`scipy.cluster.vq:vq`

source: /scipy/cluster/vq.py :150

Signature

def vq ( obs , code_book , check_finite = True )

Summary

Assign codes from a code book to observations.

Extended Summary

Assigns a code from a code book to each observation. Each observation vector in the 'M' by 'N' obs array is compared with the centroids in the code book and assigned the code of the closest centroid.

The features in obs should have unit variance, which can be achieved by passing them through the whiten function. The code book can be created with the k-means algorithm or a different encoding algorithm.

Parameters

obs : ndarray

Each row of the 'M' x 'N' array is an observation. The columns are the "features" seen during each observation. The features must be whitened first using the whiten function or something equivalent.

code_book : ndarray

The code book is usually generated using the k-means algorithm. Each row of the array holds a different code, and the columns are the features of the code

#              f0  f1  f2  f3
code_book = [[ 1., 2., 3., 4.],  #c0
             [ 1., 2., 3., 4.],  #c1
             [ 1., 2., 3., 4.]]  #c2

check_finite : bool, optional

Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs. Default: True

Returns

code : ndarray: A length M array holding the code book index for each observation.
dist : ndarray: The distortion (distance) between the observation and its nearest code.

Notes

Array API Standard Support

vq has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ⛔                   
PyTorch               ✅                     ⛔                   
JAX                   ⚠️ no JIT             ⛔                   
Dask                  ⚠️ computes graph     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

Examples

import numpy as np
from scipy.cluster.vq import vq
code_book = np.array([[1., 1., 1.],
                      [2., 2., 2.]])
features  = np.array([[1.9, 2.3, 1.7],
                      [1.5, 2.5, 2.2],
                      [0.8, 0.6, 1.7]])

✓

vq(features, code_book)

✗

Aliases

scipy.cluster.vq.vq