# jax.numpy.heaviside¶

jax.numpy.heaviside(x1, x2)[source]

Compute the Heaviside step function.

LAX-backend implementation of heaviside(). Original docstring below.

heaviside(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature, extobj])

The Heaviside step function is defined as:

                      0   if x1 < 0
heaviside(x1, x2) =  x2   if x1 == 0
1   if x1 > 0


where 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).

Returns

out – The output array, element-wise Heaviside step function of x1. This is a scalar if both x1 and x2 are scalars.

Return type

ndarray or scalar

Notes

New in version 1.13.0.

References

Examples

>>> np.heaviside([-1.5, 0, 2.0], 0.5)
array([ 0. ,  0.5,  1. ])
>>> np.heaviside([-1.5, 0, 2.0], 1)
array([ 0.,  1.,  1.])