import scipy as sp
import matplotlib.pyplot as plt
from sklearn import cluster
try: # SciPy >= 0.16 have face in misc
from scipy.misc import face
face = face(gray=True)
except ImportError:
face = sp.face(gray=True)
n_clusters = 5
np.random.seed(0)
X = face.reshape((-1, 1)) # We need an (n_sample, n_feature) array
k_means = cluster.KMeans(n_clusters=n_clusters, n_init=4)
k_means.fit(X)
values = k_means.cluster_centers_.squeeze()
labels = k_means.labels_
# create an array from labels and values
face_compressed = np.choose(labels, values)
face_compressed.shape = face.shape
vmin = face.min()
vmax = face.max()
# original face
plt.figure(1, figsize=(3, 2.2))
plt.imshow(face, cmap=plt.cm.gray, vmin=vmin, vmax=256)
import scipy as sp
import matplotlib.pyplot as plt
from sklearn import cluster
try: # SciPy >= 0.16 have face in misc
from scipy.misc import face
face = face(gray=True)
except ImportError:
face = sp.face(gray=True)
n_clusters = 5
np.random.seed(0)
X = face.reshape((-1, 1)) # We need an (n_sample, n_feature) array
k_means = cluster.KMeans(n_clusters=n_clusters, n_init=4)
k_means.fit(X)
values = k_means.cluster_centers_.squeeze()
labels = k_means.labels_
# create an array from labels and values
face_compressed = np.choose(labels, values)
face_compressed.shape = face.shape
vmin = face.min()
vmax = face.max()
# original face
plt.figure(1, figsize=(3, 2.2))
plt.imshow(face, cmap=plt.cm.gray, vmin=vmin, vmax=256)
assert a == 1
print(1)
assert a == 0
print(0)
# 實作k-means
try: # SciPy >= 0.16 have face in misc
from scipy.misc import face
face = face(gray=True)
except ImportError:
face = sp.face(gray=True) # face是一張圖片，大小為768*1024
n_clusters = 5
np.random.seed(0)
X = face.reshape((-1, 1)) # 拉成一維向量 (n_sample, n_feature) array
k_means = cluster.KMeans(n_clusters=n_clusters,
n_init=4) # K_means分類，num_cluster=5
k_means.fit(X)
values = k_means.cluster_centers_.squeeze(
) # 使用不同 centroid seeds 運行 k-means 算法的時間
labels = k_means.labels_
face_compressed = np.choose(labels,
values) #labels範圍0~4，長度768*1024，values代表五個k_keans值
face_compressed.shape = face.shape
v_min = face.min()
v_max = face.max()
plt.figure(1, figsize=(3, 2.2))