KhyatiMahendru

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

KhyatiMahendru

KhyatiMahendru

def update_weights_BCE(m1, m2, b, X1, X2, Y, learning_rate):
    m1_deriv = 0
    m2_deriv = 0
    b_deriv = 0
    N = len(X1)
    for i in range(N):
        s = 1 / (1 / (1 + math.exp(-m1*X1[i] - m2*X2[i] - b)))
        
        # Calculate partial derivatives
        m1_deriv += -X1[i] * (s - Y[i])

KhyatiMahendru

# importing requirements
from keras.layers import Dense
from keras.models import Sequential
from keras.optimizers import adam

# alpha = 0.001 as given in the lr parameter in adam() optimizer

# build the model
model_alpha1 = Sequential()
model_alpha1.add(Dense(50, input_dim=2, activation='relu'))

KhyatiMahendru

KhyatiMahendru

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'))

KhyatiMahendru

# 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)

KhyatiMahendru

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:

KhyatiMahendru

# 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

KhyatiMahendru

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:")