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#! /usr/bin/env python
from __future__ import print_function
from openturns import *
TESTPREAMBLE()
RandomGenerator.SetSeed(0)
try:
dim = 10
R = CorrelationMatrix(dim)
for i in range(dim):
for j in range(i):
R[i, j] = (i + j + 1.0) / (2.0 * dim)
mean = NumericalPoint(dim, 2.0)
sigma = NumericalPoint(dim, 3.0)
distribution = Normal(mean, sigma, R)
size = 100
sample = distribution.getSample(size)
sampleX = NumericalSample(size, dim - 1)
sampleY = NumericalSample(size, 1)
for i in range(size):
sampleY[i] = NumericalPoint(1, sample[i, 0])
p = NumericalPoint(dim - 1)
for j in range(dim - 1):
p[j] = sample[i, j + 1]
sampleX[i] = p
sampleZ = NumericalSample(size, 1)
for i in range(size):
sampleZ[i] = NumericalPoint(1, sampleY[i, 0] * sampleY[i, 0])
discreteSample1 = Poisson(0.1).getSample(size)
discreteSample2 = Geometric(0.4).getSample(size)
# ChiSquared Independance test : test if two samples (of sizes not necessarily equal) are independant ?
# Care : discrete samples only
# H0 = independent samples
# p-value threshold : probability of the H0 reject zone : 1-0.90
# p-value : probability (test variable decision > test variable decision evaluated on the samples)
# Test = True <=> p-value > p-value threshold
print("ChiSquared=", HypothesisTest.ChiSquared(
discreteSample1, discreteSample2, 0.90))
print("ChiSquared2=", HypothesisTest.ChiSquared(
discreteSample1, discreteSample1, 0.90))
# Pearson Test : test if two gaussian samples are independent (based on the evaluation of the linear correlation coefficient)
# H0 : independent samples (linear correlation coefficient = 0)
# Test = True <=> independent samples (linear correlation coefficient = 0)
# p-value threshold : probability of the H0 reject zone : 1-0.90
# p-value : probability (test variable decision > test variable decision evaluated on the samples)
# Test = True <=> p-value > p-value threshold
print("Pearson=", HypothesisTest.Pearson(sampleY, sampleZ, 0.90))
# Smirnov Test : test if two samples (of sizes not necessarily equal) follow the same distribution
# Care : continuous distributions only
# H0 = same continuous distribution
# Test = True <=> same distribution
# p-value threshold : probability of the H0 reject zone : 1-0.90
# p-value : probability (test variable decision > test variable decision evaluated on the samples)
# Test = True <=> p-value > p-value threshold
print("Smirnov=", HypothesisTest.Smirnov(sampleY, sampleZ, 0.90))
# Spearman Test : test if two samples have a monotonous relation
# H0 = no monotonous relation between both samples
# Test = True <=> no monotonous relation
# p-value threshold : probability of the H0 reject zone : 1-0.90
# p-value : probability (test variable decision > test variable decision evaluated on the samples)
# Test = True <=> p-value > p-value threshold
print("Spearman=", HypothesisTest.Spearman(sampleY, sampleZ, 0.90))
except:
import sys
print("t_HypothesisTest_std.py", sys.exc_info()[0], sys.exc_info()[1])
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