def test_iwe_nearest_neighbours():
"""Test for estimating through nearest neighbours."""
X = rnd.randn(10, 2)
Z = rnd.randn(10, 2) + 1
clf = ImportanceWeightedClassifier()
iw = clf.iwe_nearest_neighbours(X, Z)
assert np.all(iw >= 0)
y = classes_train
nsamples = y.shape[0]
n_test_samples = classes_test.shape[0]
for index in [1,2,6,7,8,9,10,11,12,13]: #range(1,n_classifiers):
for iw in [0,1,2,3]:
iwe = weighting_functions[iw]
w_clf = ImportanceWeightedClassifier(iwe=iwe)
X = np.asarray(MEG_data_train)
Y = np.asarray(MEG_data_test)
w_clf.fit(X,y,Y)
if iwe == 'lr':
w = w_clf.iwe_logistic_discrimination(X, Y)
elif iwe == 'rg':
w = w_clf.iwe_ratio_gaussians(X, Y)
elif iwe == 'nn':
w = w_clf.iwe_nearest_neighbours(X, Y)
elif iwe == 'kde':
w = w_clf.iwe_kernel_densities(X, Y)
elif iwe == 'kmm':
w = w_clf.iwe_kernel_mean_matching(X, Y)
else:
raise NotImplementedError('Estimator not implemented.')
clf = Weighted_InitClassifier(index)
# Find importance-weights
probas_ = clf.fit(X, y, sample_weight=w).predict(X)
accuracy_org = sum((probas_>0.5)==y)/(1.0*nsamples)
target_probas_ = clf.predict(Y)
#print(classes_test.shape,Y.shape,n_test_samples)