def classify_nn(X, y, k):
m = X.shape[0]
m_test = int(m*0.25)
m_train = m - m_test
# Split data in train and test data
# A random permutation, to split the data randomly
#np.random.seed(k)
indices = np.random.permutation(m)
X_train = X[indices[:m_train]]
y_train = y[indices[:m_train]]
X_test = X[indices[m_train:]]
y_test = y[indices[m_train:]]
# Create and fit a nearest-neighbor classifier
from sklearn.neighbors import NeighborsClassifier
knn = NeighborsClassifier()
knn.fit(X_train, y_train)
print 'knn=%s' % knn
y_pred = knn.predict(X_test)
correct = y_pred == y_test
print 'k=%2d: Num tests=%6d correct=%6d = %2d%%' % (k, correct.shape[0], correct.sum(),
int(100*correct.sum()/correct.shape[0]))
if False:
for i in range(correct.shape[0]):
print ' %d==%d => %d' % (y_pred[i], y_test[i], correct[i])
exit()
def run_tests(classifier, test_data, c_type, name, k_kmeans, knn_ks, t_shelf):
for k_knn in knn_ks:
if c_type == 'knn':
means, labels = classifier
cls = NeighborsClassifier(n_neighbors=k_knn)
cls.fit(means, labels)
elif c_type == 'svm':
cls = classifier
# results: every test sample is labeled by classifier
X = np.vstack(test_data)
start = time.time()
results = cls.predict(X)
elapsed_time = time.time() - start
save_time(elapsed_time, len(X), t_shelf, name, k_kmeans, k_knn)
targets = make_targets(test_data)
save_results(results, targets, name, k_kmeans, k_knn)
print_results(results, targets)
