Updating copyright date information

[modules/adao.git] / doc / en / ref_algorithm_SamplingTest.rst

diff --git a/doc/en/ref_algorithm_SamplingTest.rst b/doc/en/ref_algorithm_SamplingTest.rst
index baf16fadfcc52e206186a18f6ab05efa2c8109ab..dec82063be099978976664aa70ee10fb44a44f69 100644 (file)
--- a/doc/en/ref_algorithm_SamplingTest.rst
+++ b/doc/en/ref_algorithm_SamplingTest.rst
@@ -1,5 +1,5 @@
 ..
 ..

-   Copyright (C) 2008-2015 EDF R&D
+   Copyright (C) 2008-2017 EDF R&D

 
    This file is part of SALOME ADAO module.
 
 
    This file is part of SALOME ADAO module.
 


@@ -45,7 +45,11 @@ the form of hyper-cubes, explicit or sampled using classic distributions. Be
 careful to the size of the hyper-cube (and then to the number of calculations)
 that can be reached, it can be big very quickly.
 
 careful to the size of the hyper-cube (and then to the number of calculations)
 that can be reached, it can be big very quickly.
 

-To perform distributed or complex sampling, see other modules available in
+To be visible by the user, the results of sampling has to be explicitly asked
+for. One use for that, on the desired variable, the final saving through
+"*UserPostAnalysis*" or the treatment during the calculation by "*observer*".
+
+To perform distributed or more complex sampling, see other modules available in

 SALOME : PARAMETRIC or OPENTURNS.
 
 Optional and required commands
 SALOME : PARAMETRIC or OPENTURNS.
 
 Optional and required commands


@@ -122,7 +126,7 @@ The options of the algorithm are the following:
     list of explicit sampling of each variable as a list. That is then a list of
     lists, each of them being potentially of different size.
 
     list of explicit sampling of each variable as a list. That is then a list of
     lists, each of them being potentially of different size.
 

-    Example : ``{"SampleAsExplicitHyperCube":[[0.,0.25,0.5,0.75,1.],[-2,2,1]]}`` for a state space of dimension 2
+    Example : ``{"SampleAsExplicitHyperCube":[[0.,0.25,0.5,0.75,1.], [-2,2,1]]}`` for a state space of dimension 2

 
   SampleAsMinMaxStepHyperCube
     This key describes the calculations points as an hyper-cube, from a given
 
   SampleAsMinMaxStepHyperCube
     This key describes the calculations points as an hyper-cube, from a given


@@ -142,7 +146,7 @@ The options of the algorithm are the following:
     'uniform' of parameters (low,high), or 'weibull' of parameter (shape). That
     is then a list of the same size than the one of the state.
 
     'uniform' of parameters (low,high), or 'weibull' of parameter (shape). That
     is then a list of the same size than the one of the state.
 

-    Example : ``{"SampleAsIndependantRandomVariables":[['normal',[0.,1.],3],['uniform',[-2,2],4]]`` for a state space of dimension 2
+    Example : ``{"SampleAsIndependantRandomVariables":[ ['normal',[0.,1.],3], ['uniform',[-2,2],4]]`` for a state space of dimension 2

 
   QualityCriterion
     This key indicates the quality criterion, used to find the state estimate.
 
   QualityCriterion
     This key indicates the quality criterion, used to find the state estimate.


@@ -174,11 +178,61 @@ The options of the algorithm are the following:
     available at the end of the algorithm. It involves potentially costly
     calculations or memory consumptions. The default is a void list, none of
     these variables being calculated and stored by default. The possible names
     available at the end of the algorithm. It involves potentially costly
     calculations or memory consumptions. The default is a void list, none of
     these variables being calculated and stored by default. The possible names

-    are in the following list: ["CostFunctionJ", "CurrentState", "Innovation",
+    are in the following list: ["CostFunctionJ", "CostFunctionJb",
+    "CostFunctionJo", "CurrentState", "InnovationAtCurrentState",

     "SimulatedObservationAtCurrentState"].
 
     Example : ``{"StoreSupplementaryCalculations":["CostFunctionJ", "SimulatedObservationAtCurrentState"]}``
 
     "SimulatedObservationAtCurrentState"].
 
     Example : ``{"StoreSupplementaryCalculations":["CostFunctionJ", "SimulatedObservationAtCurrentState"]}``
 

+Information and variables available at the end of the algorithm
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+At the output, after executing the algorithm, there are variables and
+information originating from the calculation. The description of
+:ref:`section_ref_output_variables` show the way to obtain them by the method
+named ``get`` of the variable "*ADD*" of the post-processing. The input
+variables, available to the user at the output in order to facilitate the
+writing of post-processing procedures, are described in the
+:ref:`subsection_r_o_v_Inventaire`.
+
+The unconditional outputs of the algorithm are the following:
+
+  CostFunctionJ
+    *List of values*. Each element is a value of the error function :math:`J`.
+
+    Example : ``J = ADD.get("CostFunctionJ")[:]``
+
+  CostFunctionJb
+    *List of values*. Each element is a value of the error function :math:`J^b`,
+    that is of the background difference part.
+
+    Example : ``Jb = ADD.get("CostFunctionJb")[:]``
+
+  CostFunctionJo
+    *List of values*. Each element is a value of the error function :math:`J^o`,
+    that is of the observation difference part.
+
+    Example : ``Jo = ADD.get("CostFunctionJo")[:]``
+
+The conditional outputs of the algorithm are the following:
+
+  CurrentState
+    *List of vectors*. Each element is a usual state vector used during the
+    optimization algorithm procedure.
+
+    Example : ``Xs = ADD.get("CurrentState")[:]``
+
+  InnovationAtCurrentState
+    *List of vectors*. Each element is an innovation vector at current state.
+
+    Example : ``ds = ADD.get("InnovationAtCurrentState")[-1]``
+
+  SimulatedObservationAtCurrentState
+    *List of vectors*. Each element is an observed vector at the current state,
+    that is, in the observation space.
+
+    Example : ``hxs = ADD.get("SimulatedObservationAtCurrentState")[-1]``
+

 See also
 ++++++++
 
 See also
 ++++++++