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# -*- coding: utf-8 -*-
"""
Created on Mon Apr 14 09:01:18 2014
"""
def clusterPlot(X, clusterid, centroids='None', y='None', covars='None', figsize=(16,10)):
'''
CLUSTERPLOT Plots a clustering of a data set as well as the true class
labels. If data is more than 2-dimensional it should be first projected
onto the first two principal components. Data objects are plotted as a dot
with a circle around. The color of the dot indicates the true class,
and the cicle indicates the cluster index. Optionally, the centroids are
plotted as filled-star markers, and ellipsoids corresponding to covariance
matrices (e.g. for gaussian mixture models).
Usage:
clusterplot(X, clusterid)
clusterplot(X, clusterid, centroids=c_matrix, y=y_matrix)
clusterplot(X, clusterid, centroids=c_matrix, y=y_matrix, covars=c_tensor)
Input:
X N-by-M data matrix (N data objects with M attributes)
clusterid N-by-1 vector of cluster indices
centroids K-by-M matrix of cluster centroids (optional)
y N-by-1 vector of true class labels (optional)
covars M-by-M-by-K tensor of covariance matrices (optional)
'''
import numpy as np
from matplotlib.pyplot import figure, cm, plot, hold, legend, xlim, show
X = np.asarray(X)
cls = np.asarray(clusterid)
if y=='None':
y = np.zeros((X.shape[0],1))
else:
y = np.asarray(y)
if centroids!='None':
centroids = np.asarray(centroids)
K = np.size(np.unique(cls))
C = np.size(np.unique(y))
ncolors = np.max([C,K])
# plot data points color-coded by class, cluster markers and centroids
figure(figsize=figsize)
hold(True)
colors = [0]*ncolors
for color in range(ncolors):
colors[color] = cm.jet.__call__(color*1.0/(1.0*ncolors-1))[:3]
for i,cs in enumerate(np.unique(y)):
plot(X[(y==cs).ravel(),0], X[(y==cs).ravel(),1], 'o', markeredgecolor='k', markerfacecolor=colors[i],markersize=6, zorder=2)
for i,cr in enumerate(np.unique(cls)):
plot(X[(cls==cr).ravel(),0], X[(cls==cr).ravel(),1], 'o', markersize=12, markeredgecolor=colors[i], markerfacecolor='None', markeredgewidth=3, zorder=1)
if centroids!='None':
for cd in range(centroids.shape[0]):
plot(centroids[cd,0], centroids[cd,1], '*', markersize=22, markeredgecolor='k', markerfacecolor=colors[cd], markeredgewidth=2, zorder=3)
# plot cluster shapes:
if covars!='None':
for cd in range(centroids.shape[0]):
x1, x2 = gauss_2d(centroids[cd],covars[cd,:,:])
plot(x1,x2,'-', color=colors[cd], linewidth=3, zorder=5)
hold(False)
# create legend
legend_items = np.unique(y).tolist()+np.unique(cls).tolist()+np.unique(cls).tolist()
for i in range(len(legend_items)):
if i<C: legend_items[i] = 'Class: {0}'.format(legend_items[i]);
elif i<C+K: legend_items[i] = 'Cluster: {0}'.format(legend_items[i]);
else: legend_items[i] = 'Centroid: {0}'.format(legend_items[i]);
legend(legend_items, numpoints=1, markerscale=.75, prop={'size': 9})
xlim(X[:,0].min()*1.1, X[:,0].max()*1.2)
show()
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