def kmeans_classification_builder(centroid_func, x_train, x_test, y_train,
y_test):
# plot some train data
N = 25
l = int(np.ceil(np.sqrt(N)))
im = np.zeros((10 * l, 10 * l))
for m in range(l):
for n in range(l):
if (m * l + n < N):
im[10 * m:10 * m + 8,
10 * n:10 * n + 8] = x_train[m * l + n].reshape([8, 8])
plt.imsave('plots/digits.png', im, cmap='Greys')
n_cluster = 10
classifier = KMeansClassifier(n_cluster=n_cluster, max_iter=100, e=1e-6)
classifier.fit(x_train, y_train, centroid_func)
y_hat_test = classifier.predict(x_test)
# print(y_test[0])
# print(len(y_test))
assert y_hat_test.shape == y_test.shape, \
'y_hat_test and y_test should have same shape'
print(
'[*] Prediction accuracy of K-means classifier with {} cluster is {}'.
format(n_cluster, np.mean(y_hat_test == y_test)))
def kmeans_classification():
x_train, x_test, y_train, y_test = load_digits()
# plot some train data
N = 25
l = int(np.ceil(np.sqrt(N)))
#print(l)
im = np.zeros((10 * l, 10 * l))
for m in range(l):
for n in range(l):
if (m * l + n < N):
im[10 * m:10 * m + 8,
10 * n:10 * n + 8] = x_train[m * l + n].reshape([8, 8])
plt.imsave('plots/digits.png', im, cmap='Greys')
n_cluster = 30
classifier = KMeansClassifier(n_cluster=n_cluster, max_iter=100, e=1e-6)
classifier.fit(x_train, y_train)
y_hat_test = classifier.predict(x_test)
assert y_hat_test.shape == y_test.shape, \
'y_hat_test and y_test should have same shape'
print('Prediction accuracy of K-means classifier with {} cluster is {}'.
format(n_cluster, np.mean(y_hat_test == y_test)))
linear_classifier = LogisticRegression()
linear_classifier.fit(x_train, y_train)
y_hat_test = linear_classifier.predict(x_test)
print('Accuracy of logistic regression classifier is {}'.format(
np.mean(y_hat_test == y_test)))
KNNClassifier = KNeighborsClassifier()
KNNClassifier.fit(x_train, y_train)
y_hat_test = KNNClassifier.predict(x_test)
print('Accuracy of Nearest Neighbour classifier is {}'.format(
np.mean(y_hat_test == y_test)))
np.savez('results/k_means_classification.npz',
y_hat_test=y_hat_test,
y_test=y_test,
centroids=classifier.centroids,
centroid_labels=classifier.centroid_labels)
#k = 4
k=3
print(data_X)
#print(data_X[0])
clf = KMeansClassifier(k)
clf.fit(data_X)
cents = clf._centroids
labels = clf._labels
sse = clf._sse
colors = ['red','purple','darkgreen','darkgray','darksalmon','darkred','olive','yellow','yellowgreen',
'silver','cyan','pink','orangered','orange','navy','magenta','lightgoldenrodyellow',
'lavenderblush','honeydew','mediumseagreen']
print(cents)
pred = clf.predict(data_X)
print(pred)
print("The labels is:",labels)
print(traininglabel)
colorlist = []
colorlistreal = []
ARI = ARIcaculate(traininglabel,pred)
for i in range(len(data_X)):
colorlist.append(colors[int(pred[i])])
colorlistreal.append(colors[int(traininglabel[i])])
drawcluter_result(data_X,colorlist,sse,ARI)
drawcluter_real(data_X,colorlistreal)
#print(sse)
plt.title("SSE={:.2f},ARI={:.4f}".format(sse,ARI))
plt.axis([-7,7,-7,7])
outname = "./result/k_clusters" + str(k) + ".png"