DOE: Improve doc

lib/src/Base/Func/SymbolicParserExprTk.cxx

@@ -229,12 +229,14 @@ SymbolicParserExprTk::ExpressionCollection SymbolicParserExprTk::allocateExpress
   for (UnsignedInteger inputIndex = 0; inputIndex < inputDimension; ++ inputIndex)
   {
     if (!symbol_table.add_variable(inputVariablesNames_[inputIndex], stack[inputIndex]))
-      throw InvalidArgumentException(HERE) << "Invalid input variable: " << inputVariablesNames_[inputIndex];
+      throw InvalidArgumentException(HERE) << "Invalid input variable: '" 
+        << inputVariablesNames_[inputIndex] << "' at index: " << inputIndex;
   }
   for (UnsignedInteger outputIndex = 0; outputIndex < outputDimension; ++ outputIndex)
   {
     if (!symbol_table.add_variable(outputVariablesNames_[outputIndex], stack[inputDimension + outputIndex]))
-      throw InvalidArgumentException(HERE) << "Invalid output variable: " << outputVariablesNames_[outputIndex];
+      throw InvalidArgumentException(HERE) << "Invalid output variable: '" 
+        << outputVariablesNames_[outputIndex] << "' at index: " << outputIndex;
   }
   exprtk::parser<Scalar> parser;
   parser.settings().set_max_stack_depth(ResourceMap::GetAsUnsignedInteger("SymbolicParserExprTk-MaxStackDepth"));
@@ -246,7 +248,9 @@ SymbolicParserExprTk::ExpressionCollection SymbolicParserExprTk::allocateExpress
     expression.register_symbol_table(symbol_table);
     if (!parser.compile(formulas_[outputIndex], expression))
     {
-      throw InvalidArgumentException(HERE) << "Errors found when parsing expression '" << formulas_[outputIndex] << "': " << parser.error();
+      throw InvalidArgumentException(HERE) << "Errors found when parsing expression '" 
+        << formulas_[outputIndex] << "' at index: " 
+        << outputIndex << ": " << parser.error();
     }
     expressions[outputIndex] = new exprtk::expression<Scalar>(expression);
   }

lib/src/Base/Func/SymbolicParserImplementation.cxx

@@ -59,10 +59,10 @@ void SymbolicParserImplementation::setVariables(const Description & inputVariabl
   {
     const String varName(inputVariablesNames[i]);
     if (!std::regex_match(inputVariablesNames[i], std::regex("[a-zA-Z][0-9a-zA-Z_]*")))
-      throw InvalidArgumentException(HERE) << "Invalid input variable: " << varName;
+      throw InvalidArgumentException(HERE) << "Invalid input variable: '" << varName << "' at index:" << i;
     ++ varMap[varName];
     if (varMap[varName] > 1)
-      throw InvalidArgumentException(HERE) << "Duplicate input variable: " << varName;
+      throw InvalidArgumentException(HERE) << "Duplicate input variable: '" << varName << "' at index:" << i;
   }
   inputVariablesNames_ = inputVariablesNames;
 }

lib/src/Uncertainty/Algorithm/WeightedExperiments/WeightedExperimentImplementation.cxx

@@ -1,6 +1,6 @@
 //                                               -*- C++ -*-
 /**
- *  @brief Abstract top-level view of an weightedExperiment plane
+ *  @brief Abstract top-level view of a WeightedExperiment
  *
  *  Copyright 2005-2024 Airbus-EDF-IMACS-ONERA-Phimeca
  *

lib/src/Uncertainty/Algorithm/WeightedExperiments/openturns/WeightedExperimentImplementation.hxx

@@ -1,6 +1,6 @@
 //                                               -*- C++ -*-
 /**
- *  @brief Abstract top-level view of an weightedExperiment plane
+ *  @brief Abstract top-level view of a WeightedExperiment
  *
  *  Copyright 2005-2024 Airbus-EDF-IMACS-ONERA-Phimeca
  *

lib/test/CMakeLists.txt

@@ -586,7 +586,7 @@ ot_check_test (GaussProductExperiment_std IGNOREOUT)
 ot_check_test (ImportanceSamplingExperiment_std)
 ot_check_test (LHSExperiment_std)
 ot_check_test (LowDiscrepancyExperiment_std)
-ot_check_test (MonteCarloExperiment_std)
+ot_check_test (MonteCarloExperiment_std IGNOREOUT)
 ot_check_test (TensorProductExperiment_std IGNOREOUT)
 ot_check_test (SmolyakExperiment_std IGNOREOUT)
 

lib/test/t_MonteCarloExperiment_std.cxx

@@ -30,22 +30,25 @@ int main(int, char *[])
   OStream fullprint(std::cout);
   setRandomGenerator();
 
-  try
-  {
-    Normal distribution(4);
-    UnsignedInteger size = 10;
-    MonteCarloExperiment experiment(distribution, size);
-    fullprint << "experiment = " << experiment << std::endl;
-    Point weights(0);
-    Sample sample(experiment.generateWithWeights(weights));
-    fullprint << "sample = " << sample << std::endl;
-    fullprint << "weights = " << weights << std::endl;
-  }
-  catch (TestFailed & ex)
-  {
-    std::cerr << ex << std::endl;
-    return ExitCode::Error;
-  }
+  fullprint << "Test generateWithWeights()" << std::endl;
+  const Normal distribution(4);
+  const UnsignedInteger size = 100000;
+  const MonteCarloExperiment experiment(distribution, size);
+  fullprint << "experiment = " << experiment << std::endl;
+  Point weights(0);
+  const Sample sample(experiment.generateWithWeights(weights));
+  assert(sample.getSize() == size);
+  assert(sample.getDimension() == 4);
+  assert(weights.getDimension() == size);
+  const Scalar atol = 10.0 / std::sqrt(size);
+  const Scalar rtol = 0.0;
+  const Point meanExact(distribution.getMean());
+  assert_almost_equal(sample.computeMean(), meanExact, rtol, atol);
+  const CovarianceMatrix covarianceExact(distribution.getCovariance());
+  assert_almost_equal(sample.computeCovariance(), covarianceExact, rtol, atol);
+  const Point weightsExact(size, 1.0 / size);
+  assert_almost_equal(weights, weightsExact, 1.e-15);
+
 
   return ExitCode::Success;
 }

python/src/FunctionalChaosAlgorithm_doc.i.in

@@ -19,11 +19,11 @@ Available constructors:
 Parameters
 ----------
 inputSample: 2-d sequence of float
-    Sample of the input random vectors with size :math:`n` and dimension :math:`n_X`.
+    Sample of the input random vectors with size :math:`\sampleSize` and dimension :math:`\inputDim`.
 outputSample: 2-d sequence of float
-    Sample of the output random vectors with size :math:`n` and dimension :math:`n_Y`.
+    Sample of the output random vectors with size :math:`\sampleSize` and dimension :math:`\outputDim`.
 distribution : :class:`~openturns.Distribution`
-    Distribution of the random vector :math:`\vect{X}` of dimension :math:`n_X`.
+    Distribution of the random vector :math:`\inputRV` of dimension :math:`\inputDim`.
     When the *distribution* is unspecified, the
     :meth:`~openturns.MetaModelAlgorithm.BuildDistribution` static
     method is evaluated on the *inputSample*.
@@ -32,9 +32,9 @@ adaptiveStrategy : :class:`~openturns.AdaptiveStrategy`
 projectionStrategy : :class:`~openturns.ProjectionStrategy`
     Strategy of evaluation of the coefficients :math:`a_k`
 weights : sequence of float
-    Weights :math:`(w_i)_{i = 1, ..., n}` associated to the output
-    sample, where :math:`n` is the sample size.
-    Default values are :math:`w_i = \frac{1}{n}` for :math:`i = 1, ..., n`. 
+    Weights :math:`(w_i)_{i = 1, ..., \sampleSize}` associated to the output
+    sample, where :math:`\sampleSize` is the sample size.
+    Default values are :math:`w_i = \frac{1}{\sampleSize}` for :math:`i = 1, ..., \sampleSize`. 
 
 See also
 --------

python/src/MonteCarloExperiment_doc.i.in

@@ -12,11 +12,11 @@ distribution : :class:`~openturns.Distribution`
     Distribution :math:`\mu` with an independent copula used to generate the
     set of input data.
 size : positive int
-    Number :math:`cardI` of points that will be generated in the experiment.
+    Number :math:`\sampleSize` of points that will be generated in the experiment.
 
 Notes
 -----
-MonteCarloExperiment is a random weighted design of experiments ([hammersley1961]_ page 51, [lemieux2009]_ page 3).
+MonteCarloExperiment is a random weighted design of experiments (see [hammersley1961]_ page 51, [lemieux2009]_ page 3).
 The :meth:`generate` method computes the nodes :math:`(\inputReal_i)_{i = 1, ..., \sampleSize}`
 by generating independent observations from the distribution :math:`\mu`. The weights associated to the
 points are all equal to :math:`w_i = \frac{1}{\sampleSize}` where :math:`\sampleSize` is the sample size.