Vpf \ddlmZddlZddlmZddlmZddlm Z m Z ddl m Z m Z dddZdS)) annotationsN)vmap)check_arraylikepromote_dtypes_inexact)Array ArrayLikeTobsr code_book check_finiteboolreturntuple[Array, Array]c~td||t||\}|jjkrtd|jdkr|ddc}|jdkrtdt fd|}t j|d }t 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 supportedcXtj|dz dS)Naxis)jnplinalgnorm)obcb_arrs Y/var/www/html/nettyfy-visnx/env/lib/python3.11/site-packages/jax/_src/scipy/cluster/vq.pyzvq..Gs"DF):DDrr) rrndim ValueErrorrrargminoperatorgetitem)r r r obs_arrdistcodedist_minrs @rvqr'sL'i888*3 ::/'6 \V[  L M MM \Q *F9,=ogv \Q E F FF EDDDD E Eg N N$ Dr " " "$ #T(" # #D$ / /( xr)T)r rr rr r r r) __future__rr!jaxr jax.numpynumpyrjax._src.numpy.utilrrjax._src.typingrrr'rrr/s#"""""GGGGGGGG,,,,,,,,2222222r