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)