text: /* file: gbt_regression_training_batch.h */
  /*******************************************************************************
  * Copyright 2014-2019 Intel Corporation
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
  * You may obtain a copy of the License at
  *
  *     http://www.apache.org/licenses/LICENSE-2.0
  *
  * Unless required by applicable law or agreed to in writing, software
  * distributed under the License is distributed on an "AS IS" BASIS,
  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  * See the License for the specific language governing permissions and
  * limitations under the License.
  *******************************************************************************/
  
  /*
  //++
  //  Implementation of the interface for model-based training
  //  in the batch processing mode
  //--
  */
  
  #ifndef __GBT_REGRESSSION_TRAINING_BATCH_H__
  #define __GBT_REGRESSSION_TRAINING_BATCH_H__
  
  #include "algorithms/algorithm.h"
  #include "data_management/data/numeric_table.h"
  #include "services/daal_defines.h"
  #include "services/daal_memory.h"
  #include "algorithms/gradient_boosted_trees/gbt_regression_training_types.h"
  #include "algorithms/gradient_boosted_trees/gbt_regression_model.h"
  #include "algorithms/regression/regression_training_batch.h"
  
  namespace daal
  {
  namespace algorithms
  {
  namespace gbt
  {
  namespace regression
  {
  namespace training
  {
  namespace interface1
  {
  /**
   * @defgroup gbt_regression_training_batch Batch
   * @ingroup gbt_regression_training
   * @{
   */
  /**
   * <a name="DAAL-CLASS-ALGORITHMS__GBT__REGRESSION__TRAINING__BATCHCONTAINER"></a>
   * \brief Class containing methods for gradient boosted trees regression
   *        model-based training using algorithmFPType precision arithmetic
   */
  template<typename algorithmFPType, Method method, CpuType cpu>
  class BatchContainer : public TrainingContainerIface<batch>
  {
  public:
      /**
       * Constructs a container for model-based training with a specified environment
       * in the batch processing mode
       * \param[in] daalEnv   Environment object
       */
      BatchContainer(daal::services::Environment::env *daalEnv);
      /** Default destructor */
      ~BatchContainer();
      /**
       * Computes the result of model-based training in the batch processing mode
       * \return Status of computations
       */
      services::Status compute() DAAL_C11_OVERRIDE;
      services::Status setupCompute() DAAL_C11_OVERRIDE;
  };
  
  /**
   * <a name="DAAL-CLASS-ALGORITHMS__GBT__REGRESSION__TRAINING__BATCH"></a>
   * \brief Provides methods for model-based training in the batch processing mode
   * <!-- \n<a href="DAAL-REF-GBT__REGRESSION__TRAINING-ALGORITHM">gradient boosted trees algorithm description and usage models</a> -->
   *
   * \tparam algorithmFPType  Data type to use in intermediate computations for model-based training, double or float
   * \tparam method           gradient boosted trees training method, \ref Method
   *
   * \par Enumerations
   *      - \ref Method  Computation methods
   *
   * \par References
   *      - \ref gbt::regression::interface1::Model "Model" class
   *      - \ref prediction::interface1::Batch "prediction::Batch" class
   */
  template<typename algorithmFPType = DAAL_ALGORITHM_FP_TYPE, Method method = defaultDense>
  class DAAL_EXPORT Batch : public algorithms::regression::training::Batch
  {
  public:
      typedef algorithms::gbt::regression::training::Input     InputType;
      typedef algorithms::gbt::regression::training::Parameter ParameterType;
      typedef algorithms::gbt::regression::training::Result    ResultType;
  
      InputType input; /*!< %Input data structure */
  
      /** Default constructor */
      Batch();
  
      /**
       * Constructs a gradient boosted trees training algorithm by copying input objects
       * and parameters of another gradient boosted trees training algorithm in the batch processing mode
       * \param[in] other Algorithm to use as the source to initialize the input objects
       *                  and parameters of the algorithm
       */
      Batch(const Batch<algorithmFPType, method> &other);
  
      /** Destructor */
      ~Batch()
      {
          delete _par;
      }
  
      /**
      * Gets parameter of the algorithm
      * \return parameter of the algorithm
      */
      ParameterType& parameter() { return *static_cast<ParameterType*>(_par); }
  
      /**
      * Gets parameter of the algorithm
      * \return parameter of the algorithm
      */
      const ParameterType& parameter() const { return *static_cast<const ParameterType*>(_par); }
  
      /**
      * Get input objects for the algorithm
      * \return input objects of the algorithm
      */
      virtual algorithms::regression::training::Input* getInput() DAAL_C11_OVERRIDE{ return &input; }
  
      /**
       * Returns the method of the algorithm
       * \return Method of the algorithm
       */
      virtual int getMethod() const DAAL_C11_OVERRIDE { return(int)method; }
  
      /**
       * Returns the structure that contains the result of model-based training
       * \return Structure that contains the result of model-based training
       */
      ResultPtr getResult() { return ResultType::cast(_result); }
  
      /* Resets the results of the regression model-based training
       * \return Status of the operation
       */
      virtual services::Status resetResult() DAAL_C11_OVERRIDE
      {
          _result.reset(new ResultType());
          DAAL_CHECK(_result, services::ErrorNullResult)
          _res = NULL;
          return services::Status();
      }
      /**
       * Returns a pointer to a newly allocated gradient boosted trees training algorithm
       * with a copy of the input objects and parameters for this gradient boosted trees training algorithm
       * in the batch processing mode
       * \return Pointer to the newly allocated algorithm
       */
      services::SharedPtr<Batch<algorithmFPType, method> > clone() const
      {
          return services::SharedPtr<Batch<algorithmFPType, method> >(cloneImpl());
      }
  
  protected:
  
      virtual Batch<algorithmFPType, method> * cloneImpl() const DAAL_C11_OVERRIDE
      {
          return new Batch<algorithmFPType, method>(*this);
      }
  
      services::Status allocateResult() DAAL_C11_OVERRIDE
      {
          services::Status s = getResult()->template allocate<algorithmFPType>(&input, &parameter(), method);
          _res = _result.get();
          return s;
      }
  
      void initialize()
      {
          _ac  = new __DAAL_ALGORITHM_CONTAINER(batch, BatchContainer, algorithmFPType, method)(&_env);
          _in = &input;
          _result.reset(new ResultType());
      }
  };
  /** @} */
  } // namespace interface1
  using interface1::BatchContainer;
  using interface1::Batch;
  }
  }
  }
  }
  }
  #endif
  