text: /* file: gbt_regression_training_input.cpp */
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  /*
  //++
  //  Implementation of gradient boosted trees algorithm classes.
  //--
  */
  
  #include "algorithms/gradient_boosted_trees/gbt_regression_training_types.h"
  #include "daal_strings.h"
  
  using namespace daal::data_management;
  using namespace daal::services;
  
  namespace daal
  {
  namespace algorithms
  {
  namespace gbt
  {
  namespace training { Status checkImpl(const gbt::training::Parameter& prm); }
  
  namespace regression
  {
  namespace training
  {
  namespace interface1
  {
  
  Parameter::Parameter() : loss(squared){}
  Status Parameter::check() const
  {
      return gbt::training::checkImpl(*this);
  }
  
  /** Default constructor */
  Input::Input() : algorithms::regression::training::Input(lastInputId + 1) {}
  
  /**
   * Returns an input object for gradient boosted trees model-based training
   * \param[in] id    Identifier of the input object
   * \return          %Input object that corresponds to the given identifier
   */
  NumericTablePtr Input::get(InputId id) const
  {
      return algorithms::regression::training::Input::get(algorithms::regression::training::InputId(id));
  }
  
  /**
   * Sets an input object for gradient boosted trees model-based training
   * \param[in] id      Identifier of the input object
   * \param[in] value   Pointer to the object
   */
  void Input::set(InputId id, const NumericTablePtr &value)
  {
      algorithms::regression::training::Input::set(algorithms::regression::training::InputId(id), value);
  }
  
  /**
  * Checks an input object for the gradient boosted trees algorithm
  * \param[in] par     Algorithm parameter
  * \param[in] method  Computation method
  */
  
  Status Input::check(const daal::algorithms::Parameter *par, int method) const
  {
      Status s;
      DAAL_CHECK_STATUS(s, algorithms::regression::training::Input::check(par, method));
      NumericTablePtr dataTable = get(data);
      NumericTablePtr dependentVariableTable = get(dependentVariable);
  
      DAAL_CHECK_EX(dataTable.get(), ErrorNullInputNumericTable, ArgumentName, dataStr());
      DAAL_CHECK_EX(dependentVariableTable->getNumberOfColumns() == 1,
          ErrorIncorrectNumberOfColumns, ArgumentName, dependentVariableStr());
  
      const Parameter* parameter = static_cast<const Parameter*>(par);
      const size_t nSamplesPerTree(parameter->observationsPerTreeFraction*dataTable->getNumberOfRows());
      DAAL_CHECK_EX(nSamplesPerTree > 0,
          ErrorIncorrectParameter, ParameterName, observationsPerTreeFractionStr());
      const auto nFeatures = dataTable->getNumberOfColumns();
      DAAL_CHECK_EX(parameter->featuresPerNode <= nFeatures,
          ErrorIncorrectParameter, ParameterName, featuresPerNodeStr());
      return s;
  }
  
  } // namespace interface1
  } // namespace training
  } // namespace regression
  } // namespace gbt
  } // namespace algorithms
  } // namespace daal
  