Multivariate Pattern Analysis in Python

# kNN – Model Flexibility in Pictures

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')

See also

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