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Look out, working on exciting things :)

# Khyati Mahendru KhyatiMahendru

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Look out, working on exciting things :)
Last active Aug 12, 2020
For Jason Kabi.ipynb
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Created Jun 17, 2019
View silhouette_score.py
 from sklearn.metrics import silhouette_score sil = [] kmax = 10 # dissimilarity would not be defined for a single cluster, thus, minimum number of clusters should be 2 for k in range(2, kmax+1): kmeans = KMeans(n_clusters = k).fit(x) labels = kmeans.labels_ sil.append(silhouette_score(x, labels, metric = 'euclidean'))
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 Jun 3, 2019
View weight_update_Huber.py
 def update_weights_Huber(m, b, X, Y, delta, learning_rate): m_deriv = 0 b_deriv = 0 N = len(X) for i in range(N): # derivative of quadratic for small values and of linear for large values if abs(Y[i] - m*X[i] - b) <= delta: m_deriv += -X[i] * (Y[i] - (m*X[i] + b)) b_deriv += - (Y[i] - (m*X[i] + b)) else:
Last active May 11, 2020
CreditCardFraudDetection.ipynb
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Created Jun 17, 2019
View elbow_method.py
 from sklearn.cluster import KMeans # function returns WSS score for k values from 1 to kmax def calculate_WSS(points, kmax): sse = [] for k in range(1, kmax+1): kmeans = KMeans(n_clusters = k).fit(points) centroids = kmeans.cluster_centers_ pred_clusters = kmeans.predict(points) curr_sse = 0
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)