xs[300:400, :] = numpy.random.multivariate_normal(mus[3, :], sigma, 100)
xs[400:500, :] = numpy.random.multivariate_normal(mus[4, :], sigma, 100)
xs[500:600, :] = numpy.random.multivariate_normal(mus[5, :], sigma, 100)
true_z = numpy.zeros((600,))
true_z[0:100] = 0
true_z[100:200] = 1
true_z[200:300] = 2
true_z[300:400] = 3
true_z[400:500] = 4
true_z[500:600] = 5
pylab.subplot(1, 3, 1)
pylab.scatter(xs[:, 0], xs[:, 1], c=true_z)
pylab.title("True")
# pylab.show()
model = DPMixtureModel(xs, 6, 5000, 100, 1)
model.gamma = 1.0
model.fit(verbose=True)
pylab.subplot(1, 3, 2)
model_z = model.get_class()
results = model.get_results()
model_class = results.classify(xs)
pylab.scatter(xs[:, 0], xs[:, 1], c=model_class)
pylab.scatter(results.mus()[:, 0], results.mus()[:, 1], c='red')
pylab.title("component")
modal = results.make_modal()
z = modal.classify(xs)
print z.min(), z.max()
pylab.subplot(1, 3, 3)
pylab.scatter(xs[:, 0], xs[:, 1], c=z)
pylab.title("modal")
pylab.show()
xs[300:400, :] = numpy.random.multivariate_normal(mus[3, :], sigma, 100)
xs[400:500, :] = numpy.random.multivariate_normal(mus[4, :], sigma, 100)
xs[500:600, :] = numpy.random.multivariate_normal(mus[5, :], sigma, 100)
true_z = numpy.zeros((600, ))
true_z[0:100] = 0
true_z[100:200] = 1
true_z[200:300] = 2
true_z[300:400] = 3
true_z[400:500] = 4
true_z[500:600] = 5
pylab.subplot(1, 3, 1)
pylab.scatter(xs[:, 0], xs[:, 1], c=true_z)
pylab.title("True")
#pylab.show()
model = DPMixtureModel(xs, 6, 5000, 100, 1)
model.gamma = 1.0
model.fit(verbose=True)
pylab.subplot(1, 3, 2)
model_z = model.get_class()
results = model.get_results()
model_class = results.classify(xs)
pylab.scatter(xs[:, 0], xs[:, 1], c=model_class)
pylab.scatter(results.mus()[:, 0], results.mus()[:, 1], c='red')
pylab.title("component")
modal = results.make_modal()
z = modal.classify(xs)
print z.min(), z.max()
pylab.subplot(1, 3, 3)
pylab.scatter(xs[:, 0], xs[:, 1], c=z)
pylab.title("modal")
pylab.show()
