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#! /usr/bin/env python
from __future__ import print_function
from openturns import *
from math import *
TESTPREAMBLE()
RandomGenerator.SetSeed(0)
try:
sample = NumericalSample(4, 3)
sample[0] = NumericalPoint((1, 0, 9))
sample[1] = NumericalPoint((2, 3, 5))
sample[2] = NumericalPoint((5, 1, 8))
sample[3] = NumericalPoint((6, 7, 2))
print("sample=", repr(sample))
print("min=", repr(sample.getMin()))
print("max=", repr(sample.getMax()))
print("mean=", repr(sample.computeMean()))
print("covariance=", repr(sample.computeCovariance()))
print("standard deviation=", repr(sample.computeStandardDeviation()))
print("standard deviation per component=", repr(
sample.computeStandardDeviationPerComponent()))
print("Pearson correlation=", repr(sample.computePearsonCorrelation()))
print("Spearman correlation=", repr(sample.computeSpearmanCorrelation()))
print("Kendall tau=", repr(sample.computeKendallTau()))
print("range per component=", repr(sample.computeRange()))
print("median per component=", repr(sample.computeMedian()))
print("Variance=", repr(sample.computeVariance()))
print("Skewness=", repr(sample.computeSkewness()))
print("Kurtosis=", repr(sample.computeKurtosis()))
for i in range(10):
print("Centered moment of order ", i, "=",
sample.computeCenteredMoment(i))
print("Marginal 1=", repr(sample.getMarginal(1)))
indices = Indices(2)
indices[0] = 2
indices[1] = 0
print("Marginal [2, 0]=", repr(sample.getMarginal(indices)))
print("Rank=", repr(sample.rank()))
print("Sort=", repr(sample.sort()))
print("Sort according to component 0=", repr(
sample.sortAccordingToAComponent(0)))
prob = 0.25
print("Quantile per component(", prob, ")=", repr(
sample.computeQuantilePerComponent(prob)))
pointCDF = NumericalPoint(sample.getDimension(), 0.25)
print(
"Empirical CDF(", repr(pointCDF), "=", sample.computeEmpiricalCDF(pointCDF))
dim = 3
R = CorrelationMatrix(dim)
for i in range(1, dim):
R[i, i - 1] = 0.25
Rtmp = CorrelationMatrix(dim)
for i in range(dim):
for j in range(i):
Rtmp[i, j] = 6.0 * asin(R[i, j] / 2.0) / pi
print("Pearson correlation (exact)=", repr(R))
print("Spearman correlation (exact)=", repr(Rtmp))
sample[0] = NumericalPoint((1, 0, 9))
sample[1] = NumericalPoint((2, 3, 9))
sample[2] = NumericalPoint((5, 1, 9))
sample[3] = NumericalPoint((2, 2, 9))
print("Rank=", repr(sample.rank()))
print("Rank component 0=", repr(sample.rank(0)))
print("Sort=", repr(sample.sort()))
print("Sort according to component 0=", repr(
sample.sortAccordingToAComponent(0)))
print("Spearman correlation=", repr(sample.computeSpearmanCorrelation()))
print("Kendall tau=", repr(sample.computeKendallTau()))
size = 10000
normal = Normal(NumericalPoint(dim, 0.0), NumericalPoint(dim, 1.0), R)
print("Normal=", repr(normal))
print("covariance=", repr(normal.getCovariance()))
normalSample = normal.getSample(size)
print("Empirical covariance=", repr(normalSample.computeCovariance()))
RPearson = normalSample.computePearsonCorrelation()
print("Pearson correlation=", repr(RPearson))
RSpearman = normalSample.computeSpearmanCorrelation()
print("Spearman correlation=", repr(RSpearman))
# except TestFailed, ex :
except:
import sys
print("t_NumericalSample_computation.py",
sys.exc_info()[0], sys.exc_info()[1])
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