Metadata-Version: 2.1
Name: factor-analysis
Version: 0.0.2
Summary: Package to conduct factor analysis on data
Home-page: https://github.com/avkpy/factor-analysis
Author: Aswin Vijayakumar
Author-email: aswinkv28@gmail.com
License: UNKNOWN
Description: 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.
        
        ```python
        
        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())
        ```
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
