# =================== K-Means Clustering ======================
data = sio.loadmat('data\\ex7data2.mat')
K = 3
num_iters = 10
X = data['X']
initial_centroids = np.matrix([[3,3],
[6,2],
[8,5]])
kmeans = KMeans(K, num_iters)
idx = kmeans.findClosestCentroids(X, initial_centroids)
kmeans.train_model(X, initial_centroids, True)
# ============= K-Means Clustering on Pixels ===============
data = sio.loadmat('data\\bird_small.mat')
A = data['A']
A = A / 255
m, n, _ = A.shape
X = A.reshape([-1, 3])
K = 16
num_iters = 10
kmeans = KMeans(K, num_iters)
initial_centroids = kmeans.initCentroids(X)
