tf.contrib.factorization.gmm

Creates the graph for Gaussian mixture model (GMM) clustering.

tf.contrib.factorization.gmm(
    inp, initial_clusters, num_clusters, random_seed,
    covariance_type=FULL_COVARIANCE, params='wmc'
)

Args
`inp`	An input tensor or list of input tensors
`initial_clusters`	Specifies the clusters used during initialization. Can be a tensor or numpy array, or a function that generates the clusters. Can also be "random" to specify that clusters should be chosen randomly from input data. Note: type is diverse to be consistent with skflow.
`num_clusters`	number of clusters.
`random_seed`	Python integer. Seed for PRNG used to initialize centers.
`covariance_type`	one of "diag", "full".
`params`	Controls which parameters are updated in the training process. Can contain any combination of "w" for weights, "m" for means, and "c" for covars.

Returns
`Note`	tuple of lists returned to be consistent with skflow A tuple consisting of:
`assignments`	A vector (or list of vectors). Each element in the vector corresponds to an input row in 'inp' and specifies the cluster id corresponding to the input.
`training_op`	an op that runs an iteration of training.
`init_op`	an op that runs the initialization.

Args