jax.numpy.nan_to_num#

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

Replace NaN and infinite entries in an array.

JAX implementation ofnumpy.nan_to_num().

Parameters:

• x (ArrayLike) – array of values to be replaced. If it does not have an inexactdtype 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 ofx 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 leavinginf valuesuntouched, usingwhere() withjax.numpy.isnan() isa better option:

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