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# Khyati MahendruKhyatiMahendru

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Created Jul 25, 2019
View spectral_clustering.py
 # import required functions and libraries from sklearn.datasets import make_circles from sklearn.neighbors import kneighbors_graph from sklearn.cluster import SpectralClustering import numpy as np import matplotlib.pyplot as plt # generate your data X, labels = make_circles(n_samples=500, noise=0.1, factor=.2)
Created Jul 23, 2019
View ImageCompression.py
 # get the image from "https://cdn.pixabay.com/photo/2017/03/27/16/50/beach-2179624_960_720.jpg" import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 # read image in grayscale img = cv2.imread('beach-2179624_960_720.jpg', 0) # obtain svd
Created Jul 23, 2019
View rsvd.py
 import numpy as np from sklearn.utils.extmath import randomized_svd A = np.array([[-1, 2, 0], [2, 0, -2], [0, -2, 1]]) u, s, vt = randomized_svd(A, n_components = 2) print("Left Singular Vectors:") print(u) print("Singular Values:")
Created Jul 23, 2019
View tsvd_sklearn.py
 import numpy as np from sklearn.decomposition import TruncatedSVD A = np.array([[-1, 2, 0], [2, 0, -2], [0, -2, 1]]) print("Original Matrix:") print(A) svd = TruncatedSVD(n_components = 2) A_transf = svd.fit_transform(A)
Last active Jul 23, 2019
View svd_numpy.py
 import numpy as np from numpy.linalg import svd # define your matrix as a 2D numpy array A = np.array([[4, 0], [3, -5]]) U, S, VT = svd(A) print("Left Singular Vectors:") print(U)
Created Jul 17, 2019
View LatentSemanticAnalysis.py
 # create document term matrix for your data # you can use TfidfVectorizer instead of CountVectorizer as well from sklearn.feature_extraction.text import CountVectorizer cvec = CountVectorizer() docTermMat = cvec.fit_transform(data['text'].values) # truncated SVD to preserve 20 topics from sklearn.decomposition import TruncatedSVD lsa = TruncatedSVD(n_components = 20, n_iter = 500) lsa.fit(docTermMat)
Created Jul 16, 2019
View svd.py
 from sklearn.decomposition import TruncatedSVD // say you want to reduce to 2 features svd = TruncatedSVD(n_features = 2) //obtain the transformed data data_transformed = svd.fit_transform(data)
Last active Jul 17, 2019
View imageprocessing.py
 # import required libraries import numpy as np import matplotlib.pyplot as plt import cv2 from skimage.color import rgb2gray from scipy import ndimage # read the image img = cv2.imread('1.jpeg')
Created Jul 16, 2019
View pca.py
 from sklearn.decomposition import PCA // say you want to reduce to 2 features pca = PCA(n_components = 2) // obtain transformed data data_transformed = pca.fit_transform(data)
Last active Jul 11, 2019
CreditCardFraudDetection.ipynb
View creditcardfrauddetection.ipynb 