コード例 #1
0
def hash_from_ndarray(data):
    """Return a hash from an ndarray

    It takes care of the data, shapes, strides and dtype.

    """
    # We need to hash the shapes and strides as hash_from_code only hashes
    # the data buffer. Otherwise, this will cause problem with shapes like:
    # (1, 0) and (2, 0) and problem with inplace transpose.
    # We also need to add the dtype to make the distinction between
    # uint32 and int32 of zeros with the same shape and strides.

    # python hash are not strong, so I always use md5 in order not to have a
    # too long hash, I call it again on the concatenation of all parts.
    if not data.flags["C_CONTIGUOUS"]:
        # Version 1.7.1 and previous of NumPy allowed calling
        # hash_from_code on an F-contiguous array, but more recent
        # versions need a C-contiguous one.
        data = numpy.ascontiguousarray(data)
    return hash_from_code(
        hash_from_code(data)
        + hash_from_code(str(data.shape))
        + hash_from_code(str(data.strides))
        + hash_from_code(str(data.dtype))
    )
コード例 #2
0
ファイル: utils.py プロジェクト: jsalvatier/Theano-1
def hash_from_ndarray(data):
    # We need to hash the shapes and strides as hash_from_code only hashes
    # the data buffer. Otherwise, this will cause problem with shapes like:
    # (1, 0) and (2, 0) and problem with inplace transpose.
    # We also need to add the dtype to make the distinction between
    # uint32 and int32 of zeros with the same shape and strides.

    # python hash are not strong, so I always use md5 in order not to have a
    # too long hash, I call it again on the concatenation of all parts.
    if not data.flags["C_CONTIGUOUS"] and not data.flags["F_CONTIGUOUS"]:
        data = numpy.ascontiguousarray(data)
    return hash_from_code(
        hash_from_code(data) + hash_from_code(str(data.shape)) +
        hash_from_code(str(data.strides)) + hash_from_code(str(data.dtype)))
コード例 #3
0
ファイル: utils.py プロジェクト: NicolasBouchard/Theano
def hash_from_ndarray(data):
    # We need to hash the shapes and strides as hash_from_code only hashes
    # the data buffer. Otherwise, this will cause problem with shapes like:
    # (1, 0) and (2, 0) and problem with inplace transpose.
    # We also need to add the dtype to make the distinction between
    # uint32 and int32 of zeros with the same shape and strides.

    # python hash are not strong, so I always use md5 in order not to have a
    # too long hash, I call it again on the concatenation of all parts.
    if not data.flags["C_CONTIGUOUS"] and not data.flags["F_CONTIGUOUS"]:
        data = numpy.ascontiguousarray(data)
    return hash_from_code(hash_from_code(data) +
                          hash_from_code(str(data.shape)) +
                          hash_from_code(str(data.strides)) +
                          hash_from_code(str(data.dtype)))
コード例 #4
0
ファイル: utils.py プロジェクト: sdmassey27/Theano
def hash_from_ndarray(data):
    """Return a hash from an ndarray

    It takes care of the data, shapes, strides and dtype.

    """
    # We need to hash the shapes and strides as hash_from_code only hashes
    # the data buffer. Otherwise, this will cause problem with shapes like:
    # (1, 0) and (2, 0) and problem with inplace transpose.
    # We also need to add the dtype to make the distinction between
    # uint32 and int32 of zeros with the same shape and strides.

    # python hash are not strong, so I always use md5 in order not to have a
    # too long hash, I call it again on the concatenation of all parts.
    if not data.flags["C_CONTIGUOUS"]:
        # Version 1.7.1 and previous of NumPy allowed calling
        # hash_from_code on an F-contiguous array, but more recent
        # versions need a C-contiguous one.
        data = numpy.ascontiguousarray(data)
    return hash_from_code(
        hash_from_code(data) + hash_from_code(str(data.shape)) +
        hash_from_code(str(data.strides)) + hash_from_code(str(data.dtype)))
コード例 #5
0
def hash_from_sparse(data):
    # We need to hash the shapes as hash_from_code only hashes
    # the data buffer. Otherwise, this will cause problem with shapes like:
    # (1, 0) and (2, 0)
    # We also need to add the dtype to make the distinction between
    # uint32 and int32 of zeros with the same shape.

    # Python hash is not strong, so I always use md5. To avoid having a too
    # long hash, I call it again on the contatenation of all parts.
    return hash_from_code(hash_from_code(data.data) +
                          hash_from_code(data.indices) +
                          hash_from_code(data.indptr) +
                          hash_from_code(str(data.shape)) +
                          hash_from_code(str(data.dtype)) +
                          hash_from_code(data.format))