.. AUTO-GENERATED FILE -- DO NOT EDIT!

.. _example_knn_plot:


kNN -- Model Flexibility in Pictures
====================================

.. index:: kNN

TODO


::

  import numpy as np





::

  import mvpa2
  from mvpa2.base import cfg
  from mvpa2.misc.data_generators import *
  from mvpa2.clfs.knn import kNN
  from mvpa2.misc.plot import *

  mvpa2.seed(0)                            # to reproduce the plot

  dataset_kwargs = dict(nfeatures=2, nchunks=10,
      snr=2, nlabels=4, means=[ [0,1], [1,0], [1,1], [0,0] ])

  dataset_train = normal_feature_dataset(**dataset_kwargs)
  dataset_plot = normal_feature_dataset(**dataset_kwargs)


  # make a new figure
  pl.figure(figsize=(9, 9))

  for i,k in enumerate((1, 3, 9, 20)):
      knn = kNN(k)

      print "Processing kNN(%i) problem..." % k
      pl.subplot(2, 2, i+1)




::

 »    knn.train(dataset_train)

      plot_decision_boundary_2d(
          dataset_plot, clf=knn, maps='targets')


.. seealso::
  The full source code of this example is included in the PyMVPA source distribution (`doc/examples/knn_plot.py`).
