uki fddlmZddlZddlmZddlmZddlmZ ddl m Z m Z ddl mZmZd d dZy) ) annotationsN)api)numpy)linalg)check_arraylikepromote_dtypes_inexact)Array ArrayLikec~td||t||\}|jjk7r td|jdk(r |ddc}|jdk7r tdt j fd|}t j|d }t j tj||}||fS) aAssign codes from a code book to a set of observations. JAX implementation of :func:`scipy.cluster.vq.vq`. Assigns each observation vector in ``obs`` to a code from ``code_book`` based on the nearest Euclidean distance. Args: obs: array of observation vectors of shape ``(M, N)``. Each row represents a single observation. If ``obs`` is one-dimensional, then each entry is treated as a length-1 observation. code_book: array of codes with shape ``(K, N)``. Each row represents a single code vector. If ``code_book`` is one-dimensional, then each entry is treated as a length-1 code. check_finite: unused in JAX Returns: A tuple of arrays ``(code, dist)`` - ``code`` is an integer array of shape ``(M,)`` containing indices ``0 <= i < K`` of the closest entry in ``code_book`` for the given entry in ``obs``. - ``dist`` is a float array of shape ``(M,)`` containing the euclidean distance between each observation and the nearest code. Examples: >>> obs = jnp.array([[1.1, 2.1, 3.1], ... [5.9, 4.8, 6.2]]) >>> code_book = jnp.array([[1., 2., 3.], ... [2., 3., 4.], ... [3., 4., 5.], ... [4., 5., 6.]]) >>> codes, distances = jax.scipy.cluster.vq.vq(obs, code_book) >>> print(codes) [0 3] >>> print(distances) [0.17320499 1.9209373 ] zscipy.cluster.vq.vqz3Observation and code_book should have the same rank).Nz,ndim different than 1 or 2 are not supportedc>tj|dz dS)Naxis) jnp_linalgnorm)obcb_arrs T/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/jax/_src/scipy/cluster/vq.pyzvq..HsZ__RX->RHrr) rrndim ValueErrorrvmapjnpargminoperatorgetitem)obs code_book check_finiteobs_arrdistcodedist_minrs @rvqr'sL'i8*3 :/'6 \\V[[ L MM \\Q *F9,=ogv \\Q E FF IH I' R$ Dr "$ 'SXXh&& 'd 3( xr)T)r r r!r r"boolreturnztuple[Array, Array]) __future__rrjax._srcrrrjax._src.numpyrrjax._src.numpy.utilrrjax._src.typingr r r'rrr0s!#!/G,2r