def run_knn(Xtrain,Ytrain,Xtest,Ytest):
prod_features = RealFeatures(Xtrain)
prod_labels = MulticlassLabels(Ytrain)
test_features = RealFeatures(Xtest)
test_labels = MulticlassLabels(Ytest)
if os.path.exists(".lmnn_model30000_5_reg05_cor20"):
print "Using LMNN distance"
lmnn = LMNN()
sf = SerializableAsciiFile(".lmnn_model30000_5_reg05_cor20", 'r')
lmnn.load_serializable(sf)
diagonal = np.diag(lmnn.get_linear_transform())
#print('%d out of %d elements are non-zero.' % (np.sum(diagonal != 0), diagonal.size))
#diagonal = lmnn.get_linear_transform()
np.set_printoptions(precision=1,threshold=1e10,linewidth=500)
#lmnn.set_diagonal(True)
dist = lmnn.get_distance()
else:
dist = EuclideanDistance()
# classifier
knn = KNN(K, dist, prod_labels)
#knn.set_use_covertree(True)
parallel = knn.get_global_parallel()
parallel.set_num_threads(4)
knn.set_global_parallel(parallel)
knn.train(prod_features)
print "Classifying test set..."
pred = knn.apply_multiclass(test_features)
print "Accuracy = %2.2f%%" % (100*np.mean(pred == Ytest))
cm = build_confusion_matrix(Ytest, pred, NCLASSES)
#save_confusion_matrix(cm)
#cm = load_confusion_matrix()
print "Confusion matrix: "
print cm
#plot_confusion_matrix(cm)
#results = predict_class_prob(pred, cm)
#nn = build_neighbours_matrix(knn, prod_labels)
#results = predict_class_from_neighbours(nn)
#print "Log loss: " + str(calculate_log_loss(results, Ytest))
#print_prediction_output(results)
return cm
print "Training data size: " + str(Xtrain.shape)
print "Test data size: " + str(Xtest.shape)
N = Xtest.shape[0]
prod_features = RealFeatures(Xtrain.T)
prod_labels = MulticlassLabels(Ytrain.T)
test_features = RealFeatures(Xtest.T)
k = 5
# load LMNN
if os.path.exists(".lmnn_model30000_5_reg05_cor20"):
sf = SerializableAsciiFile(".lmnn_model30000_5_reg05_cor20", 'r')
lmnn = LMNN()
lmnn.load_serializable(sf)
diagonal = np.diag(lmnn.get_linear_transform())
print('%d out of %d elements are non-zero.' % (np.sum(diagonal != 0), diagonal.size))
#print diagonal
dist = lmnn.get_distance()
else:
dist = EuclideanDistance()
cm = load_confusion_matrix()
print cm
# classifier
knn = KNN(k, dist, prod_labels)
parallel = knn.get_global_parallel()
parallel.set_num_threads(4)
