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Look out, working on exciting things :)

Khyati Mahendru KhyatiMahendru

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Look out, working on exciting things :)
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View for-jason-kabi.ipynb
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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'))
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)
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:
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KhyatiMahendru / creditcardfrauddetection.ipynb
Last active May 11, 2020
CreditCardFraudDetection.ipynb
View creditcardfrauddetection.ipynb
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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
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
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:")
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)
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)