Package mdp :: Package utils :: Class CrossCovarianceMatrix

Class CrossCovarianceMatrix

Instance Methods

_init_internals(self, x, y)
Init the internal structures.

fix(self)
Returns a triple containing the covariance matrix, the average and the number of observations. The covariance matrix is then reset to a zero-state.

update(self, x, y)
Update internal structures.

Inherited from unreachable.newobject: __long__, __native__, __nonzero__, __unicode__, next

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __repr__, __setattr__, __sizeof__, __str__, __subclasshook__

Inherited from CovarianceMatrix

__init__(self, dtype=None, bias=False)
If dtype is not defined, it will be inherited from the first data bunch received by 'update'. All the matrices in this class are set up with the given dtype and no upcast is possible. If bias is True, the covariance matrix is normalized by dividing by T instead of the usual T-1.

Properties

[hide private]

Inherited from object: __class__

Method Details

[hide private]

_init_internals(self, x, y)

Init the internal structures.

The reason this is not done in the constructor is that we want to be
able to derive the input dimension and the dtype directly from the
data this class receives.

Overrides: CovarianceMatrix._init_internals: (inherited documentation)

fix(self)

Returns a triple containing the covariance matrix, the average and the number of observations. The covariance matrix is then reset to a zero-state.

If center is false, the returned matrix is the matrix of the second moments, i.e. the covariance matrix of the data without subtracting the mean.

Overrides: CovarianceMatrix.fix: (inherited documentation)

update(self, x, y)

Update internal structures.

Note that no consistency checks are performed on the data (this is typically done in the enclosing node).

Overrides: CovarianceMatrix.update: (inherited documentation)