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|
testCases= [[class=Uniform name=Uniform dimension=1 a=-1 b=3,class=Uniform name=Uniform dimension=1 a=-1 b=3], [class=Normal name=Normal dimension=1 mean=class=NumericalPoint name=Unnamed dimension=1 values=[0] sigma=class=NumericalPoint name=Unnamed dimension=1 values=[1] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1],class=Normal name=Normal dimension=1 mean=class=NumericalPoint name=Unnamed dimension=1 values=[1] sigma=class=NumericalPoint name=Unnamed dimension=1 values=[2] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1],class=Normal name=Normal dimension=1 mean=class=NumericalPoint name=Unnamed dimension=1 values=[-2] sigma=class=NumericalPoint name=Unnamed dimension=1 values=[2] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]], [class=Exponential name=Exponential dimension=1 lambda=1 gamma=0,class=Exponential name=Exponential dimension=1 lambda=1 gamma=0,class=Exponential name=Exponential dimension=1 lambda=1 gamma=0]]
references= [Triangular(a = -2, m = 2, b = 6),Normal(mu = -1, sigma = 3),Gamma(k = 3, lambda = 1, gamma = 0)]
Distribution class=RandomMixture name=RandomMixture distribution collection=[class=Triangular name=Triangular dimension=1 a=-2 m=2 b=6] weights =class=Matrix implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1] constant=class=NumericalPoint name=Unnamed dimension=1 values=[0]
Distribution RandomMixture(Triangular(a = -2, m = 2, b = 6))
Elliptical = False
Continuous = True
oneRealization= [2.5585]
oneSample first= [4.06345] last= [1.68661]
mean= [2.01813]
covariance= [[ 2.72012 ]]
Point= [0.5]
ddf = [0.0625]
ddf (ref)= [0.0625]
pdf =0.156250
pdf (FD)=0.156250
pdf (ref)=0.156250
cdf =0.195312
cdf (ref)=0.195312
characteristic function=0.382574 + 0.595823i
log characteristic function=-0.345207 + 1.000000i
quantile = [4.73509]
quantile (ref)= [4.73509]
cdf(quantile)=0.950000
mean = [2]
mean (ref)= [2]
standard deviation = [1.63299]
standard deviation (ref)= [1.63299]
skewness = [0]
skewness (ref)= [0]
kurtosis = [2.4]
kurtosis (ref)= [2.4]
covariance = [[ 2.66667 ]]
covariance (ref)= [[ 2.66667 ]]
parameters= [[Triangular_a : -2, Triangular_m : 2, Triangular_b : 6]]
Standard representative= RandomMixture(Triangular(a = -2, m = 2, b = 6))
blockMin= 5
blockMax= 20
maxSize= 4000000
alpha= 5.0
beta= 8.5
Distribution class=RandomMixture name=RandomMixture distribution collection=[class=Normal name=Normal dimension=1 mean=class=NumericalPoint name=Unnamed dimension=1 values=[-1] sigma=class=NumericalPoint name=Unnamed dimension=1 values=[3] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]] weights =class=Matrix implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1] constant=class=NumericalPoint name=Unnamed dimension=1 values=[0]
Distribution RandomMixture(Normal(mu = -1, sigma = 3))
Elliptical = True
Continuous = True
oneRealization= [-3.87153]
oneSample first= [-4.03363] last= [0.331499]
mean= [-1.0314]
covariance= [[ 8.91043 ]]
Point= [0.5]
ddf = [-0.0195592]
ddf (ref)= [-0.0195592]
pdf =0.117355
pdf (FD)=0.117355
pdf (ref)=0.117355
cdf =0.691462
cdf (ref)=0.691462
characteristic function=0.284909 + -0.155647i
log characteristic function=-1.125000 + -0.500000i
quantile = [3.93456]
quantile (ref)= [3.93456]
cdf(quantile)=0.950000
mean = [-1]
mean (ref)= [-1]
standard deviation = [3]
standard deviation (ref)= [3]
skewness = [0]
skewness (ref)= [0]
kurtosis = [3]
kurtosis (ref)= [3]
covariance = [[ 9 ]]
covariance (ref)= [[ 9 ]]
parameters= [[Normal_mean_0 : -1, Normal_standard_deviation_0 : 3]]
Standard representative= RandomMixture(Normal(mu = -1, sigma = 3))
blockMin= 5
blockMax= 20
maxSize= 4000000
alpha= 5.0
beta= 8.5
Distribution class=RandomMixture name=RandomMixture distribution collection=[class=Exponential name=Exponential dimension=1 lambda=1 gamma=0,class=Exponential name=Exponential dimension=1 lambda=1 gamma=0,class=Exponential name=Exponential dimension=1 lambda=1 gamma=0] weights =class=Matrix implementation=class=MatrixImplementation name=Unnamed rows=1 columns=3 values=[1,1,1] constant=class=NumericalPoint name=Unnamed dimension=1 values=[0]
Distribution RandomMixture(Exponential(lambda = 1, gamma = 0) + Exponential(lambda = 1, gamma = 0) + Exponential(lambda = 1, gamma = 0))
Elliptical = False
Continuous = True
oneRealization= [0.665902]
oneSample first= [5.24685] last= [5.44861]
mean= [2.9992]
covariance= [[ 2.9295 ]]
Point= [0.5]
ddf = [0.227449]
ddf (ref)= [0.227449]
pdf =0.075816
pdf (FD)=0.075816
pdf (ref)=0.075816
cdf =0.014388
cdf (ref)=0.014388
characteristic function=0.128000 + 0.704000i
log characteristic function=-0.334715 + 1.390943i
quantile = [6.2957]
quantile (ref)= [6.29579]
cdf(quantile)=0.950000
mean = [3]
mean (ref)= [3]
standard deviation = [1.73205]
standard deviation (ref)= [1.73205]
skewness = [1.1547]
skewness (ref)= [1.1547]
kurtosis = [5]
kurtosis (ref)= [5]
covariance = [[ 3 ]]
covariance (ref)= [[ 3 ]]
parameters= [[Exponential_lambda : 1, Exponential_gamma : 0, Exponential_lambda : 1, Exponential_gamma : 0, Exponential_lambda : 1, Exponential_gamma : 0]]
Standard representative= RandomMixture(Exponential(lambda = 1, gamma = 0) + Exponential(lambda = 1, gamma = 0) + Exponential(lambda = 1, gamma = 0))
blockMin= 5
blockMax= 20
maxSize= 4000000
alpha= 5.0
beta= 8.5
distribution= class=RandomMixture name=RandomMixture distribution collection=[class=Triangular name=Triangular dimension=1 a=2 m=6 b=10,class=Exponential name=Exponential dimension=1 lambda=2 gamma=-3,class=Triangular name=Triangular dimension=1 a=2 m=6 b=10,class=Exponential name=Exponential dimension=1 lambda=2 gamma=-3,class=Gamma name=Gamma dimension=1 k=3 lambda=4 gamma=-2,class=Normal name=Normal dimension=1 mean=class=NumericalPoint name=Unnamed dimension=1 values=[5.25] sigma=class=NumericalPoint name=Unnamed dimension=1 values=[13.4629] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]] weights =class=Matrix implementation=class=MatrixImplementation name=Unnamed rows=1 columns=6 values=[1,1.5,-2.5,-3.75,2.5,1] constant=class=NumericalPoint name=Unnamed dimension=1 values=[0]
distribution= RandomMixture(Triangular(a = 2, m = 6, b = 10) + 1.5 * Exponential(lambda = 2, gamma = -3) - 2.5 * Triangular(a = 2, m = 6, b = 10) - 3.75 * Exponential(lambda = 2, gamma = -3) + 2.5 * Gamma(k = 3, lambda = 4, gamma = -2) + Normal(mu = 5.25, sigma = 13.4629))
pdf( -44.290678 )=0.000311
pdf( -34.726083 )=0.001834
pdf( -25.161488 )=0.006936
pdf( -15.596893 )=0.016849
pdf( -6.032298 )=0.026283
pdf( 3.532298 )=0.026313
pdf( 13.096893 )=0.016889
pdf( 22.661488 )=0.006940
pdf( 32.226083 )=0.001821
pdf( 41.790678 )=0.000305
projections= [Normal(mu = -1.24816, sigma = 24.1247),Gamma(k = 9.52102, lambda = 0.127903, gamma = -75.6876),Triangular(a = -75.6876, m = -1.15189, b = 73.095),Uniform(a = -75.6876, b = 73.095),Exponential(lambda = 0.0134337, gamma = -75.6876)]
norms= [0.122161,0.139869,0.185637,0.291551,0.444576]
distribution = RandomMixture(
Normal(mu = 0, sigma = 1) - 2 * Normal(mu = 0, sigma = 1) + Normal(mu = 0, sigma = 1)
Normal(mu = 0, sigma = 1) + Normal(mu = 0, sigma = 1) - 3 * Normal(mu = 0, sigma = 1)
)
range = [-20.8207, 20.8207]
[-28.1913, 28.1913]
mean = [0,0]
cov = [[ 6 -4 ]
[ -4 11 ]]
sigma = [2.44949,3.31662]
pdf = 0.0225079
pdf (ref)= 0.0225079
pdf = 0.0139715
pdf (ref)= 0.0139715
pdf = 0.00334166
pdf (ref)= 0.00334166
pdf = 0.000307961
pdf (ref)= 0.000307961
pdf = 1.09356e-05
pdf (ref)= 1.09356e-05
pdf = 1.49624e-07
pdf (ref)= 1.49624e-07
pdf = 0.0139715
pdf (ref)= 0.0139715
pdf = 0.00542272
pdf (ref)= 0.00542272
pdf = 0.000810972
pdf (ref)= 0.000810972
pdf = 4.67313e-05
pdf (ref)= 4.67313e-05
pdf = 1.03758e-06
pdf (ref)= 1.03758e-06
pdf = 8.8767e-09
pdf (ref)= 8.8767e-09
pdf = 0.00334166
pdf (ref)= 0.00334166
pdf = 0.000810972
pdf (ref)= 0.000810972
pdf = 7.58338e-05
pdf (ref)= 7.58338e-05
pdf = 2.73233e-06
pdf (ref)= 2.73233e-06
pdf = 3.7933e-08
pdf (ref)= 3.7933e-08
pdf = 2.02915e-10
pdf (ref)= 2.02915e-10
pdf = 0.000307961
pdf (ref)= 0.000307961
pdf = 4.67313e-05
pdf (ref)= 4.67313e-05
pdf = 2.73233e-06
pdf (ref)= 2.73233e-06
pdf = 6.15563e-08
pdf (ref)= 6.15563e-08
pdf = 5.34349e-10
pdf (ref)= 5.34349e-10
pdf = 1.78727e-12
pdf (ref)= 1.78727e-12
pdf = 1.09356e-05
pdf (ref)= 1.09356e-05
pdf = 1.03758e-06
pdf (ref)= 1.03758e-06
pdf = 3.7933e-08
pdf (ref)= 3.7933e-08
pdf = 5.34349e-10
pdf (ref)= 5.34349e-10
pdf = 2.90032e-12
pdf (ref)= 2.90032e-12
pdf = 1.49624e-07
pdf (ref)= 1.49624e-07
pdf = 8.8767e-09
pdf (ref)= 8.8767e-09
pdf = 2.02915e-10
pdf (ref)= 2.02915e-10
pdf = 1.78727e-12
pdf (ref)= 1.78727e-12
new distribution = RandomMixture(
Uniform(a = 0, b = 1) - 2 * Uniform(a = 0, b = 1) + Uniform(a = 0, b = 1)
Uniform(a = 0, b = 1) + Uniform(a = 0, b = 1) - 3 * Uniform(a = 0, b = 1)
)
range = [-2, 2]
[-3, 2]
mean = [0,-0.5]
cov = [[ 0.5 -0.333333 ]
[ -0.333333 0.916667 ]]
sigma = [0.707107,0.957427]
pdf = 0.200008
pdf = 0.199953
pdf = 0.18257
pdf = 0.161365
pdf = 0.140147
pdf = 0.11893
pdf = 0.200012
pdf = 0.194237
pdf = 0.172999
pdf = 0.151788
pdf = 0.130572
pdf = 0.109357
pdf = 0.200032
pdf = 0.184647
pdf = 0.163427
pdf = 0.142211
pdf = 0.120996
pdf = 0.099784
pdf = 0.190057
pdf = 0.175051
pdf = 0.153858
pdf = 0.132633
pdf = 0.111423
pdf = 0.0902108
pdf = 0.164538
pdf = 0.155122
pdf = 0.144316
pdf = 0.123061
pdf = 0.101846
pdf = 0.0806394
pdf = 0.13901
pdf = 0.129584
pdf = 0.120154
pdf = 0.110695
pdf = 0.0922665
pdf = 0.0710676
3D distribution = RandomMixture(
Normal(mu = 0, sigma = 1) - 0.05 * Mixture((w = 0.5, d = Normal(mu = 2, sigma = 1)), (w = 0.5, d = Normal(mu = -2, sigma = 1))) + Uniform(a = 0, b = 1) - 0.5 * Uniform(a = 0, b = 1)
0.5 * Normal(mu = 0, sigma = 1) + Mixture((w = 0.5, d = Normal(mu = 2, sigma = 1)), (w = 0.5, d = Normal(mu = -2, sigma = 1))) - 0.05 * Uniform(a = 0, b = 1) + 0.3 * Uniform(a = 0, b = 1)
-0.5 * Normal(mu = 0, sigma = 1) - 0.1 * Mixture((w = 0.5, d = Normal(mu = 2, sigma = 1)), (w = 0.5, d = Normal(mu = -2, sigma = 1))) + 1.2 * Uniform(a = 0, b = 1) - 0.8 * Uniform(a = 0, b = 1)
)
range = [-8.63316, 9.13316]
[-13.5259, 13.7759]
[-5.59038, 5.99038]
mean = [0.25,0.125,0.2]
cov = [[ 1.11667 0.233333 -0.341667 ]
[ 0.233333 5.25771 -0.775 ]
[ -0.341667 -0.775 0.473333 ]]
sigma = [1.05672,2.29297,0.687992]
pdf = 0.0128814
pdf = 0.0128655
pdf = 0.0122294
pdf = 0.00984818
pdf = 0.0234842
pdf = 0.020225
pdf = 0.0150424
pdf = 0.00949663
pdf = 0.0432697
pdf = 0.0385232
pdf = 0.0281784
pdf = 0.0180749
pdf = 0.044762
pdf = 0.0406428
pdf = 0.0308978
pdf = 0.0210441
pdf = 0.0129975
pdf = 0.0121201
pdf = 0.00968603
pdf = 0.0071769
pdf = 0.0247237
pdf = 0.0192342
pdf = 0.0131031
pdf = 0.00795833
pdf = 0.0400697
pdf = 0.0318194
pdf = 0.0226119
pdf = 0.0136759
pdf = 0.0348167
pdf = 0.0294142
pdf = 0.0218493
pdf = 0.0134862
pdf = 0.011291
pdf = 0.0091266
pdf = 0.00689789
pdf = 0.00454873
pdf = 0.0207392
pdf = 0.0154986
pdf = 0.00997757
pdf = 0.00537413
pdf = 0.0295955
pdf = 0.0230235
pdf = 0.0149557
pdf = 0.00725254
pdf = 0.0229623
pdf = 0.0183174
pdf = 0.0117906
pdf = 0.00422686
pdf = 0.00832467
pdf = 0.00628249
pdf = 0.00406557
pdf = 0.00186481
pdf = 0.0152402
pdf = 0.0104641
pdf = 0.00556431
pdf = 0.00128214
pdf = 0.0189734
pdf = 0.0129675
pdf = 0.00534378
pdf = 0.000220278
pdf = 0.0123168
pdf = 0.00770527
pdf = 0.00160947
pdf = 0
|
