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Interface Summary
Interface
Description
Model
Model.Trainer
This represents a training algorithm for a Model
Trainer is responsible for counting calls to accumulate()
between flushes

Class Summary
Class
Description
AlignedGaussian
Differential scaler: scales and offsets each element of a vector
independently aiming to match a given prior model.

BatchedTrainer
Manages a Model.Trainer to do batched learning.

Covariance
Collect covariance statistics assuming input is zero mean.

DiffScaler
Differential scaler: scales and offsets each element of a vector
independently aiming to match a given prior model.

GaussianStats
Collect statistics about a vector: accumulates
sums and sums of products, then computes mean
and covariance.

GaussianStatsOnline
Similar to GaussianStats:
Collect statistics about a vector: accumulates
sums and sums of products, then computes mean
and covariance.

GeneralisedExponential
ICA
ICAScalerSync
This is a task which subsumes a post-scaling into an ICA weight matrix

ICAWithScaler
IIDPrior
Non adaptive model of vector in which each component is
independent of the others and all are identically distributed
according to a given prior

JointHistogramBase
MatrixTrainer
Handles batched delta updates to a matrix.

Mixture
MOGModel
MOGVector
NoisyICA
Scaler
Automatic gain control for a given input vector.

SignalHistogram
SmoothGeneralisedExponential
Non-adaptive generalised exponential factorial prior: the pointy
bit of the usual GeneralisedExponential has been smoothed out
by blending with a quadratic.

SparseICA
VarianceICA

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