SVD.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import TruncatedSVD, PCA\n",
    "from sklearn.datasets import load_iris\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       [ 8.49037542e+00, -9.15931258e-01]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = load_iris()\n",
    "iris_ftrs = iris.data\n",
    "tsvd = TruncatedSVD(n_components=2)\n",
    "iris_tsvd= tsvd.fit_transform(iris_ftrs)\n",
    "iris_tsvd #변환된 feature extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.52875361, 0.44845576])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsvd.explained_variance_ratio_ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "원본 행렬: \n",
      " [[0.11133083 0.21076757 0.23296249 0.15194456 0.83017814 0.40791941]\n",
      " [0.5557906  0.74552394 0.24849976 0.9686594  0.95268418 0.48984885]\n",
      " [0.01829731 0.85760612 0.40493829 0.62247394 0.29537149 0.92958852]\n",
      " [0.4056155  0.56730065 0.24575605 0.22573721 0.03827786 0.58098021]\n",
      " [0.82925331 0.77326256 0.94693849 0.73632338 0.67328275 0.74517176]\n",
      " [0.51161442 0.46920965 0.6439515  0.82081228 0.14548493 0.01806415]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.sparse.linalg import svds\n",
    "from scipy.linalg import svd\n",
    "\n",
    "np.random.seed(121)\n",
    "matrix=np.random.random((6,6))\n",
    "print('원본 행렬: \\n', matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 분해 행렬 차원: (6, 6) (6,) (6, 6)\n",
      "\n",
      " Sigma 행렬: [3.2535007  0.88116505 0.83865238 0.55463089 0.35834824 0.0349925 ]\n"
     ]
    }
   ],
   "source": [
    "U, Sigma, Vt = svd(matrix, full_matrices=False)\n",
    "print('\\n 분해 행렬 차원:',U.shape,Sigma.shape,Vt.shape)\n",
    "print('\\n Sigma 행렬:', Sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Truncated SVD 분해 행렬 차원: (6, 4) (4,) (4, 6)\n",
      "\n",
      "truncated SVD Sigma값 행렬: [0.55463089 0.83865238 0.88116505 3.2535007 ]\n",
      "\n",
      "Truncated SVD로 분해 후 복원 행렬:\n",
      " [[0.19222941 0.21792946 0.15951023 0.14084013 0.81641405 0.42533093]\n",
      " [0.44874275 0.72204422 0.34594106 0.99148577 0.96866325 0.4754868 ]\n",
      " [0.12656662 0.88860729 0.30625735 0.59517439 0.28036734 0.93961948]\n",
      " [0.23989012 0.51026588 0.39697353 0.27308905 0.05971563 0.57156395]\n",
      " [0.83806144 0.78847467 0.93868685 0.72673231 0.6740867  0.73812389]\n",
      " [0.59726589 0.47953891 0.56613544 0.80746028 0.13135039 0.03479656]]\n"
     ]
    }
   ],
   "source": [
    "num_components=4\n",
    "U_tr,Sigma_tr,Vt_tr =svds(matrix,k=num_components)\n",
    "print(\"\\nTruncated SVD 분해 행렬 차원:\",U_tr.shape,Sigma_tr.shape,Vt_tr.shape)\n",
    "print('\\ntruncated SVD Sigma값 행렬:', Sigma_tr)\n",
    "matrix_tr=np.dot(np.dot(U_tr,np.diag(Sigma_tr)),Vt_tr)\n",
    "print(\"\\nTruncated SVD로 분해 후 복원 행렬:\\n\", matrix_tr)"
   ]
  }
 ],
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