abhishek-Kumar009

import java.util.Scanner;

public class uncleJohny {

	public static void main(String[] args) {
		Scanner sc = new Scanner(System.in);
		int T = sc.nextInt(); // Number of test Cases
		int i, j, l;

abhishek-Kumar009

def multivariateGaussian(X, mu, sigma2):
     n = np.size(sigma2, 1)
     m = np.size(sigma2, 0)
     #print(m,n)
     
     if n == 1 or m == 1:
        # print('Yes!')
         sigma2 = np.diag(sigma2[0, :])
     #print(sigma2)
     X = X - mu

abhishek-Kumar009

abhishek-Kumar009

import numpy as np
import matplotlib.pyplot as plt
import scipy.io as sio
import math

abhishek-Kumar009

dataset = sio.loadmat('anomalyData.mat')
X = dataset['X']
Xval = dataset['Xval']
yval = dataset['yval']

abhishek-Kumar009

plt.scatter(X[:, 0], X[:, 1], marker = "x")
plt.xlabel('Latency(ms)')
plt.ylabel('Throughput(mb/s)')

abhishek-Kumar009

def estimateGaussian(X):
    n = np.size(X, 1)
    m = np.size(X, 0)
    mu = np.zeros((n, 1))
    sigma2 = np.zeros((n, 1))
    
    mu = np.reshape((1/m)*np.sum(X, 0), (1, n))
    sigma2 = np.reshape((1/m)*np.sum(np.power((X - mu),2), 0),(1, n))
    
    return mu, sigma2

abhishek-Kumar009

def multivariateGaussian(X, mu, sigma2):
     n = np.size(sigma2, 1)
     m = np.size(sigma2, 0)
     #print(m,n)
     
     if n == 1 or m == 1:
        # print('Yes!')
         sigma2 = np.diag(sigma2[0, :])
     #print(sigma2)
     X = X - mu

abhishek-Kumar009

def selectThreshHold(yval, pval):
    
    F1 = 0
    bestF1 = 0
    bestEpsilon = 0
    
    stepsize = (np.max(pval) - np.min(pval))/1000
        
    epsVec = np.arange(np.min(pval), np.max(pval), stepsize)
    noe = len(epsVec)

abhishek-Kumar009

def findIndices(binVec):
    l = []
    for i in range(len(binVec)):
        if binVec[i] == 1:
            l.append(i)
    return l