TODO
"""
import numpy as np
"""
"""
import mvpa
from mvpa.base import cfg
from mvpa.misc.data_generators import *
from mvpa.clfs.knn import kNN
from mvpa.misc.plot import *
mvpa.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)
"""
import numpy as np
"""
"""
import mvpa
from mvpa.base import cfg
from mvpa.misc.data_generators import *
from mvpa.clfs.knn import kNN
from mvpa.misc.plot import *
mvpa.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)
def newfunc(*arg, **kwargs):
mvpa.seed(mvpa._random_seed)
return func(*arg, **kwargs)
