def KNNAccuracy(distance, data, k, flag):
transformedData = np.dot(data[0], distance.T)
feat = RealFeatures(transformedData.T)
labels = MulticlassLabels(data[1].astype(np.float64))
dist = EuclideanDistance(feat, feat)
knn = KNN(k + 1, dist, labels)
knn.train(feat)
# Get nearest neighbors.
nn = knn.nearest_neighbors()
nn = np.delete(nn, 0, 0)
# Compute unique labels.
uniqueLabels = np.unique(labels)
# Keep count correct predictions.
count = 0
# Normalize labels
for i in range(data[0].shape[0]):
for j in range(len(uniqueLabels)):
if (labels[i] == uniqueLabels[j]):
labels[i] = j
break
for i in range(nn.shape[1]):
mapLabels = [0 for x in range(len(uniqueLabels))]
for j in range(nn.shape[0]):
if (flag):
distPoints = np.linalg.norm(data[0][nn[j][i], :] -
data[0][i, :])
# Add constant factor of 1 incase two points overlap
mapLabels[int(labels[nn[j, i]])] += 1 / (distPoints + 1)**2
else:
# Subtract a variable factor to avoid draw condition without
# affecting actual result.
mapLabels[int(labels[nn[j, i]])] += 1 - j * 1e-8
maxInd = np.argmax(mapLabels)
if (maxInd == labels[i]):
count += 1
accuracy = (count / nn.shape[1]) * 100
return accuracy
figure, axarr = pyplot.subplots(3, 1)
x, y = sandwich_data()
features = RealFeatures(x.T)
labels = MulticlassLabels(y)
print('%d vectors with %d features' % (features.get_num_vectors(), features.get_num_features()))
assert(features.get_num_vectors() == labels.get_num_labels())
distance = EuclideanDistance(features, features)
k = 2
knn = KNN(k, distance, labels)
plot_data(x, y, axarr[0])
plot_neighborhood_graph(x, knn.nearest_neighbors(), axarr[0])
axarr[0].set_aspect('equal')
axarr[0].set_xlim(-6, 4)
axarr[0].set_ylim(-3, 2)
lmnn = LMNN(features, labels, k)
lmnn.set_maxiter(10000)
lmnn.train()
L = lmnn.get_linear_transform()
knn.set_distance(lmnn.get_distance())
plot_data(x, y, axarr[1])
plot_neighborhood_graph(x, knn.nearest_neighbors(), axarr[1])
axarr[1].set_aspect('equal')
axarr[1].set_xlim(-6, 4)
axarr[1].set_ylim(-3, 2)
figure, axarr = pyplot.subplots(3, 1)
x, y = sandwich_data()
features = RealFeatures(x.T)
labels = MulticlassLabels(y)
print('%d vectors with %d features' %
(features.get_num_vectors(), features.get_num_features()))
assert (features.get_num_vectors() == labels.get_num_labels())
distance = EuclideanDistance(features, features)
k = 2
knn = KNN(k, distance, labels)
plot_data(x, y, axarr[0])
plot_neighborhood_graph(x, knn.nearest_neighbors(), axarr[0])
axarr[0].set_aspect('equal')
axarr[0].set_xlim(-6, 4)
axarr[0].set_ylim(-3, 2)
lmnn = LMNN(features, labels, k)
lmnn.set_maxiter(10000)
lmnn.train()
L = lmnn.get_linear_transform()
knn.set_distance(lmnn.get_distance())
plot_data(x, y, axarr[1])
plot_neighborhood_graph(x, knn.nearest_neighbors(), axarr[1])
axarr[1].set_aspect('equal')
axarr[1].set_xlim(-6, 4)
axarr[1].set_ylim(-3, 2)
