我想在散点图中绘制这个例子：
http://scikit-learn.org/dev/auto_examples/document_clustering.html#example-document-clustering-py
我是 sklearn 和 numpy 新手，我想获取向量坐标的数据，这样我就可以绘图了。
编辑：
这是我到目前为止得到的：
'''
Created on Apr 4, 2013
@author: v3ss
'''
from classify import recursive_load_files
from time import time
import numpy as np
import pylab as pl
from sklearn import metrics
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.preprocessing import scale
from sklearn.cluster import KMeans, MiniBatchKMeans
from os.path import isdir
from os import listdir
from os.path import join
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_selection import SelectKBest, chi2
from sklearn.linear_model import Perceptron, RidgeClassifier, SGDClassifier
from sklearn.naive_bayes import BernoulliNB, MultinomialNB
from sklearn.svm import LinearSVC
from sklearn.decomposition import RandomizedPCA
from sklearn.utils.validation import check_random_state
from time import time
import numpy as np
import os
import traceback
def clustering_from_files(trainer_path = "./dataset/dataset/training_data/"):
classifier = "NB"
load_files = recursive_load_files
trainer_path = os.path.realpath(trainer_path)
data_train = load_files(trainer_path, load_content = True, shuffle = False)
print "Extracting features from the training dataset using a sparse vectorizer"
t0 = time()
vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.7,
stop_words='english',charset_error="ignore")
X_train = vectorizer.fit_transform(data_train.data)
print "done in %fs" % (time() - t0)
print "Targets:",data_train.target
km = MiniBatchKMeans(n_clusters=15, init='k-means++', n_init=1,
init_size=1000,
batch_size=1000, verbose=1)
# kmeans = KMeans(init='k-means++', n_clusters=5, n_init=1)
print "Clustering sparse data with %s" % km
t0 = time()
return (km,X_train)
def reduce_dems(X_train):
rpca=RandomizedPCA(n_components=2)
return rpca.fit_transform(X_train)
def plot(kmeans,reduced_data):
kmeans.fit(reduced_data)
h = 0.1
x_min, x_max = reduced_data[:, 0].min() + 1, reduced_data[:, 0].max() - 1
y_min, y_max = reduced_data[:, 1].min() + 1, reduced_data[:, 1].max() - 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
pl.figure(1)
pl.clf()
pl.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.', markersize=2)
# Plot the centroids as a white X
centroids = kmeans.cluster_centers_
pl.scatter(centroids[:, 0], centroids[:, 1],
marker='x', s=20, linewidths=3,
color='r', zorder=10)
pl.title('K-means clustering on selected 20_newsgroup (religion group and technology) ')
pl.xlim(x_min, x_max)
pl.ylim(y_min, y_max)
pl.xticks(())
pl.yticks(())
pl.show()
def main():
k_means,X_train = clustering_from_files()
reduced = reduce_dems(X_train)
plot(k_means,reduced)
if __name__ == "__main__":
main()
编辑：
这现在效果更好，可以增加集群大小。