Probabilistic PCA¶

TODO: description…

TODO: math

TODO: diagram

import probflow as pf
import tensorflow as tf

class PPCA(pf.Model):

    def __init__(self, d, q):
        self.W = pf.Parameter(shape=[d, q])
        self.sigma = pf.ScaleParameter()

    def __call__(self):
        W = self.W()
        cov = W @ tf.transpose(W) + self.sigma()*tf.eye(W.shape[0])
        return pf.MultivariateNormal(tf.zeros(W.shape[0]), cov)

import probflow as pf
import torch

class PPCA(pf.Model):

    def __init__(self, d, q):
        self.W = pf.Parameter(shape=[d, q])
        self.sigma = pf.ScaleParameter()

    def __call__(self):
        W = self.W()
        cov = W @ torch.t(W) + self.sigma()*torch.eye(W.shape[0])
        return pf.MultivariateNormal(torch.zeros(W.shape[0]), cov)