anomaly5n.py

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
     pi = math.pi
     det = np.linalg.det(sigma2)
     inv = np.linalg.inv(sigma2)
     val = np.reshape((-0.5)*np.sum(np.multiply((X@inv),X), 1),(np.size(X, 0), 1))
     #print(val.shape)
     p = np.power(2*pi, -n/2)*np.power(det, -0.5)*np.exp(val)
     
     return p