#! /usr/bin/env python

from openturns import *

TESTPREAMBLE()
RandomGenerator().SetSeed(0)

try :
    size = 100
    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)

    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])
    print "LinearModelAdjustedRSquared=", LinearModelTest.LinearModelAdjustedRSquared(sampleY, sampleZ)
    print "LinearModelFisher=", LinearModelTest.LinearModelFisher(sampleY, sampleZ)
    print "LinearModelResidualMean=", LinearModelTest.LinearModelResidualMean(sampleY, sampleZ)
    print "LinearModelRSquared=", LinearModelTest.LinearModelRSquared(sampleY, sampleZ)

except :
    import sys
    print "t_LinearModelTest_std.py", sys.exc_type, sys.exc_value
