def pca_fit(self, n_components=None, fit_transform=True):# k=None, kernel=None, extern=False):
"""
Adaptation of Alexis Mignon's pca.py script
Adapted to fit skspec 5/6/2013.
Original credit to Alexis Mignon:
Module for Principal Component Analysis.
Author: Alexis Mignon (c)
Date: 10/01/2012
e-mail: [email protected]
(https://code.google.com/p/pypca/source/browse/trunk/PCA.py)
Constructor arguments:
* k: number of principal components to compute. 'None'
(default) means that all components are computed.
* kernel: perform PCA on kernel matrices (default is False)
* extern: use extern product to perform PCA (default is
False). Use this option when the number of samples
is much smaller than the number of features.
See pca.py constructor for more info.
This will initialize PCA class and fit current values of timespectra.
Notes:
------
The pcakernel.py module is more modular. These class methods
make it easier to perform PCA on a timespectra, but are less
flexible than using the module functions directly.
timespectra gets transposed as PCA module expects rows as
samples and columns as features.
Changes to timespectra do not retrigger PCA refresh. This
method should be called each time changes are made to the data.
"""
# NOW USES DYNSPEC BUT DID NOT TEST BEFORE CHANGING
if self.center:
logger.warn('Builtin PCA will perform mean-centering on'
' data. Data is not mean centered yet.')
self._PCA = PCA(n_components=n_components)
if fit_transform:
return self._PCA.fit_transform(self.dyn_spec)#.transpose())
else:
self._PCA.fit(self.dyn_spec)#.transpose())
