flopscope.numpy.heaviside
fnp.heaviside(*args, **kwargs)[flopscope source][numpy source]
Compute the Heaviside step function.
Adapted from NumPy docs np.heaviside
Heaviside step function element-wise.
The Heaviside step function [1]_ is defined as:
0 if x1 < 0
heaviside(x1, x2) = x2 if x1 == 0
1 if x1 > 0where x2 is often taken to be 0.5, but 0 and 1 are also sometimes used.
Parameters
- x1:array_like
Input values.
- x2:array_like
The value of the function when x1 is 0. If
x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).- out:ndarray, None, or tuple of ndarray and None, optional
A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
- where:array_like, optional
This condition is broadcast over the input. At locations where the condition is True, the
outarray will be set to the ufunc result. Elsewhere, theoutarray will retain its original value. Note that if an uninitializedoutarray is created via the defaultout=None, locations within it where the condition is False will remain uninitialized.- **kwargs
For other keyword-only arguments, see the ufunc docs.
Returns
- out:ndarray or scalar
The output array, element-wise Heaviside step function of
x1. This is a scalar if bothx1andx2are scalars.
References
1
Wikipedia, "Heaviside step function",
https://en.wikipedia.org/wiki/Heaviside_step_functionExamples
>>> import flopscope.numpy as fnp
>>> flops.heaviside([-1.5, 0, 2.0], 0.5)
array([ 0. , 0.5, 1. ])
>>> flops.heaviside([-1.5, 0, 2.0], 1)
array([ 0., 1., 1.])