PCA-Class.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e8abb3c3",
   "metadata": {},
   "source": [
    "# PCA Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "307fcb72",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7719f514",
   "metadata": {},
   "outputs": [],
   "source": [
    "class My_PCA():\n",
    "    \"\"\"\n",
    "    My PCA class\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, n_components=2):\n",
    "        self.n_components = n_components\n",
    "    \n",
    "    \n",
    "    def fit(self, data):\n",
    "        data = data.T\n",
    "        data_cov = np.cov(data)\n",
    "        _, eig_vec = np.linalg.eig(data_cov)\n",
    "        eig_vec[:,1] *= -1 # Fixing small error ;-;\n",
    "        self.eig_vec = eig_vec.T\n",
    "    \n",
    "    \n",
    "    def transform(self, data):\n",
    "        data = data.T\n",
    "        components = self.eig_vec[:self.n_components, :]\n",
    "    \n",
    "        return np.dot(components, data).T\n",
    "    \n",
    "\n",
    "    def fit_transform(self, data):\n",
    "        eig_vec = fit(data)\n",
    "        transform(eig_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "40e0c3b3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading dataset\n",
    "X, _ = datasets.load_iris(return_X_y=True)\n",
    "\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "62b318ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaled_X = StandardScaler().fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3c8a8b5",
   "metadata": {},
   "source": [
    "# Comparing with PCA from skleran"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "14290033",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.26470281,  0.4800266 ],\n",
       "       [-2.08096115, -0.67413356],\n",
       "       [-2.36422905, -0.34190802]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mypca = My_PCA(2)\n",
    "\n",
    "mypca.fit(scaled_X)\n",
    "\n",
    "mypcadata = mypca.transform(scaled_X)\n",
    "\n",
    "mypcadata[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f2855f7",
   "metadata": {},
   "source": [
    "mypca = My_PCA(2)\n",
    "\n",
    "mypcadata = mypca.fit_transform(scaled_X)\n",
    "\n",
    "mypcadata[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cd6e0515",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.26470281,  0.4800266 ],\n",
       "       [-2.08096115, -0.67413356],\n",
       "       [-2.36422905, -0.34190802]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(2)\n",
    "\n",
    "pcadata = pca.fit_transform(scaled_X)\n",
    "\n",
    "pcadata[:3]"
   ]
  }
 ],
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