mixture = ot.Mixture(dists)
# 3-d test
R1 = ot.CovarianceMatrix(3)
R1[2, 1] = -0.25
R2 = ot.CovarianceMatrix(3)
R2[1, 0] = 0.5
R2[2, 1] = -0.3
R2[0, 0] = 1.3
print(R2)
dists = [ot.Normal([1.0, -2.0, 3.0], R1), ot.Normal([-1.0, 2.0, -2.0], R2)]
mixture = ot.Mixture(dists, [2.0 / 3.0, 1.0 / 3.0])
sample = mixture.getSample(1000)
distribution = ot.KernelSmoothing().build(sample)
algo = ot.MinimumVolumeClassifier(distribution, 0.8)
threshold = algo.getThreshold()
print("threshold=", threshold)
assert m.fabs(threshold - 0.0012555) < 1e-3, "wrong threshold"
cls_ref = [
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 1, 1, 0, 1, 1, 1, 1, 1
]
for i in range(35):
x = sample[i]
cls = algo.classify(x)
pdf = mixture.computePDF(x)
print(i, x, cls, pdf - threshold)
assert cls == cls_ref[i], "wrong class"
graph1 = algo.drawSample(sample, [0])
mixture = ot.Mixture(dists)
# 3-d test
R1 = ot.CovarianceMatrix(3)
R1[2, 1] = -0.25
R2 = ot.CovarianceMatrix(3)
R2[1, 0] = 0.5
R2[2, 1] = -0.3
R2[0, 0] = 1.3
print(R2)
dists = [ot.Normal([1.0, -2.0, 3.0], R1), ot.Normal([-1.0, 2.0, -2.0], R2)]
mixture = ot.Mixture(dists, [2.0 / 3.0, 1.0 / 3.0])
sample = mixture.getSample(1000)
distribution = ot.KernelSmoothing().build(sample)
algo = ot.MinimumVolumeClassifier(distribution, [0.8])
threshold = algo.getThreshold()
print("threshold=", threshold)
assert m.fabs(threshold[0] - 0.0012555) < 1e-3, "wrong threshold"
cls_ref = [
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 1, 1, 0, 1, 1, 1, 1, 1
]
for i in range(35):
x = sample[i]
cls = algo.classify(x)
pdf = mixture.computePDF(x)
print(i, x, cls, pdf - threshold[0])
assert cls == 1 - cls_ref[i], "wrong class"
graph1 = algo.drawSample(sample, [0])
