NAME

    AI::MXNet::TestUtils - Convenience subs used in tests.

same

    Test if two pdl arrays are the same

    Parameters
    ----------
    a : pdl
    b : pdl

allclose

    Test if all elements of two pdl arrays are almost equal

    Parameters
    ----------
    a : pdl
    b : pdl

reldiff

    Calculate the relative difference between two input arrays

    Calculated by :math:`\\frac{|a-b|_1}{|a|_1 + |b|_1}`

    Parameters
    ----------
    a : pdl
    b : pdl

almost_equal

    Test if two pdl arrays are almost equal.

check_consistency

    Check symbol gives the same output for different running context

    Parameters
    ----------
    sym : Symbol or list of Symbols
        symbol(s) to run the consistency test
    ctx_list : list
        running context. See example for more detail.
    scale : float, optional
        standard deviation of the inner normal distribution. Used in initialization
    grad_req : str or list of str or dict of str to str
        gradient requirement.

same_array

    Check whether two NDArrays sharing the same memory block

    Parameters
    ----------

    array1 : NDArray
        First NDArray to be checked
    array2 : NDArray
        Second NDArray to be checked

    Returns
    -------
    bool
        Whether two NDArrays share the same memory

rand_sparse_ndarray

    Generate a random sparse ndarray. Returns the ndarray, value(np) and indices(np)

    Parameters
    ----------
    shape: list or tuple
    stype: str, valid values: "csr" or "row_sparse"
    density, optional: float, should be between 0 and 1
    distribution, optional: str, valid values: "uniform" or "powerlaw"
    dtype, optional: numpy.dtype, default value is None

    Returns
    -------
    Result of type CSRNDArray or RowSparseNDArray

    Examples
    --------
    Below is an example of the powerlaw distribution with csr as the stype.
    It calculates the nnz using the shape and density.
    It fills up the ndarray with exponentially increasing number of elements.
    If there are enough unused_nnzs, n+1th row will have twice more nnzs compared to nth row.
    else, remaining unused_nnzs will be used in n+1th row
    If number of cols is too small and we have already reached column size it will fill up
    all following columns in all followings rows until we reach the required density.

    >>> csr_arr, _ = rand_sparse_ndarray(shape=(5, 16), stype="csr",
                                         density=0.50, distribution="powerlaw")
    >>> indptr = csr_arr.indptr.asnumpy()
    >>> indices = csr_arr.indices.asnumpy()
    >>> data = csr_arr.data.asnumpy()
    >>> row2nnz = len(data[indptr[1]:indptr[2]])
    >>> row3nnz = len(data[indptr[2]:indptr[3]])
    >>> assert(row3nnz == 2*row2nnz)
    >>> row4nnz = len(data[indptr[3]:indptr[4]])
    >>> assert(row4nnz == 2*row3nnz)