jax.numpy.min#

jax.numpy.min(a,axis=None,out=None,keepdims=False,initial=None,where=None)[source]#

Return the minimum of array elements along a given axis.

JAX implementation ofnumpy.min().

Parameters:

• a (ArrayLike) – Input array.

• axis (Axis) – int or array, default=None. Axis along which the minimum to be computed.If None, the minimum is computed along all the axes.

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

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

• where (ArrayLike |None) – int or array, default=None. The elements to be used in the minimum.Array should be broadcast compatible to the input.initial must bespecified whenwhere is used.

• out (None) – Unused by JAX.

Returns:

An array of minimum values along the given axis.

Return type:

Array

See also

• jax.numpy.max(): Compute the maximum of array elements along a givenaxis.

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

• jax.numpy.prod(): Compute the product of array elements along a givenaxis.

Examples

By default, the minimum is computed along all the axes.

>>>x=jnp.array([[2,5,1,6],...[3,-7,-2,4],...[8,-4,1,-3]])>>>jnp.min(x)Array(-7, dtype=int32)

Ifaxis=1, the minimum is computed along axis 1.

>>>jnp.min(x,axis=1)Array([ 1, -7, -4], dtype=int32)

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

>>>jnp.min(x,axis=1,keepdims=True)Array([[ 1],       [-7],       [-4]], dtype=int32)

To include only specific elements in computing the minimum, you can usewhere.where can either have same dimension as input.

>>>where=jnp.array([[1,0,1,0],...[0,0,1,1],...[1,1,1,0]],dtype=bool)>>>jnp.min(x,axis=1,keepdims=True,initial=0,where=where)Array([[ 0],       [-2],       [-4]], dtype=int32)

or must be broadcast compatible with input.

>>>where=jnp.array([[False],...[False],...[False]])>>>jnp.min(x,axis=0,keepdims=True,initial=0,where=where)Array([[0, 0, 0, 0]], dtype=int32)