#! /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])
