jax.ops.segment_min — JAX documentation (original) (raw)

jax.ops.segment_min#

jax.ops.segment_min(data, segment_ids, num_segments=None, indices_are_sorted=False, unique_indices=False, bucket_size=None, mode=None)[source]#

Computes the minimum within segments of an array.

Similar to TensorFlow’s segment_min

Parameters:

• data (ArrayLike) – an array with the values to be reduced.
• segment_ids (ArrayLike) – an array with integer dtype that indicates the segments ofdata (along its leading axis) to be reduced. Values can be repeated and need not be sorted.
• num_segments (int | None) – optional, an int with nonnegative value indicating the number of segments. The default is set to be the minimum number of segments that would support all indices in segment_ids, calculated asmax(segment_ids) + 1. Since num_segments determines the size of the output, a static value must be provided to use segment_min in a JIT-compiled function.
• indices_are_sorted (bool) – whether segment_ids is known to be sorted.
• unique_indices (bool) – whether segment_ids is known to be free of duplicates.
• bucket_size (int | None) – size of bucket to group indices into. segment_min is performed on each bucket separately. Default None means no bucketing.
• mode (slicing.GatherScatterMode | str | None) – a jax.lax.GatherScatterMode value describing how out-of-bounds indices should be handled. By default, values outside of the range [0, num_segments) are dropped and do not contribute to the result.

Returns:

An array with shape (num_segments,) + data.shape[1:] representing the segment minimums.

Return type:

Array

Examples

Simple 1D segment min:

data = jnp.arange(6) segment_ids = jnp.array([0, 0, 1, 1, 2, 2]) segment_min(data, segment_ids) Array([0, 2, 4], dtype=int32)

Using JIT requires static num_segments:

from jax import jit jit(segment_min, static_argnums=2)(data, segment_ids, 3) Array([0, 2, 4], dtype=int32)