from __future__ import print_function
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
import math as m
Id = ot.IdentityMatrix(2)
atoms = [
ot.Normal([1.0, 2.0], [0.5, 0.8], Id),
ot.Normal([1.0, -2.0], [0.9, 0.8], Id),
ot.Normal([-1.0, 0.0], [0.5, 0.6], Id)
]
weights = [0.3, 0.3, 0.4]
mixture = ot.Mixture(atoms, weights)
data = mixture.getSample(1000)
classifier = ot.MixtureClassifier(mixture)
graph = mixture.drawPDF(data.getMin(), data.getMax())
graph.setLegendPosition("")
graph.setTitle("MixtureClassifier example")
classes = classifier.classify(data)
palette = ot.Drawable.BuildDefaultPalette(len(atoms))
symbols = ot.Drawable.GetValidPointStyles()
for i in range(classes.getSize()):
index = classes[i]
graph.add(
ot.Cloud([data[i]], palette[index % len(palette)],
symbols[index % len(symbols)]))
fig = plt.figure(figsize=(4, 4))
axis = fig.add_subplot(111)
axis.set_xlim(auto=True)
#! /usr/bin/env python
from __future__ import print_function
import openturns as ot
R = ot.CorrelationMatrix(2)
R[0, 1] = -0.99
d1 = ot.Normal([-1.0, 1.0], [1.0, 1.0], R)
R[0, 1] = 0.99
d2 = ot.Normal([1.0, 1.0], [1.0, 1.0], R)
distribution = ot.Mixture([d1, d2], [1.0] * 2)
classifier = ot.MixtureClassifier(distribution)
f1 = ot.SymbolicFunction(['x'], ['-x'])
f2 = ot.SymbolicFunction(['x'], ['x'])
experts = ot.Basis([f1, f2])
moe = ot.ExpertMixture(experts, classifier)
moeNMF = ot.Function(moe)
print('Mixture of experts=', moe)
# Evaluate the mixture of experts on some points
for i in range(2):
p = [-0.3 + 0.8 * i / 4.0]
print('moe ( %.6g )=' % p[0], moe(p))
print('moeNMF( %.6g )=' % p[0], moeNMF(p))
# and on a sample
x = [[-0.3], [0.1]]
print('x=', ot.Sample(x), 'moeNMF(x)=', moeNMF(x))
# non-supervised mode (2d)
f1 = ot.SymbolicFunction(['x1', 'x2'], ['-8'])
