMYxNJSYlA4upe4G53n2FmY4CJKbb7rBllGvCCu1+U4vEdid+7ye69tyPp72zLENmPmoYkrrqxb2jlS5vasBlmAaPN7GjYOyLmMWn2+SdwQWL7rxEGGQPYAnTNUVwiTVIikLiaCDxlZvMI0zJmY6yZVdf9AEcD44HHzWwRoVko3UQ1PwG+ZmZLgPMJM1BtcfcNhCamJUk3i0UiodFHRfLIzDoBu919l5mNIsxONSTPYUnMqI1RJL+OAKaZWRGwE7gsz/FIDOkTgYhIzOkegYhIzCkRiIjEnBKBiEjMKRGIiMScEoGISMz9fxua9jfQP4yjAAAAAElFTkSuQmCC\n",
135 |       "text/plain": [
136 |        "<Figure size 432x288 with 1 Axes>"
137 |       ]
138 |      },
139 |      "metadata": {
140 |       "needs_background": "light"
141 |      },
142 |      "output_type": "display_data"
143 |     }
144 |    ],
145 |    "source": [
146 |     "# Adaline \n",
147 |     "\n",
148 |     "m=np.matmul(inv(np.matmul(X.T,X)),np.matmul(X.T,Y))\n",
149 |     "y_pred=np.matmul(X,m)\n",
150 |     "\n",
151 |     "plt.scatter(X[:n],Y[:n],label='famale')\n",
152 |     "plt.scatter(X[n:],Y[n:],label='man')\n",
153 |     "plt.plot(X,y_pred,c='red',lw=3)\n",
154 |     "plt.xlabel('Hair Length')\n",
155 |     "plt.ylabel('Gender')\n",
156 |     "plt.legend()"
157 |    ]
158 |   },
159 |   {
160 |    "cell_type": "code",
161 |    "execution_count": null,
162 |    "id": "cb608d7d",
163 |    "metadata": {},
164 |    "outputs": [],
165 |    "source": []
166 |   }
167 |  ],
168 |  "metadata": {
169 |   "kernelspec": {
170 |    "display_name": "Python 3 (ipykernel)",
171 |    "language": "python",
172 |    "name": "python3"
173 |   },
174 |   "language_info": {
175 |    "codemirror_mode": {
176 |     "name": "ipython",
177 |     "version": 3
178 |    },
179 |    "file_extension": ".py",
180 |    "mimetype": "text/x-python",
181 |    "name": "python",
182 |    "nbconvert_exporter": "python",
183 |    "pygments_lexer": "ipython3",
184 |    "version": "3.7.10"
185 |   }
186 |  },
187 |  "nbformat": 4,
188 |  "nbformat_minor": 5
189 | }
190 | 


--------------------------------------------------------------------------------
/iris_classifier.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 98,
  6 |    "id": "121a4f69",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import numpy as np\n",
 11 |     "from sklearn.datasets import load_iris\n",
 12 |     "from sklearn.model_selection import train_test_split\n",
 13 |     "from numpy.linalg import inv"
 14 |    ]
 15 |   },
 16 |   {
 17 |    "cell_type": "code",
 18 |    "execution_count": 99,
 19 |    "id": "8a32a739",
 20 |    "metadata": {},
 21 |    "outputs": [
 22 |     {
 23 |      "data": {
 24 |       "text/plain": [
 25 |        "{'data': array([[5.1, 3.5, 1.4, 0.2],\n",
 26 |        "        [4.9, 3. , 1.4, 0.2],\n",
 27 |        "        [4.7, 3.2, 1.3, 0.2],\n",
 28 |        "        [4.6, 3.1, 1.5, 0.2],\n",
 29 |        "        [5. , 3.6, 1.4, 0.2],\n",
 30 |        "        [5.4, 3.9, 1.7, 0.4],\n",
 31 |        "        [4.6, 3.4, 1.4, 0.3],\n",
 32 |        "        [5. , 3.4, 1.5, 0.2],\n",
 33 |        "        [4.4, 2.9, 1.4, 0.2],\n",
 34 |        "        [4.9, 3.1, 1.5, 0.1],\n",
 35 |        "        [5.4, 3.7, 1.5, 0.2],\n",
 36 |        "        [4.8, 3.4, 1.6, 0.2],\n",
 37 |        "        [4.8, 3. , 1.4, 0.1],\n",
 38 |        "        [4.3, 3. , 1.1, 0.1],\n",
 39 |        "        [5.8, 4. , 1.2, 0.2],\n",
 40 |        "        [5.7, 4.4, 1.5, 0.4],\n",
 41 |        "        [5.4, 3.9, 1.3, 0.4],\n",
 42 |        "        [5.1, 3.5, 1.4, 0.3],\n",
 43 |        "        [5.7, 3.8, 1.7, 0.3],\n",
 44 |        "        [5.1, 3.8, 1.5, 0.3],\n",
 45 |        "        [5.4, 3.4, 1.7, 0.2],\n",
 46 |        "        [5.1, 3.7, 1.5, 0.4],\n",
 47 |        "        [4.6, 3.6, 1. , 0.2],\n",
 48 |        "        [5.1, 3.3, 1.7, 0.5],\n",
 49 |        "        [4.8, 3.4, 1.9, 0.2],\n",
 50 |        "        [5. , 3. , 1.6, 0.2],\n",
 51 |        "        [5. , 3.4, 1.6, 0.4],\n",
 52 |        "        [5.2, 3.5, 1.5, 0.2],\n",
 53 |        "        [5.2, 3.4, 1.4, 0.2],\n",
 54 |        "        [4.7, 3.2, 1.6, 0.2],\n",
 55 |        "        [4.8, 3.1, 1.6, 0.2],\n",
 56 |        "        [5.4, 3.4, 1.5, 0.4],\n",
 57 |        "        [5.2, 4.1, 1.5, 0.1],\n",
 58 |        "        [5.5, 4.2, 1.4, 0.2],\n",
 59 |        "        [4.9, 3.1, 1.5, 0.2],\n",
 60 |        "        [5. , 3.2, 1.2, 0.2],\n",
 61 |        "        [5.5, 3.5, 1.3, 0.2],\n",
 62 |        "        [4.9, 3.6, 1.4, 0.1],\n",
 63 |        "        [4.4, 3. , 1.3, 0.2],\n",
 64 |        "        [5.1, 3.4, 1.5, 0.2],\n",
 65 |        "        [5. , 3.5, 1.3, 0.3],\n",
 66 |        "        [4.5, 2.3, 1.3, 0.3],\n",
 67 |        "        [4.4, 3.2, 1.3, 0.2],\n",
 68 |        "        [5. , 3.5, 1.6, 0.6],\n",
 69 |        "        [5.1, 3.8, 1.9, 0.4],\n",
 70 |        "        [4.8, 3. , 1.4, 0.3],\n",
 71 |        "        [5.1, 3.8, 1.6, 0.2],\n",
 72 |        "        [4.6, 3.2, 1.4, 0.2],\n",
 73 |        "        [5.3, 3.7, 1.5, 0.2],\n",
 74 |        "        [5. , 3.3, 1.4, 0.2],\n",
 75 |        "        [7. , 3.2, 4.7, 1.4],\n",
 76 |        "        [6.4, 3.2, 4.5, 1.5],\n",
 77 |        "        [6.9, 3.1, 4.9, 1.5],\n",
 78 |        "        [5.5, 2.3, 4. , 1.3],\n",
 79 |        "        [6.5, 2.8, 4.6, 1.5],\n",
 80 |        "        [5.7, 2.8, 4.5, 1.3],\n",
 81 |        "        [6.3, 3.3, 4.7, 1.6],\n",
 82 |        "        [4.9, 2.4, 3.3, 1. ],\n",
 83 |        "        [6.6, 2.9, 4.6, 1.3],\n",
 84 |        "        [5.2, 2.7, 3.9, 1.4],\n",
 85 |        "        [5. , 2. , 3.5, 1. ],\n",
 86 |        "        [5.9, 3. , 4.2, 1.5],\n",
 87 |        "        [6. , 2.2, 4. , 1. ],\n",
 88 |        "        [6.1, 2.9, 4.7, 1.4],\n",
 89 |        "        [5.6, 2.9, 3.6, 1.3],\n",
 90 |        "        [6.7, 3.1, 4.4, 1.4],\n",
 91 |        "        [5.6, 3. , 4.5, 1.5],\n",
 92 |        "        [5.8, 2.7, 4.1, 1. ],\n",
 93 |        "        [6.2, 2.2, 4.5, 1.5],\n",
 94 |        "        [5.6, 2.5, 3.9, 1.1],\n",
 95 |        "        [5.9, 3.2, 4.8, 1.8],\n",
 96 |        "        [6.1, 2.8, 4. , 1.3],\n",
 97 |        "        [6.3, 2.5, 4.9, 1.5],\n",
 98 |        "        [6.1, 2.8, 4.7, 1.2],\n",
 99 |        "        [6.4, 2.9, 4.3, 1.3],\n",
100 |        "        [6.6, 3. , 4.4, 1.4],\n",
101 |        "        [6.8, 2.8, 4.8, 1.4],\n",
102 |        "        [6.7, 3. , 5. , 1.7],\n",
103 |        "        [6. , 2.9, 4.5, 1.5],\n",
104 |        "        [5.7, 2.6, 3.5, 1. ],\n",
105 |        "        [5.5, 2.4, 3.8, 1.1],\n",
106 |        "        [5.5, 2.4, 3.7, 1. ],\n",
107 |        "        [5.8, 2.7, 3.9, 1.2],\n",
108 |        "        [6. , 2.7, 5.1, 1.6],\n",
109 |        "        [5.4, 3. , 4.5, 1.5],\n",
110 |        "        [6. , 3.4, 4.5, 1.6],\n",
111 |        "        [6.7, 3.1, 4.7, 1.5],\n",
112 |        "        [6.3, 2.3, 4.4, 1.3],\n",
113 |        "        [5.6, 3. , 4.1, 1.3],\n",
114 |        "        [5.5, 2.5, 4. , 1.3],\n",
115 |        "        [5.5, 2.6, 4.4, 1.2],\n",
116 |        "        [6.1, 3. , 4.6, 1.4],\n",
117 |        "        [5.8, 2.6, 4. , 1.2],\n",
118 |        "        [5. , 2.3, 3.3, 1. ],\n",
119 |        "        [5.6, 2.7, 4.2, 1.3],\n",
120 |        "        [5.7, 3. , 4.2, 1.2],\n",
121 |        "        [5.7, 2.9, 4.2, 1.3],\n",
122 |        "        [6.2, 2.9, 4.3, 1.3],\n",
123 |        "        [5.1, 2.5, 3. , 1.1],\n",
124 |        "        [5.7, 2.8, 4.1, 1.3],\n",
125 |        "        [6.3, 3.3, 6. , 2.5],\n",
126 |        "        [5.8, 2.7, 5.1, 1.9],\n",
127 |        "        [7.1, 3. , 5.9, 2.1],\n",
128 |        "        [6.3, 2.9, 5.6, 1.8],\n",
129 |        "        [6.5, 3. , 5.8, 2.2],\n",
130 |        "        [7.6, 3. , 6.6, 2.1],\n",
131 |        "        [4.9, 2.5, 4.5, 1.7],\n",
132 |        "        [7.3, 2.9, 6.3, 1.8],\n",
133 |        "        [6.7, 2.5, 5.8, 1.8],\n",
134 |        "        [7.2, 3.6, 6.1, 2.5],\n",
135 |        "        [6.5, 3.2, 5.1, 2. ],\n",
136 |        "        [6.4, 2.7, 5.3, 1.9],\n",
137 |        "        [6.8, 3. , 5.5, 2.1],\n",
138 |        "        [5.7, 2.5, 5. , 2. ],\n",
139 |        "        [5.8, 2.8, 5.1, 2.4],\n",
140 |        "        [6.4, 3.2, 5.3, 2.3],\n",
141 |        "        [6.5, 3. , 5.5, 1.8],\n",
142 |        "        [7.7, 3.8, 6.7, 2.2],\n",
143 |        "        [7.7, 2.6, 6.9, 2.3],\n",
144 |        "        [6. , 2.2, 5. , 1.5],\n",
145 |        "        [6.9, 3.2, 5.7, 2.3],\n",
146 |        "        [5.6, 2.8, 4.9, 2. ],\n",
147 |        "        [7.7, 2.8, 6.7, 2. ],\n",
148 |        "        [6.3, 2.7, 4.9, 1.8],\n",
149 |        "        [6.7, 3.3, 5.7, 2.1],\n",
150 |        "        [7.2, 3.2, 6. , 1.8],\n",
151 |        "        [6.2, 2.8, 4.8, 1.8],\n",
152 |        "        [6.1, 3. , 4.9, 1.8],\n",
153 |        "        [6.4, 2.8, 5.6, 2.1],\n",
154 |        "        [7.2, 3. , 5.8, 1.6],\n",
155 |        "        [7.4, 2.8, 6.1, 1.9],\n",
156 |        "        [7.9, 3.8, 6.4, 2. ],\n",
157 |        "        [6.4, 2.8, 5.6, 2.2],\n",
158 |        "        [6.3, 2.8, 5.1, 1.5],\n",
159 |        "        [6.1, 2.6, 5.6, 1.4],\n",
160 |        "        [7.7, 3. , 6.1, 2.3],\n",
161 |        "        [6.3, 3.4, 5.6, 2.4],\n",
162 |        "        [6.4, 3.1, 5.5, 1.8],\n",
163 |        "        [6. , 3. , 4.8, 1.8],\n",
164 |        "        [6.9, 3.1, 5.4, 2.1],\n",
165 |        "        [6.7, 3.1, 5.6, 2.4],\n",
166 |        "        [6.9, 3.1, 5.1, 2.3],\n",
167 |        "        [5.8, 2.7, 5.1, 1.9],\n",
168 |        "        [6.8, 3.2, 5.9, 2.3],\n",
169 |        "        [6.7, 3.3, 5.7, 2.5],\n",
170 |        "        [6.7, 3. , 5.2, 2.3],\n",
171 |        "        [6.3, 2.5, 5. , 1.9],\n",
172 |        "        [6.5, 3. , 5.2, 2. ],\n",
173 |        "        [6.2, 3.4, 5.4, 2.3],\n",
174 |        "        [5.9, 3. , 5.1, 1.8]]),\n",
175 |        " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
176 |        "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
177 |        "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
178 |        "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
179 |        "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
180 |        "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
181 |        "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
182 |        " 'frame': None,\n",
183 |        " 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),\n",
184 |        " 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n                \\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...',\n",
185 |        " 'feature_names': ['sepal length (cm)',\n",
186 |        "  'sepal width (cm)',\n",
187 |        "  'petal length (cm)',\n",
188 |        "  'petal width (cm)'],\n",
189 |        " 'filename': '/home/deep/anaconda3/envs/py36/lib/python3.6/site-packages/sklearn/datasets/data/iris.csv'}"
190 |       ]
191 |      },
192 |      "execution_count": 99,
193 |      "metadata": {},
194 |      "output_type": "execute_result"
195 |     }
196 |    ],
197 |    "source": [
198 |     "data=load_iris()\n",
199 |     "data"
200 |    ]
201 |   },
202 |   {
203 |    "cell_type": "code",
204 |    "execution_count": 168,
205 |    "id": "0d943064",
206 |    "metadata": {},
207 |    "outputs": [],
208 |    "source": [
209 |     "X=data.data\n",
210 |     "Y=data.target\n",
211 |     "indx = 0\n",
212 |     "\n",
213 |     "X= X[np.where((Y == 1) | (Y == 2))]\n",
214 |     "Y = Y[np.where((Y == 1) | (Y == 2))]\n"
215 |    ]
216 |   },
217 |   {
218 |    "cell_type": "code",
219 |    "execution_count": 175,
220 |    "id": "9f9b662d",
221 |    "metadata": {},
222 |    "outputs": [],
223 |    "source": [
224 |     "class AdalineClassifier:\n",
225 |     "    def __init__(self):\n",
226 |     "        pass\n",
227 |     "    def fit(self,x_train,y_train):\n",
228 |     "        self.m=np.matmul(inv(np.matmul(x_train.T,x_train)),np.matmul(x_train.T,y_train))\n",
229 |     "    def predict(self,x_test):\n",
230 |     "        y_pred=np.matmul(x_test,self.m)\n",
231 |     "        y_pred=np.round(y_pred)\n",
232 |     "        return y_pred\n",
233 |     "    def evaluate(self,x_test,y_test):\n",
234 |     "        y_pred=np.matmul(x_test,self.m)\n",
235 |     "#         y_pred=np.round(y_pred\n",
236 |     "        y_pred[np.where(y_pred < 1)] = 1\n",
237 |     "        y_pred[np.where(y_pred >= 2)] = 2\n",
238 |     "        loss = np.mean(np.abs(np.subtract(y_test, y_pred)))\n",
239 |     "        return loss \n",
240 |     "#         if np.min(y_test)==0\n",
241 |     "        "
242 |    ]
243 |   },
244 |   {
245 |    "cell_type": "code",
246 |    "execution_count": 176,
247 |    "id": "d5b7e556",
248 |    "metadata": {},
249 |    "outputs": [
250 |     {
251 |      "name": "stdout",
252 |      "output_type": "stream",
253 |      "text": [
254 |       "m [-0.01087176 -0.34079226  0.288308    0.67914941]\n"
255 |      ]
256 |     }
257 |    ],
258 |    "source": [
259 |     "x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.5)\n",
260 |     "model=AdalineClassifier()\n",
261 |     "model.fit(x_train,y_train)\n",
262 |     "print('m',model.m)\n",
263 |     "y_pred=model.predict(x_test)\n",
264 |     "loss = model.evaluate(x_test, y_test)"
265 |    ]
266 |   },
267 |   {
268 |    "cell_type": "code",
269 |    "execution_count": 181,
270 |    "id": "5a16c6b1",
271 |    "metadata": {},
272 |    "outputs": [
273 |     {
274 |      "name": "stdout",
275 |      "output_type": "stream",
276 |      "text": [
277 |       "loss: 0.15998411915144156\n"
278 |      ]
279 |     }
280 |    ],
281 |    "source": [
282 |     "print('loss:',loss)"
283 |    ]
284 |   },
285 |   {
286 |    "cell_type": "code",
287 |    "execution_count": 178,
288 |    "id": "292aedaa",
289 |    "metadata": {},
290 |    "outputs": [],
291 |    "source": [
292 |     "class KNearestNeighbors:\n",
293 |     "    def __init__(self,k):\n",
294 |     "        self.k=k\n",
295 |     "    def fit(self,x_train,y_train):\n",
296 |     "        self.x_train=x_train\n",
297 |     "        self.y_train=y_train\n",
298 |     "        self.number_classes=len(np.unique(y_train))\n",
299 |     "    def NearNeighbors(self,x_test):\n",
300 |     "        distance=np.sum(np.subtract(x_test,self.x_train)**2, axis=1)\n",
301 |     "        near_neighbors=np.argsort(distance)[:self.k]\n",
302 |     "        return near_neighbors\n",
303 |     "    def predict(self,x_test):\n",
304 |     "        near_neighbors=self.NearNeighbors(x_test)\n",
305 |     "        y=np.argmax(np.bincount(self.y_train[near_neighbors]))\n",
306 |     "        return y\n",
307 |     "    def evaluate(self,x_test,y_test):\n",
308 |     "        self.x_test=x_test\n",
309 |     "        self.y_test=y_test\n",
310 |     "        y_predict=np.zeros(len(self.x_test))\n",
311 |     "        \n",
312 |     "        for i,test in enumerate(self.x_test):\n",
313 |     "            y_predict[i]=self.predict(test)\n",
314 |     "        evaluatation=(y_predict==self.y_test).sum()/len(self.y_test)\n",
315 |     "        return evaluatation"
316 |    ]
317 |   },
318 |   {
319 |    "cell_type": "code",
320 |    "execution_count": 182,
321 |    "id": "6b1514ca",
322 |    "metadata": {},
323 |    "outputs": [
324 |     {
325 |      "name": "stdout",
326 |      "output_type": "stream",
327 |      "text": [
328 |       "acc:  0.94\n"
329 |      ]
330 |     }
331 |    ],
332 |    "source": [
333 |     "knn=KNearestNeighbors(k=5)\n",
334 |     "knn.fit(x_train,y_train)\n",
335 |     "\n",
336 |     "y_pred=knn.predict(x_test)\n",
337 |     "acc=knn.evaluate(x_test,y_test)\n",
338 |     "print('acc: ',acc)"
339 |    ]
340 |   },
341 |   {
342 |    "cell_type": "code",
343 |    "execution_count": null,
344 |    "id": "f4d9e968",
345 |    "metadata": {},
346 |    "outputs": [],
347 |    "source": []
348 |   }
349 |  ],
350 |  "metadata": {
351 |   "kernelspec": {
352 |    "display_name": "Python 3 (ipykernel)",
353 |    "language": "python",
354 |    "name": "python3"
355 |   },
356 |   "language_info": {
357 |    "codemirror_mode": {
358 |     "name": "ipython",
359 |     "version": 3
360 |    },
361 |    "file_extension": ".py",
362 |    "mimetype": "text/x-python",
363 |    "name": "python",
364 |    "nbconvert_exporter": "python",
365 |    "pygments_lexer": "ipython3",
366 |    "version": "3.6.12"
367 |   }
368 |  },
369 |  "nbformat": 4,
370 |  "nbformat_minor": 5
371 | }
372 | 


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