ridge_regresson_from scratch m and b.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6ea3b40b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_regression\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7de2d6dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y = make_regression(n_samples=100,n_features=1,n_informative=1,n_targets=1,noise=20,random_state=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4764934b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2c1333dcd60>"
      ]
     },
     "execution_count": 3,
     "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,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d11b331f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "36054c8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[27.82809103]\n",
      "-2.29474455867698\n"
     ]
    }
   ],
   "source": [
    "lr=LinearRegression()\n",
    "lr.fit(X,y)\n",
    "print(lr.coef_)\n",
    "print(lr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9428002",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6fd9916c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "27fd410d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[24.9546267]\n",
      "-2.1269130035235735\n"
     ]
    }
   ],
   "source": [
    "rr2=Ridge(alpha=10)\n",
    "rr2.fit(X,y)\n",
    "print(rr2.coef_)\n",
    "print(rr2.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5f40ccf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12.93442104]\n",
      "-1.4248441496033308\n"
     ]
    }
   ],
   "source": [
    "rr=Ridge(alpha=100)\n",
    "rr.fit(X,y)\n",
    "print(rr.coef_)\n",
    "print(rr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "90827e31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2c136df62b0>"
      ]
     },
     "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.plot(X,y,\"b.\")\n",
    "plt.plot(X,lr.predict(X),color=\"r\",label=\"alpha=0\")\n",
    "plt.plot(X,rr.predict(X),color=\"g\",label=\"alpha=20\")\n",
    "plt.plot(X,rr2.predict(X),color=\"y\",label=\"alpha=100\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bf4481f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "class meraridge:\n",
    "    def __init__(self,alpha=.1):\n",
    "        self.alpha=alpha\n",
    "        self.m=None\n",
    "        self.b=None\n",
    "    def fit(self,x_train,y_train):\n",
    "        num=0\n",
    "        den=0\n",
    "        for i in range(x_train.shape[0]):\n",
    "            num=num+(y_train[i]-y_train.mean())*(x_train[i]-x_train.mean())\n",
    "            den=den+(x_train[i]-x_train.mean())*(x_train[i]-x_train.mean()) \n",
    "        self.m=num/(den +self.alpha)\n",
    "        self.b=y_train.mean() -(self.m*x_train.mean())\n",
    "        print(self.m,self.b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5f998141",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg=meraridge(alpha=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "23dd91e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12.93442104] [-1.42484415]\n"
     ]
    }
   ],
   "source": [
    "reg.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c50628e9",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}