def _reconstruct_similarity(self, post_normalize=True, force=True):
if not self.get_matrix_similarity() or force:
self._matrix_similarity = SimilarityMatrix()
self._matrix_similarity.create(self._U,
self._S,
post_normalize=post_normalize)
return self._matrix_similarity
def __init__(self, filename=None):
#Call parent constructor
super(SVD, self).__init__()
# self._U: Eigen vector. Relates the concepts of the input matrix to the principal axes
# self._S (or \Sigma): Singular -or eigen- values. It represents the strength of each eigenvector.
# self._V: Eigen vector. Relates features to the principal axes
self._U, self._S, self._V = (None, None, None)
# Mean centered Matrix: row and col shifts
self._shifts = None
# self._matrix_reconstructed: M' = U S V^t
self._matrix_reconstructed = None
# Similarity matrix: (U \Sigma)(U \Sigma)^T = U \Sigma^2 U^T
# U \Sigma is concept_axes weighted by axis_weights.
self._matrix_similarity = SimilarityMatrix()
if filename:
self.load_model(filename)
# Row and Col ids. Only when importing from SVDLIBC
self._file_row_ids = None
self._file_col_ids = None
#Update feature
self._foldinZeroes = {}
self.inv_S = None #since it doesn't get updated so redundent to calculate each time
def __init__(self, filename=None):
#Call parent constructor
super(SVD, self).__init__()
# self._U: Eigen vector. Relates the concepts of the input matrix to the principal axes
# self._S (or \Sigma): Singular -or eigen- values. It represents the strength of each eigenvector.
# self._V: Eigen vector. Relates features to the principal axes
self._U, self._S, self._V = (None, None, None)
# Mean centered Matrix: row and col shifts
self._shifts = None
# self._matrix_reconstructed: M' = U S V^t
self._matrix_reconstructed = None
# Similarity matrix: (U \Sigma)(U \Sigma)^T = U \Sigma^2 U^T
# U \Sigma is concept_axes weighted by axis_weights.
self._matrix_similarity = SimilarityMatrix()
if filename:
self.load_model(filename)
