/usr/include/openturns/swig/FittingTest_doc.i is in libopenturns-dev 1.9-5.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 | %feature("docstring") OT::FittingTest::BestModelBIC
"Select the best model according to the Bayesian information criterion.
Parameters
----------
sample : 2-d sequence of float
Tested sample.
models : list of :class:`~openturns.Distribution` or :class:`~openturns.DistributionFactory`
Tested distributions.
Returns
-------
best_model : :class:`~openturns.Distribution`
The best distribution for the sample according to Bayesian information
criterion.
This may raise a warning if the best model does not perform well.
See Also
--------
FittingTest_BIC
Examples
--------
>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> distribution = ot.Normal()
>>> sample = distribution.getSample(30)
>>> tested_distributions = [ot.ExponentialFactory(), ot.NormalFactory()]
>>> best_model = ot.FittingTest.BestModelBIC(sample, tested_distributions)
>>> print(best_model)
Normal(mu = -0.0944924, sigma = 0.989808)"
// ---------------------------------------------------------------------
%feature("docstring") OT::FittingTest::BestModelChiSquared
"Select the best model according to the :math:`\\\\chi^2` goodness-of-fit test.
Parameters
----------
sample : 2-d sequence of float
Tested sample.
models : list of :class:`~openturns.Distribution` or :class:`~openturns.DistributionFactory`
Tested distributions.
Returns
-------
best_model : :class:`~openturns.Distribution`
The best distribution for the sample according to Bayesian information
criterion.
This may raise a warning if the best model does not perform well.
best_result : :class:`~openturns.TestResult`
Best test result.
See Also
--------
FittingTest_ChiSquared
Examples
--------
>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> distribution = ot.Poisson()
>>> sample = distribution.getSample(30)
>>> tested_distributions = [ot.PoissonFactory(), ot.UserDefinedFactory()]
>>> best_model = ot.FittingTest.BestModelBIC(sample, tested_distributions)
>>> print(best_model)
Poisson(lambda = 1.06667)"
// ---------------------------------------------------------------------
%feature("docstring") OT::FittingTest::BestModelKolmogorov
"Select the best model according to the Kolmogorov goodness-of-fit test.
Parameters
----------
sample : 2-d sequence of float
Tested sample.
models : list of :class:`~openturns.Distribution` or :class:`~openturns.DistributionFactory`
Tested distributions.
Returns
-------
best_model : :class:`~openturns.Distribution`
The best distribution for the sample according to Bayesian information
criterion.
This may raise a warning if the best model does not perform well.
best_result : :class:`~openturns.TestResult`
Best test result.
See Also
--------
FittingTest_Kolmogorov
Examples
--------
>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> distribution = ot.Normal()
>>> sample = distribution.getSample(30)
>>> tested_distributions = [ot.ExponentialFactory(), ot.NormalFactory()]
>>> best_model, best_result = ot.FittingTest.BestModelKolmogorov(sample, tested_distributions)
>>> print(best_model)
Normal(mu = -0.0944924, sigma = 0.989808)"
// ---------------------------------------------------------------------
%feature("docstring") OT::FittingTest::BIC
"Compute the Bayesian information criterion.
Parameters
----------
sample : 2-d sequence of float
Tested sample.
model : :class:`~openturns.Distribution` or :class:`~openturns.DistributionFactory`
Tested distribution.
n_parameters : int, :math:`0 \\\\leq k`, optional
The number of parameters in the distribution that have been estimated from
the sample.
This parameter must not be provided if a :class:`~openturns.DistributionFactory`
was provided as the second argument (it will internally be set to the
number of parameters estimated by the :class:`~openturns.DistributionFactory`).
It can be specified if a :class:`~openturns.Distribution` was provided
as the second argument, but if it is not, it will be set equal to 0.
Returns
-------
BIC : float
The Bayesian information criterion.
Notes
-----
The Bayesian information criterion is defined as follows:
.. math::
{\\\\rm BIC} = \\\\frac{1}{m}
\\\\left(- 2 \\\\log L(\\\\vect{x}^{(i)}, i = 1, \\\\ldots, m)
+ k \\\\log m\\\\right)
where :math:`\\\\log L` denotes the log-likelihood of the sample with respect to
the given distribution, and :math:`k` denotes the number of estimated
parameters in the distribution.
This is used for model selection.
Examples
--------
>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> distribution = ot.Normal()
>>> sample = distribution.getSample(30)
>>> ot.FittingTest.BIC(sample, distribution)
2.7938693005063415
>>> ot.FittingTest.BIC(sample, distribution, 2)
3.0206157926171517
>>> ot.FittingTest.BIC(sample, ot.NormalFactory())
3.0108025506670955"
// ---------------------------------------------------------------------
%feature("docstring") OT::FittingTest::ChiSquared
"Perform a :math:`\\\\chi^2` goodness-of-fit test for 1-d discrete distributions.
Parameters
----------
sample : 2-d sequence of float
Tested sample.
model : :class:`~openturns.Distribution` or :class:`~openturns.DistributionFactory`
Tested distribution.
level : float, :math:`0 \\\\leq {\\\\rm level} \\\\leq 1`, optional
This the value such that :math:`\\\\alpha = 1 - {\\\\rm level}` is the risk of
committing a Type I error, that is an incorrect rejection of a true
null hypothesis.
n_parameters : int, :math:`0 \\\\leq k`, optional
The number of parameters in the distribution that have been estimated from
the sample.
This parameter must not be provided if a :class:`~openturns.DistributionFactory`
was provided as the second argument (it will internally be set to the
number of parameters estimated by the :class:`~openturns.DistributionFactory`).
It can be specified if a :class:`~openturns.Distribution` was provided
as the second argument, but if it is not, it will be set equal to 0.
Returns
-------
test_result : :class:`~openturns.TestResult`
Test result.
Raises
------
TypeError : If the distribution is not discrete or if the sample is
multivariate.
Notes
-----
This is an interface to the `chisq.test function from the
'stats' R package <http://stat.ethz.ch/R-manual/R-patched/library/stats/html/chisq.test.html>`_.
Examples
--------
>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> distribution = ot.Poisson()
>>> sample = distribution.getSample(30)
>>> ot.FittingTest.ChiSquared(sample, ot.PoissonFactory(), .99)
class=TestResult name=Unnamed type=ChiSquaredPoisson binaryQualityMeasure=true p-value threshold=0.01 p-value=0.606136 description=[]"
// ---------------------------------------------------------------------
%feature("docstring") OT::FittingTest::Kolmogorov
"Perform a Kolmogorov goodness-of-fit test for 1-d continuous distributions.
Parameters
----------
sample : 2-d sequence of float
Tested sample.
model : :class:`~openturns.Distribution` or :class:`~openturns.DistributionFactory`
Tested distribution.
level : float, :math:`0 \\\\leq {\\\\rm level} \\\\leq 1`, optional
This is the value such that :math:`\\\\alpha = 1 - {\\\\rm level}` is the risk of
committing a Type I error, that is an incorrect rejection of a true
null hypothesis.
n_parameters : int, :math:`0 \\\\leq k`, optional
The number of parameters in the distribution that have been estimated from
the sample.
This parameter must not be provided if a :class:`~openturns.DistributionFactory`
was provided as the second argument (it will internally be set to the
number of parameters estimated by the :class:`~openturns.DistributionFactory`).
It can be specified if a :class:`~openturns.Distribution` was provided
as the second argument, but if it is not, it will be set equal to 0.
Returns
-------
test_result : :class:`~openturns.TestResult`
Test result.
Raises
------
TypeError : If the distribution is not continuous or if the sample is
multivariate.
Notes
-----
The present implementation of the Kolmogorov goodness-of-fit test is
two-sided. This uses an external C implementation of the Kolmogorov cumulative
distribution function by [Simard2011]_.
Examples
--------
>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> distribution = ot.Normal()
>>> sample = distribution.getSample(30)
>>> ot.FittingTest.Kolmogorov(sample, ot.NormalFactory(), .99)
class=TestResult name=Unnamed type=KolmogorovDistribution binaryQualityMeasure=true p-value threshold=0.01 p-value=0.846896 description=[Normal(mu = -0.0944924, sigma = 0.989808) vs sample Normal]"
// ---------------------------------------------------------------------
%feature("docstring") OT::FittingTest::TwoSamplesKolmogorov
"Perform a Kolmogorov goodness-of-fit test on two samples.
If the p-value is high, then we cannot reject the hypothesis that the
distributions of the two samples are the same.
Parameters
----------
sample1 : 2-d float array
A continuous distribution sample.
sample2 : 2-d float array
Another continuous distribution sample, can be of different size.
level : float, :math:`0 \\\\leq {\\\\rm level} \\\\leq 1`, optional
This the value such that :math:`\\\\alpha = 1 - {\\\\rm level}` is the risk of
committing a Type I error, that is an incorrect rejection of a true
null hypothesis.
Returns
-------
test_result : :class:`~openturns.TestResult`
Test result.
Examples
--------
>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> sample1 = ot.Normal().getSample(20)
>>> sample2 = ot.Normal(0.1, 1.1).getSample(30)
>>> ot.FittingTest.TwoSamplesKolmogorov(sample1, sample2)
class=TestResult name=Unnamed type=Kolmogorov Normal/Normal binaryQualityMeasure=true p-value threshold=0.05 p-value=0.554765 description=[sampleNormal vs sample Normal]"
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