text: /* file: linear_regression_group_of_betas_batch.h */
  /*******************************************************************************
  * Copyright 2014-2020 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.
  *******************************************************************************/
  
  /*
  //++
  //  Interface of the linear regression group of betas quality metric in the batch processing mode.
  //--
  */
  
  #ifndef __LINEAR_REGRESSION_GROUP_OF_BETAS_BATCH_H__
  #define __LINEAR_REGRESSION_GROUP_OF_BETAS_BATCH_H__
  
  #include "algorithms/algorithm.h"
  #include "algorithms/linear_regression/linear_regression_group_of_betas_types.h"
  
  namespace daal
  {
  namespace algorithms
  {
  namespace linear_regression
  {
  /**
   * \brief Contains classes for computing linear regression quality metrics
   */
  namespace quality_metric
  {
  /**
   * \brief Contains classes for computing linear regression quality metrics for group of betas
   */
  namespace group_of_betas
  {
  namespace interface1
  {
  /**
   * @defgroup linear_regression_quality_metric_group_of_betas_batch Batch
   * @ingroup linear_regression_quality_metric_group_of_betas
   * @{
   */
  /**
   * <a name="DAAL-CLASS-ALGORITHMS__LINEAR_REGRESSION__QUALITY_METRIC__GROUP_OF_BETAS__BATCHCONTAINER"></a>
   *  \brief Class containing methods to compute regression quality metric
   *
   * \tparam algorithmFPType  Data type to use in intermediate computations of the quality metric, double or float
   * \tparam method           Computation method for the metric, \ref Method
   */
  template <typename algorithmFPType, Method method, CpuType cpu>
  class BatchContainer : public daal::algorithms::AnalysisContainerIface<batch>
  {
  public:
      /** Default constructor */
      BatchContainer(daal::services::Environment::env * daalEnv);
      /** Default destructor */
      virtual ~BatchContainer();
  
      /**
       * Computes the result of linear regression model-based training in the batch processing mode
       *
       * \return Status of computations
       */
      virtual services::Status compute() DAAL_C11_OVERRIDE;
  };
  
  /**
   * <a name="DAAL-CLASS-ALGORITHMS__LINEAR_REGRESSION__QUALITY_METRIC__GROUP_OF_BETAS__BATCH"></a>
   * \brief Computes the linear regression quality metric in the batch processing mode.
   *
   * \tparam algorithmFPType  Data type to use in intermediate computations of metric, double or float
   * \tparam method           Computation method for the metric, \ref Method
   *
   * \par Enumerations
   *      - \ref Method         Computation method for the metric
   *      - \ref DataInputId    Identifiers of input objects for the metric algorithm
   *      - \ref ResultId       Result identifiers for the metric algorithm
   */
  template <typename algorithmFPType = DAAL_ALGORITHM_FP_TYPE, Method method = defaultDense>
  class DAAL_EXPORT Batch : public daal::algorithms::quality_metric::Batch
  {
  public:
      typedef algorithms::linear_regression::quality_metric::group_of_betas::Input InputType;
      typedef algorithms::linear_regression::quality_metric::group_of_betas::Parameter ParameterType;
      typedef algorithms::linear_regression::quality_metric::group_of_betas::Result ResultType;
  
      InputType input;         /*!< %Input objects of the algorithm */
      ParameterType parameter; /*!< Parameters of the algorithm */
  
      /** Default constructor */
      Batch(size_t nBeta, size_t nBetaReducedModel) : parameter(nBeta, nBetaReducedModel) { initialize(); }
  
      /**
       * Constructs an algorithm by copying input objects and parameters
       * of another algorithm
       * \param[in] other An algorithm to be used as the source to initialize the input objects
       *                  and parameters of the algorithm
       */
      Batch(const Batch<algorithmFPType, method> & other) : parameter(other.parameter)
      {
          initialize();
          input.set(expectedResponses, other.input.get(expectedResponses));
          input.set(predictedResponses, other.input.get(predictedResponses));
          input.set(predictedReducedModelResponses, other.input.get(predictedReducedModelResponses));
      }
  
      /**
       * 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 results of the algorithm
       * \return Structure that contains results of the algorithm
       */
      ResultPtr getResult() const { return _result; }
  
      /**
       * Registers user-allocated memory to store results of the algorithm
       * \param[in] result  Structure to store results of the algorithm
       *
       * \return Status of computations
       */
      services::Status setResult(const ResultPtr & result)
      {
          DAAL_CHECK(result, services::ErrorNullResult)
          _result = result;
          _res    = _result.get();
          return services::Status();
      }
  
      /**
       * Registers user-allocated memory to store the input object for the algorithm
       * \param[in] other  Structure to store the input object for the algorithm
       */
      virtual void setInput(const algorithms::Input * other) DAAL_C11_OVERRIDE
      {
          const InputType * inputPtr = static_cast<const InputType *>(other);
          input.set(expectedResponses, inputPtr->get(expectedResponses));
          input.set(predictedResponses, inputPtr->get(predictedResponses));
          input.set(predictedReducedModelResponses, inputPtr->get(predictedReducedModelResponses));
      }
  
      /**
       * Returns a pointer to the newly allocated algorithm with a copy of input objects
       * and parameters of this algorithm
       * \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); }
  
      virtual services::Status allocateResult() DAAL_C11_OVERRIDE
      {
          services::Status s = _result->allocate<algorithmFPType>(&input, &parameter, (int)method);
          _res               = _result.get();
          return s;
      }
  
      virtual algorithms::ResultPtr getResultImpl() const DAAL_C11_OVERRIDE { return _result; }
  
      void initialize()
      {
          Analysis<batch>::_ac = new __DAAL_ALGORITHM_CONTAINER(batch, BatchContainer, algorithmFPType, method)(&_env);
          _in                  = &input;
          _par                 = &parameter;
          _result.reset(new ResultType());
      }
  
  private:
      ResultPtr _result;
  };
  /** @} */
  } // namespace interface1
  using interface1::BatchContainer;
  using interface1::Batch;
  
  } // namespace group_of_betas
  } // namespace quality_metric
  } // namespace linear_regression
  } // namespace algorithms
  } // namespace daal
  #endif
  