factor-analysis 0.0.2

Package to conduct factor analysis on data

Factor Analysis

Your data is factorized into latent variables and noise parameters all within the same sample.

m denotes sample length, n denotes number of features for the data sample k denotes number of latent features to be represented for the data sample

λ denotes the factor z denotes the latent variable of size m x k ϵ denotes the noise parameters of size m x ``n ψ` denotes the covariance of `ϵ`

Factor Analysis equation

            x = μ + λz + ϵ

We determine λ and ψ using posterior distribution ( z | x ) by expectation maximisation. The method is useful to predict the factor variables from a posterior distribution known to the user provided the data you are processing can be fit into the equation.

import tensorflow as tf

f = factor_analysis.factors.Factor(data, factor_analysis.posterior.Posterior(covariance_prior, means))

noise = factor_analysis.noise.Noise(f, f.posterior)

with tf.Session() as sess:
    print(f.create_factor().eval())
    print(noise.create_noise(f.create_factor()).eval())