Class PCANode

The number of principal components to be kept can be specified as
'output_dim' directly (e.g. 'output_dim=10' means 10 components
are kept) or by the fraction of variance to be explained
(e.g. 'output_dim=0.95' means that as many components as necessary
will be kept in order to explain 95% of the input variance).

Other Keyword Arguments:

svd -- if True use Singular Value Decomposition instead of the
       standard eigenvalue problem solver. Use it when PCANode
       complains about singular covariance matrices

reduce -- Keep only those principal components which have a variance
          larger than 'var_abs' and a variance relative to the
          first principal component larger than 'var_rel' and a
          variance relative to total variance larger than 'var_part'
          (set var_part to None or 0 for no filtering).
          Note: when the 'reduce' switch is enabled, the actual number
          of principal components (self.output_dim) may be different
          from that set when creating the instance.

Overrides: object.__init__

Project the input on the first 'n' principal components.
If 'n' is not set, use all available components.

Overrides: Node.execute

Return the fraction of the original variance that can be
explained by self._output_dim PCA components.
If for example output_dim has been set to 0.95, the explained
variance could be something like 0.958...
Note that if output_dim was explicitly set to be a fixed number
of components, there is no way to calculate the explained variance.

Return the projection matrix.

Return the back-projection matrix (i.e. the reconstruction matrix).
        

Project 'y' to the input space using the first 'n' components.
If 'n' is not set, use all available components.

Overrides: Node.inverse

Stop the training phase.

Keyword arguments:

debug=True     if stop_training fails because of singular cov
               matrices, the singular matrices itselves are stored in
               self.cov_mtx and self.dcov_mtx to be examined.

Overrides: Node.stop_training

Update the internal structures according to the input data `x`.

`x` is a matrix having different variables on different columns
and observations on the rows.

By default, subclasses should overwrite `_train` to implement their
training phase. The docstring of the `_train` method overwrites this
docstring.

Note: a subclass supporting multiple training phases should implement
the *same* signature for all the training phases and document the
meaning of the arguments in the `_train` method doc-string. Having
consistent signatures is a requirement to use the node in a flow.

Overrides: Node.train