jax.numpy.nanmin

jax.numpy.nanmin(a, axis=None, out=None, keepdims=False, initial=None, where=None)

Return the minimum of the array elements along a given axis, ignoring NaNs.

JAX implementation of numpy.nanmin().

Parameters:

• a (ArrayLike) – Input array.

• axis (Axis) – int or sequence of ints, default=None. Axis along which the minimum is computed. If None, the minimum is computed along the flattened array.

• keepdims (bool) – bool, default=False. If True, reduced axes are left in the result with size 1.

• initial (ArrayLike | None) – int or array, default=None. Initial value for the minimum.

• where (ArrayLike | None) – array of boolean dtype, default=None. The elements to be used in the minimum. Array should be broadcast compatible to the input. initial must be specified when where is used.

• out (None) – Unused by JAX.

Returns:

An array of minimum values along the given axis, ignoring NaNs. If all values are NaNs along the given axis, returns nan.

Return type:

Array

See also

• jax.numpy.nanmax(): Compute the maximum of array elements along a given axis, ignoring NaNs.

• jax.numpy.nansum(): Compute the sum of array elements along a given axis, ignoring NaNs.

• jax.numpy.nanprod(): Compute the product of array elements along a given axis, ignoring NaNs.

• jax.numpy.nanmean(): Compute the mean of array elements along a given axis, ignoring NaNs.

Examples

By default, jnp.nanmin computes the minimum of elements along the flattened array.

>>> nan = jnp.nan
>>> x = jnp.array([[1, nan, 4, 5],
...                [nan, -2, nan, -4],
...                [2, 1, 3, nan]])
>>> jnp.nanmin(x)
Array(-4., dtype=float32)

If axis=1, the maximum will be computed along axis 1.

>>> jnp.nanmin(x, axis=1)
Array([ 1., -4.,  1.], dtype=float32)

If keepdims=True, ndim of the output will be same of that of the input.

>>> jnp.nanmin(x, axis=1, keepdims=True)
Array([[ 1.],
       [-4.],
       [ 1.]], dtype=float32)

To include only specific elements in computing the maximum, you can use where. It can either have same dimension as input

>>> where=jnp.array([[0, 0, 1, 0],
...                  [0, 0, 1, 1],
...                  [1, 1, 1, 0]], dtype=bool)
>>> jnp.nanmin(x, axis=1, keepdims=True, initial=0, where=where)
Array([[ 0.],
       [-4.],
       [ 0.]], dtype=float32)

or must be broadcast compatible with input.

>>> where = jnp.array([[False],
...                    [True],
...                    [False]])
>>> jnp.nanmin(x, axis=0, keepdims=True, initial=0, where=where)
Array([[ 0., -2.,  0., -4.]], dtype=float32)