jax.numpy.nan_to_num

jax.numpy.nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None)

Replace NaN and infinite entries in an array.

JAX implementation of numpy.nan_to_num().

Parameters:

• x (ArrayLike) – array of values to be replaced. If it does not have an inexact dtype it will be returned unmodified.

• copy (bool) – unused by JAX

• nan (ArrayLike) – value to substitute for NaN entries. Defaults to 0.0.

• posinf (ArrayLike | None) – value to substitute for positive infinite entries. Defaults to the maximum representable value.

• neginf (ArrayLike | None) – value to substitute for positive infinite entries. Defaults to the minimum representable value.

Returns:

A copy of x with the requested substitutions.

Return type:

Array

See also

• jax.numpy.isnan(): return True where the array contains NaN

• jax.numpy.isposinf(): return True where the array contains +inf

• jax.numpy.isneginf(): return True where the array contains -inf

Examples

>>> x = jnp.array([0, jnp.nan, 1, jnp.inf, 2, -jnp.inf])

Default substitution values:

>>> jnp.nan_to_num(x)
Array([ 0.0000000e+00,  0.0000000e+00,  1.0000000e+00,  3.4028235e+38,
        2.0000000e+00, -3.4028235e+38], dtype=float32)

Overriding substitutions for -inf and +inf:

>>> jnp.nan_to_num(x, posinf=999, neginf=-999)
Array([   0.,    0.,    1.,  999.,    2., -999.], dtype=float32)

If you only wish to substitute for NaN values while leaving inf values untouched, using where() with jax.numpy.isnan() is a better option:

>>> jnp.where(jnp.isnan(x), 0, x)
Array([  0.,   0.,   1.,  inf,   2., -inf], dtype=float32)