CODEWITHSUNDEEP
pythonnumpymachine-learning
frylock405
frylock405

Reputation: 31

Calculating mean vector for multivariate normal distribution python

I am having trouble fitting a multivariate gaussian distribution to my dataset, more specifically, finding a mean vector (or multiple mean vectors). My dataset is an N x 8 matrix and currently I am using this code:

muVector = np.mean(Xtrain, axis=0) where Xtrain is my training data set.

For the covariance I am building it using a arbitrary variance value (.5) and doing:

covariance = np.dot(.5, np.eye(N,N) where N is the number of observations.

But when I construct my Phi matrix, I am getting all zeros. Here is my code:

muVector = np.mean(Xtrain, axis=0)
# get covariance matrix from Xtrain
cov = np.dot(var, np.eye(N,N))
cov = np.linalg.inv(cov)  

# build Xtrain Phi
Phi = np.ones((N,M))
for row in range(N):
  temp = Xtrain[row,:] - muVector
  temp.shape = (1,M)
  temp = np.dot((-.5), temp)
  temp = np.dot(temp, cov)
  temp = np.dot(temp, (Xtrain[row,:] - muVector))
  Phi[row,:] = np.exp(temp)

Any help is appreciated. I think I might have to use np.random.multivariate_normal()? But I do not know how to use it in this case.

Upvotes: 3

Views: 9978

Answers (1)

bjbschmitt
bjbschmitt

Reputation: 124

By "Phi" I believe that you mean the probability density function (pdf) that you want to estimate. In this case, the covariance matrix should be MxM and the output Phi will be Nx1:

# -*- coding: utf-8 -*-

import numpy as np

N = 1024
M = 8
var = 0.5

# Creating a Xtrain NxM observation matrix.
# Its muVector is [0, 1, 2, 3, 4, 5, 6, 7] and the variance for all
# independent random variables is 0.5.
Xtrain = np.random.multivariate_normal(np.arange(8), np.eye(8,8)*var, N)

# Estimating the mean vector.
muVector = np.mean(Xtrain, axis=0)

# Creating the estimated covariance matrix and its inverse.
cov = np.eye(M,M)*var
inv_cov = np.linalg.inv(cov)  

# Normalization factor from the pdf.
norm_factor = 1/np.sqrt((2*np.pi)**M * np.linalg.det(cov))

# Estimating the pdf.
Phi = np.ones((N,1))
for row in range(N):
    temp = Xtrain[row,:] - muVector
    temp.shape = (1,M)
    temp = np.dot(-0.5*temp, inv_cov)
    temp = np.dot(temp, (Xtrain[row,:] - muVector))
    Phi[row] = norm_factor*np.exp(temp)

Alternatively, you can use the pdf method from scipy.stats.multivariate_normal:

# -*- coding: utf-8 -*-

import numpy as np
from scipy.stats import multivariate_normal

N = 1024
M = 8
var = 0.5

# Creating a Xtrain NxM observation matrix.
# Its muVector is [0, 1, 2, 3, 4, 5, 6, 7] and the variance for all
# independent random variables is 0.5.
Xtrain = np.random.multivariate_normal(np.arange(8), np.eye(8,8)*var, N)

# Estimating the mean vector.
muVector = np.mean(Xtrain, axis=0)

# Creating the estimated covariance matrix.
cov = np.eye(M,M)*var

Phi2 = multivariate_normal.pdf(Xtrain, mean=muVector, cov=cov)

Both Phi and Phi2 output arrays will be equal.

Upvotes: 3

Related Questions