def __init__(self, sparse, id_order=None):
if not scipy.sparse.issparse(sparse):
raise ValueError("must pass a scipy sparse object")
rows, cols = sparse.shape
if rows != cols:
raise ValueError("Weights object must be square")
self.sparse = sparse.tocsr()
self.n = sparse.shape[0]
if id_order:
if len(id_order) != self.n:
raise ValueError("Number of values in id_order must match shape of sparse")
self.id_order = id_order
self._cache = {}
def __init__(self, sparse, documents_columns=True):
"""
Parameters
----------
sparse : `scipy.sparse`
Corpus scipy sparse format
documents_columns : bool, optional
If True - documents will be column, rows otherwise.
"""
if documents_columns:
self.sparse = sparse.tocsc()
else:
self.sparse = sparse.tocsr().T # make sure shape[1]=number of docs (needed in len())
def __init__(self, sparse, documents_columns=True):
if documents_columns:
self.sparse = sparse.tocsc()
else:
self.sparse = sparse.tocsr().T # make sure shape[1]=number of docs (needed in len())
def __init__(self, sparse, documents_columns=True):
if documents_columns:
self.sparse = sparse.tocsc()
else:
self.sparse = sparse.tocsr(
).T # make sure shape[1]=number of docs (needed in len())