def _finalizeSVD(self, numSVDDims=None):
if numSVDDims is not None:<EOL><INDENT>self.numSVDDims = numSVDDims<EOL><DEDENT>if self.numSVDDims=="<STR_LIT>":<EOL><INDENT>if self.fractionOfMax is not None:<EOL><INDENT>self.numSVDDims = self.getAdaptiveSVDDims(self._s, self.fractionOfMax)<EOL><DEDENT>else:<EOL><INDENT>self.numSVDDims = self.getAdaptiveSVDDims(self._s)<EOL><DEDENT><DEDENT>if self._vt.shape[<NUM_LIT:0>] < self.numSVDDims:<EOL><INDENT>print("<STR_LIT>")<EOL>print ("<STR_LIT>"<EOL>"<STR_LIT>")<EOL>print("<STR_LIT>", self._vt.shape[<NUM_LIT:0>])<EOL>print("<STR_LIT>")<EOL>self.numSVDDims = self._vt.shape[<NUM_LIT:0>]<EOL><DEDENT>self._vt = self._vt[:self.numSVDDims]<EOL>if len(self._vt) == <NUM_LIT:0>:<EOL><INDENT>return<EOL><DEDENT>self._Memory = numpy.zeros((self._numPatterns,self.numSVDDims))<EOL>self._M = self._Memory<EOL>self.useSparseMemory = False<EOL>for i in range(self._numPatterns):<EOL><INDENT>self._Memory[i] = numpy.dot(self._vt, self._a[i])<EOL><DEDENT>self._a = None<EOL>
Called by finalizeLearning(). This will project all the patterns onto the
SVD eigenvectors.
:param numSVDDims: (int) number of egeinvectors used for projection.
:return: