# scipy.cluster.vq.vq¶

scipy.cluster.vq.vq(obs, code_book)[source]

Assign codes from a code book to observations.

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

Notes

This currently forces 32-bit math precision for speed. Anyone know of a situation where this undermines the accuracy of the algorithm?

Examples

```>>> from numpy import array
>>> from scipy.cluster.vq import vq
>>> code_book = array([[1.,1.,1.],
...                    [2.,2.,2.]])
>>> features  = array([[  1.9,2.3,1.7],
...                    [  1.5,2.5,2.2],
...                    [  0.8,0.6,1.7]])
>>> vq(features,code_book)
(array([1, 1, 0],'i'), array([ 0.43588989,  0.73484692,  0.83066239]))
```

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