The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.

Example 1

def _compute_extra_terms(Y, W, items_u, trustees_u):
    # Implicit information
    norm_Iu = math.sqrt(len(items_u))

    # TODO: Clean this. Hint: np.nans
    y_term = 0
    if norm_Iu > 0:
        y_sum = np.sum(Y[items_u, :], axis=0)
        y_term = y_sum / norm_Iu

    # Trust information
    w_term = 0
    norm_Tu = math.sqrt(len(trustees_u))
    if norm_Tu > 0:
        w_sum = np.sum(W[trustees_u, :], axis=0)
        w_term = w_sum / norm_Tu

    return y_term, w_term, norm_Iu, norm_Tu 

Example 2

def write_metadata(hdf5_out, dim, metadata_df, convert_back_to_neg_666):
    """
	Writes either column or row metadata to proper node of gctx out (hdf5) file.

	Input:
		- hdf5_out (h5py): open hdf5 file to write to
		- dim (str; must be "row" or "col"): dimension of metadata to write to 
		- metadata_df (pandas DataFrame): metadata DataFrame to write to file 
		- convert_back_to_neg_666 (bool): Whether to convert numpy.nans back to "-666",
				as per CMap metadata null convention 
	"""
    if dim == "col":
        hdf5_out.create_group(col_meta_group_node)
        metadata_node_name = col_meta_group_node
    elif dim == "row":
        hdf5_out.create_group(row_meta_group_node)
        metadata_node_name = row_meta_group_node
    else:
        logger.error("'dim' argument must be either 'row' or 'col'!")

    # write id field to expected node
    hdf5_out.create_dataset(metadata_node_name + "/id", data=[str(x) for x in metadata_df.index])

    metadata_fields = list(metadata_df.columns.copy())

    # if specified, convert numpy.nans in metadata back to -666
    if convert_back_to_neg_666:
        for c in metadata_fields:
            metadata_df[[c]] = metadata_df[[c]].replace([numpy.nan], ["-666"])

    # write metadata columns to their own arrays
    for field in [entry for entry in metadata_fields if entry != "ind"]:
        hdf5_out.create_dataset(metadata_node_name + "/" + field,
                                data=numpy.array(list(metadata_df.loc[:, field]))) 

Example 3

def test_NanGuardMode():
    # Tests if NanGuardMode is working by feeding in numpy.inf and numpy.nans
    # intentionally. A working implementation should be able to capture all
    # the abnormalties.
    x = T.matrix()
    w = theano.shared(numpy.random.randn(5, 7).astype(theano.config.floatX))
    y = T.dot(x, w)

    fun = theano.function(
        [x], y,
        mode=NanGuardMode(nan_is_error=True, inf_is_error=True)
    )
    a = numpy.random.randn(3, 5).astype(theano.config.floatX)
    infa = numpy.tile(
        (numpy.asarray(100.) ** 1000000).astype(theano.config.floatX), (3, 5))
    nana = numpy.tile(
        numpy.asarray(numpy.nan).astype(theano.config.floatX), (3, 5))
    biga = numpy.tile(
        numpy.asarray(1e20).astype(theano.config.floatX), (3, 5))

    fun(a)  # normal values

    # Temporarily silence logger
    _logger = logging.getLogger("theano.compile.nanguardmode")
    try:
        _logger.propagate = False
        assert_raises(AssertionError, fun, infa)  # INFs
        assert_raises(AssertionError, fun, nana)  # NANs
        assert_raises(AssertionError, fun, biga)  # big values
    finally:
        _logger.propagate = True

    # slices
    a = numpy.random.randn(3, 4, 5).astype(theano.config.floatX)
    infa = numpy.tile(
        (numpy.asarray(100.) ** 1000000).astype(theano.config.floatX),
        (3, 4, 5))
    nana = numpy.tile(
        numpy.asarray(numpy.nan).astype(theano.config.floatX), (3, 4, 5))
    biga = numpy.tile(
        numpy.asarray(1e20).astype(theano.config.floatX), (3, 4, 5))

    x = T.tensor3()
    y = x[:, T.arange(2), T.arange(2)]
    fun = theano.function(
        [x], y,
        mode=NanGuardMode(nan_is_error=True, inf_is_error=True)
    )
    fun(a)  # normal values
    try:
        _logger.propagate = False
        assert_raises(AssertionError, fun, infa)  # INFs
        assert_raises(AssertionError, fun, nana)  # NANs
        assert_raises(AssertionError, fun, biga)  # big values
    finally:
        _logger.propagate = True