Source code for botmpy.nodes.prewhiten

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# Copyright (c) 2012-2013, Berlin Institute of Technology
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# Developed by:	Philipp Meier <pmeier82@gmail.com>
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#               Neural Information Processing Group (NI)
#               School for Electrical Engineering and Computer Science
#               Berlin Institute of Technology
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# Acknowledgements:
#   Philipp Meier <pmeier82@gmail.com>
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# Changelog:
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"""spike noise prewhitening algorithm"""

__docformat__ = 'restructuredtext'
__all__ = ['PrewhiteningNode', 'PrewhiteningNode2']

##  IMPORTS

import scipy as sp
from scipy import linalg as sp_la
from .base_nodes import Node
from ..common import coloured_loading, mad_scaling, TimeSeriesCovE

##  CLASSES

[docs]class PrewhiteningNode(Node): """prewhitens the data with respect to a noise covariance matrix""" ## constructor def __init__(self, ncov=None, dtype=sp.float32): """ :Parameters: ncov : ndarray The noise covariance matrix or None """ # super super(PrewhiteningNode, self).__init__(dtype=dtype) # members self._ncov = None self._chol_ncov = None self._inv_chol_ncov = None self._is_ready = False # build if ncov is not None: self.update(ncov) ## privates
[docs] def update(self, ncov): """updates the covariance matrix and recalculates internals :Parameters: ncov : ndarray symetric matrix, noise covariance """ # checks if ncov.ndim != 2 or ncov.shape[0] != ncov.shape[1]: raise ValueError('noise covariance is not a symmetric, ' 'pos. definite matrix') # inits self.input_dim = ncov.shape[0] self._ncov = ncov self._chol_ncov = None self._inv_chol_ncov = None # compute cholesky decomposition try: self._chol_ncov = sp_la.cholesky(self._ncov) except: self._ncov = coloured_loading(self._ncov, 50) self._chol_ncov = sp_la.cholesky(self._ncov) # invert self._inv_chol_ncov = sp_la.inv(self._chol_ncov) # set ready flag self._is_ready = True ## node implementation
@staticmethod
[docs] def is_invertible(): return False
@staticmethod
[docs] def is_trainable(): return False
def _execute(self, x, ncov=None): # check for update if ncov is not None: self.update(ncov) # ready check if self._is_ready is False: raise RuntimeError('Node not initialised yet!') # return prewhitened data return sp.dot(x, self._inv_chol_ncov).astype(self.dtype)
[docs]class PrewhiteningNode2(Node): """pre-whitens data with respect to a noise covariance matrix""" ## constructor def __init__(self, covest): """ :type covest: TimeSeriesCovE :param covest: noise covariance estimator """ # checks if not isinstance(covest, TimeSeriesCovE): raise TypeError('expecting instance of TimeSeriesCovE!') # super super(PrewhiteningNode2, self).__init__(dtype=covest.dtype) # members self._covest = covest ## node implementation @staticmethod
[docs] def is_invertible(): return False
@staticmethod
[docs] def is_trainable(): return False
def _execute(self, x): if self._covest.is_initialised is False: raise RuntimeError('Node not initialised yet!') # return prewhitened data tf = self.input_dim / self._covest.nc rval = sp.dot(x, self._covest.get_whitening_op(tf=tf)) return rval.astype(self.dtype)
class MADScalingNode(Node): """scales input data""" ## constructor def __init__(self, covest): """ :type covest: TimeSeriesCovE :param covest: noise covariance estimator """ # checks if not isinstance(covest, TimeSeriesCovE): raise TypeError('expecting instance of TimeSeriesCovE!') # super super(MADScalingNode, self).__init__(dtype=covest.dtype) # members self._covest = covest ## node implementation @staticmethod def is_invertible(): return False @staticmethod def is_trainable(): return False def _execute(self, x): if self._covest.is_initialised is False: raise RuntimeError('Node not initialised yet!') # return prewhitened data tf = self.input_dim / self._covest.nc rval = sp.dot(x, self._covest.get_whitening_op(tf=tf)) return rval.astype(self.dtype) ## interface def update_scales(self): """update the internal scale""" ## MAIN if __name__ == '__main__': pass

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