Simi_ML_SVM 1_Classnotes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "23d65862",
   "metadata": {},
   "outputs": [],
   "source": [
    "#svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "07532e5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d926eee9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a4761724",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "06d83e9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1a4af4a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "31b7ba94",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=np.array([\n",
    "    [1,2],\n",
    "    [5,8],\n",
    "    [1.5,1.8],\n",
    "    [8,8],\n",
    "    [1,0.6],\n",
    "    [9,11],\n",
    "    [2,2],\n",
    "    [3,2.5],\n",
    "    [10,6],\n",
    "    [6,7]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "38cb6f4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = ['A','B','A','B','A','B','A','A','B','B']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "336f14ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x259759256a0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[: , 0],X[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6d37a16d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = svm.SVC(kernel='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c2d01e1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "34916955",
   "metadata": {},
   "outputs": [],
   "source": [
    "p=model.predict([[0.58,0.76]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4967e214",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A']\n"
     ]
    }
   ],
   "source": [
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eacf678f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d23deed1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05439819",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cd4d0c87",
   "metadata": {},
   "outputs": [],
   "source": [
    "w=model.coef_[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "27454644",
   "metadata": {},
   "outputs": [],
   "source": [
    "p=-w[0]/w[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "611706b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "xx=np.linspace(min(X[: , 0]),max(X[:,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f391e293",
   "metadata": {},
   "outputs": [],
   "source": [
    "yy=p*xx-model.intercept_[0]/w[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e97eff58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x25975a35d30>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xx,yy,c='y')\n",
    "plt.scatter(X[: , 0],X[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53aa5ea4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}