本文整理汇总了Python中scipy.sparse.data方法的典型用法代码示例。如果您正苦于以下问题：Python sparse.data方法的具体用法？Python sparse.data怎么用？Python sparse.data使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类scipy.sparse的用法示例。
在下文中一共展示了sparse.data方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: chunkize_serial
# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import data [as 别名]
def chunkize_serial(iterable, chunksize, as_numpy=False):
"""
Return elements from the iterable in `chunksize`-ed lists. The last returned
element may be smaller (if length of collection is not divisible by `chunksize`).
>>> print(list(grouper(range(10), 3)))
[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
"""
import numpy
it = iter(iterable)
while True:
if as_numpy:
# convert each document to a 2d numpy array (~6x faster when transmitting
# chunk data over the wire, in Pyro)
wrapped_chunk = [[numpy.array(doc) for doc in itertools.islice(it, int(chunksize))]]
else:
wrapped_chunk = [list(itertools.islice(it, int(chunksize)))]
if not wrapped_chunk[0]:
break
# memory opt: wrap the chunk and then pop(), to avoid leaving behind a dangling reference
yield wrapped_chunk.pop() 示例2: load
# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import data [as 别名]
def load(cls, fname, mmap=None):
"""
Load a previously saved object from file (also see `save`).
If the object was saved with large arrays stored separately, you can load
these arrays via mmap (shared memory) using `mmap='r'`. Default: don't use
mmap, load large arrays as normal objects.
"""
logger.info("loading %s object from %s" % (cls.__name__, fname))
subname = lambda suffix: fname + '.' + suffix + '.npy'
obj = unpickle(fname)
for attrib in getattr(obj, '__numpys', []):
logger.info("loading %s from %s with mmap=%s" % (attrib, subname(attrib), mmap))
setattr(obj, attrib, numpy.load(subname(attrib), mmap_mode=mmap))
for attrib in getattr(obj, '__scipys', []):
logger.info("loading %s from %s with mmap=%s" % (attrib, subname(attrib), mmap))
sparse = unpickle(subname(attrib))
sparse.data = numpy.load(subname(attrib) + '.data.npy', mmap_mode=mmap)
sparse.indptr = numpy.load(subname(attrib) + '.indptr.npy', mmap_mode=mmap)
sparse.indices = numpy.load(subname(attrib) + '.indices.npy', mmap_mode=mmap)
setattr(obj, attrib, sparse)
for attrib in getattr(obj, '__ignoreds', []):
logger.info("setting ignored attribute %s to None" % (attrib))
setattr(obj, attrib, None)
return obj 示例3: load
# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import data [as 别名]
def load(cls, fname, mmap=None):
"""
Load a previously saved object from file (also see `save`).
If the object was saved with large arrays stored separately, you can load
these arrays via mmap (shared memory) using `mmap='r'`. Default: don't use
mmap, load large arrays as normal objects.
"""
#logger.info("loading %s object from %s" % (cls.__name__, fname))
subname = lambda suffix: fname + '.' + suffix + '.npy'
obj = unpickle(fname)
for attrib in getattr(obj, '__numpys', []):
logger.info("loading %s from %s with mmap=%s" % (attrib, subname(attrib), mmap))
setattr(obj, attrib, numpy.load(subname(attrib), mmap_mode=mmap))
for attrib in getattr(obj, '__scipys', []):
logger.info("loading %s from %s with mmap=%s" % (attrib, subname(attrib), mmap))
sparse = unpickle(subname(attrib))
sparse.data = numpy.load(subname(attrib) + '.data.npy', mmap_mode=mmap)
sparse.indptr = numpy.load(subname(attrib) + '.indptr.npy', mmap_mode=mmap)
sparse.indices = numpy.load(subname(attrib) + '.indices.npy', mmap_mode=mmap)
setattr(obj, attrib, sparse)
for attrib in getattr(obj, '__ignoreds', []):
#logger.info("setting ignored attribute %s to None" % (attrib))
setattr(obj, attrib, None)
return obj 示例4: _load_specials
# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import data [as 别名]
def _load_specials(self, fname, mmap, compress, subname):
"""
Loads any attributes that were stored specially, and gives the same
opportunity to recursively included SaveLoad instances.
"""
mmap_error = lambda x, y: IOError(
'Cannot mmap compressed object %s in file %s. ' % (x, y) +
'Use `load(fname, mmap=None)` or uncompress files manually.')
for attrib in getattr(self, '__recursive_saveloads', []):
cfname = '.'.join((fname, attrib))
logger.info("loading %s recursively from %s.* with mmap=%s" % (
attrib, cfname, mmap))
getattr(self, attrib)._load_specials(cfname, mmap, compress, subname)
for attrib in getattr(self, '__numpys', []):
logger.info("loading %s from %s with mmap=%s" % (
attrib, subname(fname, attrib), mmap))
if compress:
if mmap:
raise mmap_error(attrib, subname(fname, attrib))
val = numpy.load(subname(fname, attrib))['val']
else:
val = numpy.load(subname(fname, attrib), mmap_mode=mmap)
setattr(self, attrib, val)
for attrib in getattr(self, '__scipys', []):
logger.info("loading %s from %s with mmap=%s" % (
attrib, subname(fname, attrib), mmap))
sparse = unpickle(subname(fname, attrib))
if compress:
if mmap:
raise mmap_error(attrib, subname(fname, attrib))
with numpy.load(subname(fname, attrib, 'sparse')) as f:
sparse.data = f['data']
sparse.indptr = f['indptr']
sparse.indices = f['indices']
else:
sparse.data = numpy.load(subname(fname, attrib, 'data'), mmap_mode=mmap)
sparse.indptr = numpy.load(subname(fname, attrib, 'indptr'), mmap_mode=mmap)
sparse.indices = numpy.load(subname(fname, attrib, 'indices'), mmap_mode=mmap)
setattr(self, attrib, sparse)
for attrib in getattr(self, '__ignoreds', []):
logger.info("setting ignored attribute %s to None" % (attrib))
setattr(self, attrib, None)