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Python sparse.tocsr方法代码示例

本文整理汇总了Python中scipy.sparse.tocsr方法的典型用法代码示例。如果您正苦于以下问题:Python sparse.tocsr方法的具体用法?Python sparse.tocsr怎么用?Python sparse.tocsr使用的例子?那么恭喜您, 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模块scipy.sparse的用法示例。

在下文中一共展示了sparse.tocsr方法的7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,您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: scipy2sparse

# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import tocsr [as 别名]
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] 
开发者ID:largelymfs,项目名称:topical_word_embeddings,代码行数:7,代码来源:matutils.py


示例2: __init__

# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import tocsr [as 别名]
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()) 
开发者ID:largelymfs,项目名称:topical_word_embeddings,代码行数:7,代码来源:matutils.py


示例3: unitvec

# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import tocsr [as 别名]
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") 
开发者ID:largelymfs,项目名称:topical_word_embeddings,代码行数:40,代码来源:matutils.py


示例4: __init__

# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import tocsr [as 别名]
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()) 
开发者ID:masr,项目名称:pynlpini,代码行数:7,代码来源:matutils.py


示例5: unitvec

# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import tocsr [as 别名]
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") 
开发者ID:masr,项目名称:pynlpini,代码行数:40,代码来源:matutils.py


示例6: scipy2sparse

# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import tocsr [as 别名]
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] 
开发者ID:mims-harvard,项目名称:ohmnet,代码行数:7,代码来源:matutils.py


示例7: unitvec

# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import tocsr [as 别名]
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") 
开发者ID:mims-harvard,项目名称:ohmnet,代码行数:47,代码来源:matutils.py



注:本文中的scipy.sparse.tocsr方法示例整理自Github/MSDocs等源码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。