本文整理匯總了Python中scipy.sparse.tocsr方法的典型用法代碼示例。如果您正苦於以下問題：Python sparse.tocsr方法的具體用法？Python sparse.tocsr怎麽用？Python sparse.tocsr使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類scipy.sparse的用法示例。
在下文中一共展示了sparse.tocsr方法的7個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: scipy2sparse
# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import tocsr [as 別名]
def scipy2sparse(vec, eps=1e-9):
"""Convert a scipy.sparse vector into gensim document format (=list of 2-tuples)."""
vec = vec.tocsr()
assert vec.shape[0] == 1
return [(int(pos), float(val)) for pos, val in zip(vec.indices, vec.data) if numpy.abs(val) > eps] 示例2: __init__
# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import tocsr [as 別名]
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()) 示例3: unitvec
# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import tocsr [as 別名]
def unitvec(vec):
"""
Scale a vector to unit length. The only exception is the zero vector, which
is returned back unchanged.
Output will be in the same format as input (i.e., gensim vector=>gensim vector,
or numpy array=>numpy array, scipy.sparse=>scipy.sparse).
"""
if scipy.sparse.issparse(vec): # convert scipy.sparse to standard numpy array
vec = vec.tocsr()
veclen = numpy.sqrt(numpy.sum(vec.data ** 2))
if veclen > 0.0:
return vec / veclen
else:
return vec
if isinstance(vec, numpy.ndarray):
vec = numpy.asarray(vec, dtype=float)
veclen = blas_nrm2(vec)
if veclen > 0.0:
return blas_scal(1.0 / veclen, vec)
else:
return vec
try:
first = next(iter(vec)) # is there at least one element?
except:
return vec
if isinstance(first, (tuple, list)) and len(first) == 2: # gensim sparse format?
length = 1.0 * math.sqrt(sum(val ** 2 for _, val in vec))
assert length > 0.0, "sparse documents must not contain any explicit zero entries"
if length != 1.0:
return [(termid, val / length) for termid, val in vec]
else:
return list(vec)
else:
raise ValueError("unknown input type") 示例4: __init__
# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import tocsr [as 別名]
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()) 示例5: unitvec
# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import tocsr [as 別名]
def unitvec(vec):
"""
Scale a vector to unit length. The only exception is the zero vector, which
is returned back unchanged.
Output will be in the same format as input (i.e., gensim vector=>gensim vector,
or numpy array=>numpy array, scipy.sparse=>scipy.sparse).
"""
if scipy.sparse.issparse(vec): # convert scipy.sparse to standard numpy array
vec = vec.tocsr()
veclen = numpy.sqrt(numpy.sum(vec.data ** 2))
if veclen > 0.0:
return vec / veclen
else:
return vec
if isinstance(vec, numpy.ndarray):
vec = numpy.asarray(vec, dtype=float)
veclen = blas_nrm2(vec)
if veclen > 0.0:
return blas_scal(1.0 / veclen, vec)
else:
return vec
try:
first = next(iter(vec)) # is there at least one element?
except:
return vec
if isinstance(first, (tuple, list)) and len(first) == 2: # gensim sparse format?
length = 1.0 * math.sqrt(sum(val ** 2 for _, val in vec))
assert length > 0.0, "sparse documents must not contain any explicit zero entries"
if length != 1.0:
return [(termid, val / length) for termid, val in vec]
else:
return list(vec)
else:
raise ValueError("unknown input type") 示例6: scipy2sparse
# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import tocsr [as 別名]
def scipy2sparse(vec, eps=1e-9):
"""Convert a scipy.sparse vector into document format (=list of 2-tuples)."""
vec = vec.tocsr()
assert vec.shape[0] == 1
return [(int(pos), float(val)) for pos, val in zip(vec.indices, vec.data) if numpy.abs(val) > eps] 示例7: unitvec
# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import tocsr [as 別名]
def unitvec(vec, norm='l2'):
"""
Scale a vector to unit length. The only exception is the zero vector, which
is returned back unchanged.
Output will be in the same format as input.
"""
if norm not in ('l1', 'l2'):
raise ValueError("'%s' is not a supported norm. Currently supported norms are 'l1' and 'l2'." % norm)
if scipy.sparse.issparse(vec):
vec = vec.tocsr()
if norm == 'l1':
veclen = numpy.sum(numpy.abs(vec.data))
if norm == 'l2':
veclen = numpy.sqrt(numpy.sum(vec.data ** 2))
if veclen > 0.0:
return vec / veclen
else:
return vec
if isinstance(vec, numpy.ndarray):
vec = numpy.asarray(vec, dtype=float)
if norm == 'l1':
veclen = numpy.sum(numpy.abs(vec))
if norm == 'l2':
veclen = blas_nrm2(vec)
if veclen > 0.0:
return blas_scal(1.0 / veclen, vec)
else:
return vec
try:
first = next(iter(vec)) # is there at least one element?
except:
return vec
if isinstance(first, (tuple, list)) and len(first) == 2:
if norm == 'l1':
length = float(sum(abs(val) for _, val in vec))
if norm == 'l2':
length = 1.0 * math.sqrt(sum(val ** 2 for _, val in vec))
assert length > 0.0, "sparse documents must not contain any explicit zero entries"
return ret_normalized_vec(vec, length)
else:
raise ValueError("unknown input type")