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        "author": "Aswin Vijayakumar",
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        "description": "Factor Analysis\n===============\n\nYour data is factorized into latent variables and noise parameters all within the same sample. \n\n`m` denotes sample length, \n`n` denotes number of features for the data sample\n`k` denotes number of latent features to be represented for the data sample\n\n`\u03bb` denotes the factor\n`z` denotes the latent variable of size `m` x `k`\n`\u03f5` denotes the noise parameters of size `m` x ``n`\n`\u03c8` denotes the covariance of `\u03f5`\n\nFactor Analysis equation\n------------------------\n\n                x = \u03bc + \u03bbz + \u03f5\n\nWe determine `\u03bb` and `\u03c8` 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.\n\n```python\n\nimport tensorflow as tf\n\nf = factor_analysis.factors.Factor(data, factor_analysis.posterior.Posterior(covariance_prior, means))\n\nnoise = factor_analysis.noise.Noise(f, f.posterior)\n\nwith tf.Session() as sess:\n    print(f.create_factor().eval())\n    print(noise.create_noise(f.create_factor()).eval())\n```\n\n\n\n",
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