def reshape(self, new_shape):
"""
Reshapes current matrix.
Overwrites the current matrix with a new matrix of the
given shape!
Args:
shape: length 2 tuple or pair of integers
Raises:
ValueError: if shape is not an integer pair or
if new shape is inconsistent with the total
size of the current matrix.
"""
if not isinstance(new_shape, tuple) or len(new_shape) != 2:
raise ValueError("shape must be integer pair")
no_rows, no_cols = self.mat.shape
new_no_rows, new_no_cols = new_shape
if not is_integer(new_no_rows) or not is_integer(new_no_cols):
raise ValueError("shape must be integer pair")
if no_rows * no_cols != new_no_rows * new_no_cols:
raise ValueError("total size of new matrix must be unchanged.")
# TODO: change here if we want a copy!!
mat = self.mat.tocoo(copy=False)
# upcast mat.row and mat.col
if no_rows * no_cols >= 2 ** 31 - 1:
linear_pos = np.array(mat.row, dtype=np.int64) * no_cols + mat.col
else:
linear_pos = mat.row * no_cols + mat.col
mat.row = linear_pos // new_no_cols
mat.col = linear_pos - (mat.row * new_no_cols)
# NOTE: change here if we want a copy!!
self.mat = csr_matrix((mat.data, (mat.row, mat.col)), shape=new_shape)
def set_min_samples(self, min_samples):
if not is_integer(min_samples):
raise ValueError("expected %s min_samples value, received %s"
% ("integer", type(min_samples)))
self._MIN_SAMPLES = min_samples
def set_min_samples(self, min_samples):
if not is_integer(min_samples):
raise ValueError("expected %s min_samples value, received %s" %
("integer", type(min_samples)))
self._MIN_SAMPLES = min_samples
