raw_random.py 文件源码

def uniform(random_state, size=None, low=0.0, high=1.0, ndim=None, dtype=None):
    """
    Sample from a uniform distribution between low and high.

    If the size argument is ambiguous on the number of dimensions, ndim
    may be a plain integer to supplement the missing information.

    If size is None, the output shape will be determined by the shapes
    of low and high.

    If dtype is not specified, it will be inferred from the dtype of
    low and high, but will be at least as precise as floatX.

    """
    low = tensor.as_tensor_variable(low)
    high = tensor.as_tensor_variable(high)
    if dtype is None:
        dtype = tensor.scal.upcast(theano.config.floatX, low.dtype, high.dtype)
    ndim, size, bcast = _infer_ndim_bcast(ndim, size, low, high)
    op = RandomFunction('uniform',
                        tensor.TensorType(dtype=dtype, broadcastable=bcast))
    return op(random_state, size, low, high)