import numpy as np
from sklearn.datasets
import make_blobs
from sklearn.cluster
import KMeans
from sklearn.metrics
import pairwise_distances
import matplotlib.pyplot as plt
import matplotlib as mpl
from cycler
import cycler
from .tools
import discrete_scatter
from .plot_2d_separator
import plot_2d_classification
from .plot_helpers
import cm3
def plot_kmeans_algorithm():
X, y = make_blobs(random_state=1
)
# we don't want cyan in there
with mpl.rc_context(rc={
'axes.prop_cycle': cycler(
'color', [
'#0000aa',
'#ff2020',
'#50ff50'])}):
fig, axes = plt.subplots(3, 3, figsize=(10, 8), subplot_kw={
'xticks': (),
'yticks': ()})
axes =
axes.ravel()
axes[0].set_title("Input data")
discrete_scatter(X[:, 0], X[:, 1], ax=axes[0], markers=[
'o'], c=
'w')
axes[1].set_title(
"Initialization")
init = X[:3
, :]
discrete_scatter(X[:, 0], X[:, 1], ax=axes[1], markers=[
'o'], c=
'w')
discrete_scatter(init[:, 0], init[:, 1], [0, 1, 2], ax=axes[1
],
markers=[
'^'], markeredgewidth=2
)
axes[2].set_title(
"Assign Points (1)")
km = KMeans(n_clusters=3, init=init, max_iter=1, n_init=1
).fit(X)
centers =
km.cluster_centers_
# need to compute labels by hand. scikit-learn does two e-steps for max_iter=1
# (and it's totally my fault)
labels = np.argmin(pairwise_distances(init, X), axis=
0)
discrete_scatter(X[:, 0], X[:, 1], labels, markers=[
'o'],
ax=axes[2
])
discrete_scatter(init[:, 0], init[:, 1], [0, 1, 2
],
ax=axes[2], markers=[
'^'], markeredgewidth=2
)
axes[3].set_title(
"Recompute Centers (1)")
discrete_scatter(X[:, 0], X[:, 1], labels, markers=[
'o'],
ax=axes[3
])
discrete_scatter(centers[:, 0], centers[:, 1], [0, 1, 2
],
ax=axes[3], markers=[
'^'], markeredgewidth=2
)
axes[4].set_title(
"Reassign Points (2)")
km = KMeans(n_clusters=3, init=init, max_iter=1, n_init=1
).fit(X)
labels =
km.labels_
discrete_scatter(X[:, 0], X[:, 1], labels, markers=[
'o'],
ax=axes[4
])
discrete_scatter(centers[:, 0], centers[:, 1], [0, 1, 2
],
ax=axes[4], markers=[
'^'], markeredgewidth=2
)
km = KMeans(n_clusters=3, init=init, max_iter=2, n_init=1
).fit(X)
axes[5].set_title(
"Recompute Centers (2)")
centers =
km.cluster_centers_
discrete_scatter(X[:, 0], X[:, 1], labels, markers=[
'o'],
ax=axes[5
])
discrete_scatter(centers[:, 0], centers[:, 1], [0, 1, 2
],
ax=axes[5], markers=[
'^'], markeredgewidth=2
)
axes[6].set_title(
"Reassign Points (3)")
labels =
km.labels_
discrete_scatter(X[:, 0], X[:, 1], labels, markers=[
'o'],
ax=axes[6
])
markers = discrete_scatter(centers[:, 0], centers[:, 1], [0, 1, 2
],
ax=axes[6], markers=[
'^'],
markeredgewidth=2
)
axes[7].set_title(
"Recompute Centers (3)")
km = KMeans(n_clusters=3, init=init, max_iter=3, n_init=1
).fit(X)
centers =
km.cluster_centers_
discrete_scatter(X[:, 0], X[:, 1], labels, markers=[
'o'],
ax=axes[7
])
discrete_scatter(centers[:, 0], centers[:, 1], [0, 1, 2
],
ax=axes[7], markers=[
'^'], markeredgewidth=2
)
axes[8
].set_axis_off()
axes[8].legend(markers, [
"Cluster 0",
"Cluster 1",
"Cluster 2"], loc=
'best')
def plot_kmeans_boundaries():
X, y = make_blobs(random_state=1
)
init = X[:3
, :]
km = KMeans(n_clusters=3, init=init, max_iter=2, n_init=1
).fit(X)
discrete_scatter(X[:, 0], X[:, 1], km.labels_, markers=[
'o'])
discrete_scatter(km.cluster_centers_[:, 0], km.cluster_centers_[:, 1
],
[0, 1, 2], markers=[
'^'], markeredgewidth=2
)
plot_2d_classification(km, X, cm=cm3, alpha=.4
)
def plot_kmeans_faces(km, pca, X_pca, X_people, y_people, target_names):
n_clusters = 10
image_shape = (87, 65
)
fig, axes = plt.subplots(n_clusters, 11, subplot_kw={
'xticks': (),
'yticks': ()},
figsize=(10, 15), gridspec_kw={
"hspace": .3
})
for cluster
in range(n_clusters):
center =
km.cluster_centers_[cluster]
mask = km.labels_ ==
cluster
dists = np.sum((X_pca - center) ** 2, axis=1
)
dists[~mask] =
np.inf
inds = np.argsort(dists)[:5
]
dists[~mask] = -
np.inf
inds = np.r_[inds, np.argsort(dists)[-5
:]]
axes[cluster, 0].imshow(pca.inverse_transform(center).reshape(image_shape), vmin=0, vmax=1
)
for image, label, asdf, ax
in zip(X_people[inds], y_people[inds],
km.labels_[inds], axes[cluster, 1
:]):
ax.imshow(image.reshape(image_shape), vmin=0, vmax=1
)
ax.set_title("%s" % (target_names[label].split()[-1]), fontdict={
'fontsize': 9
})
# add some boxes to illustrate which are similar and which dissimilar
rec = plt.Rectangle([-5, -30], 73, 1295, fill=False, lw=2
)
rec =
axes[0, 0].add_patch(rec)
rec.set_clip_on(False)
axes[0, 0].text(0, -40,
"Center")
rec = plt.Rectangle([-5, -30], 385, 1295, fill=False, lw=2
)
rec = axes[0, 1
].add_patch(rec)
rec.set_clip_on(False)
axes[0, 1].text(0, -40,
"Close to center")
rec = plt.Rectangle([-5, -30], 385, 1295, fill=False, lw=2
)
rec = axes[0, 6
].add_patch(rec)
rec.set_clip_on(False)
axes[0, 6].text(0, -40,
"Far from center")
过程解析：
在大数据集的情况下还可以使用scikit-learn 提供了MiniBatchKMeans算法，大致思想就是对数据进行抽样，每次不使用所有的数据来计算，这就会导致准确率的损失。
MiniBatchKmeans 继承自Kmeans 因为MiniBathcKmeans 本质上还利用了Kmeans 的思想.从构造方法和文档大致能看到这些参数的含义，了解了这些参数会对使用的时候有很大的帮助。batch_size 是每次选取的用于计算的数据的样本量，默认为100.Mini Batch K-Means算法是K-Means算法的变种，采用小批量的数据子集减小计算时间，同时仍试图优化目标函数，这里所谓的小批量是指每次训练算法时所随机抽取的数据子集，采用这些随机产生的子集进行训练算法，大大减小了计算时间，与其他算法相比，减少了k-均值的收敛时间，小批量k-均值产生的结果，一般只略差于标准算法。
代码只需要修改一行：
clf = MiniBatchKMeans(n_clusters = 3)
