"""
plt.close('all')
# Load an image of a bird
A = mpimg.imread('bird_small.png')
A = A / 255 # Divide by 255 so that all values are in the range 0 - 1
# Size of the image
m, n, z = A.shape
X = A.reshape(m * n, 3)
K = 16
max_iters = 10
initial_centroids = kmeans_init_centroids(X, K)
# Run K-Means
centroids, idx = run_kmeans(X, initial_centroids, max_iters)
# Sample 1000 random indexes (since working with all the data is
# too expensive. If you have a fast computer, you may increase this.
sel = np.random.choice(X.shape[0], 1000)
fig = plt.figure()
from mpl_toolkits.mplot3d import Axes3D
ax = fig.add_subplot(111, projection='3d')
# Visualize the data and centroid memberships in 3D
ax.scatter(X[sel, 0], X[sel, 1], X[sel, 2], c=idx[sel])
plt.title('Pixel dataset plotted in 3D. Color shows centroid memberships')
print('Image shape {}'.format(img_shape))
# Reshape the image into an Nx3 matrix where N = number of pixels.
# Each row will contain the Red, Green and Blue pixel values
# This gives us our dataset matrix X that we will use K-Means on.
X = image.reshape(img_shape[0] * img_shape[1], 3)
# Run your K-Means algorithm on this data
# You should try different values of K and max_iters here
K = 16
max_iters = 10
# When using K-Means, it is important the initialize the centroids
# randomly.
# You should complete the code in kMeansInitCentroids.py before proceeding
initial_centroids = kmic.kmeans_init_centroids(X, K)
# Run K-Means
centroids, idx = km.run_kmeans(X, initial_centroids, max_iters, False)
# plt.show()
print('K-Means Done.')
input('Program paused. Press ENTER to continue')
# ===================== Part 5: Image Compression =====================
# In this part of the exercise, you will use the clusters of K-Means to
# compress an image. To do this, we first find the closest clusters for
# each example.
print('Applying K-Means to compress an image.')
# Find closest cluster members
#压缩到100维 再压缩重放后的数据
disp.display_data(X_rec[0:100])
plt.title('Recovered faces')
plt.axis('equal')
input('Program paused. Press ENTER to continue')
'''第8部分 利用PCA可视化高维数据'''
image = io.imread('bird_small.png') #读取图片
image = img_as_float(image)
img_shape = image.shape
X = image.reshape((img_shape[0] * img_shape[1], 3)) #将图片格式转换为包含3列（3个颜色通道）的矩阵
K = 16 #聚类中心数量
max_iters = 10 #外循环迭代次数
initial_centroids = kmic.kmeans_init_centroids(X, K) #初始化K个聚类中心
centroids, idx = km.run_kmeans(X, initial_centroids, max_iters,
False) #执行k-means，得到最终的聚类中心和每个样本点所属的聚类中心序号
selected = np.random.randint(X.shape[0],
size=1000) #随机选择1000（可以更改）个样本点 每个样本点3维
#可视化3维数据 不同颜色表示每个样本点的所属的簇
cm = plt.cm.get_cmap('RdYlBu')
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(X[selected, 0],
X[selected, 1],
X[selected, 2],
c=idx[selected],
cmap=cm,
