标签：obs output model 技术分享 cat rom 降维 data form
TSNE提供了一种有效的数据降维方式，让我们可以在2维或3维的空间中展示聚类结果。
# -*- coding: utf-8 -*- from __future__ import unicode_literals from sklearn.manifold import TSNE import pandas as pd import matplotlib.pyplot as mp inputfile = ‘data/consumption_data.xls‘ outputfile = ‘tmp/data_type3.xls‘ data = pd.read_excel(inputfile, index_col=‘Id‘) data_zs = 1.0 * (data - data.mean()) / data.std() from sklearn.cluster import KMeans k = 3 iteration = 800 model = KMeans(n_clusters=k, n_jobs =4, max_iter=iteration) # n_jobs 并发数设置为4 model.fit(data_zs) # 开始聚类 r1 = pd.Series(model.labels_).value_counts() # 统计各个类别的数目 r2 = pd.DataFrame(model.cluster_centers_) # 找出聚类中心 r = pd.concat([data,pd.Series(model.labels_,index = data.index)],axis=1) r.columns = list(data.columns) + [r‘聚类类别‘] tsne = TSNE() tsne.fit_transform(data_zs) # 进行数据降维 tsne = pd.DataFrame(tsne.embedding_, index=data_zs.index) mp.rcParams[‘font.sans-serif‘] = [‘SimHei‘] mp.rcParams[‘axes.unicode_minus‘] = False d = tsne[r[r‘聚类类别‘] == 0] mp.plot(d[0], d[1], ‘r.‘) d = tsne[r[r‘聚类类别‘] == 1] mp.plot(d[0], d[1], ‘go‘) d = tsne[r[r‘聚类类别‘] == 2] mp.plot(d[0], d[1], ‘b*‘) mp.show()
运行结果：
标签：obs output model 技术分享 cat rom 降维 data form
原文地址：https://www.cnblogs.com/dancyhou/p/10255250.html
