text: /* file: df_regression_train_container.h */
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  /*
  //++
  //  Implementation of decision forest container.
  //--
  */
  
  #ifndef __DF_REGRESSION_TRAIN_CONTAINER_H__
  #define __DF_REGRESSION_TRAIN_CONTAINER_H__
  
  #include "kernel.h"
  #include "decision_forest_regression_training_types.h"
  #include "decision_forest_regression_training_batch.h"
  #include "df_regression_train_kernel.h"
  #include "df_regression_model_impl.h"
  #include "service_algo_utils.h"
  
  namespace daal
  {
  namespace algorithms
  {
  namespace decision_forest
  {
  namespace regression
  {
  namespace training
  {
  
  template <typename algorithmFPType, Method method, CpuType cpu>
  BatchContainer<algorithmFPType, method, cpu>::BatchContainer(daal::services::Environment::env *daalEnv)
  {
      __DAAL_INITIALIZE_KERNELS(internal::RegressionTrainBatchKernel, algorithmFPType, method);
  }
  
  template <typename algorithmFPType, Method method, CpuType cpu>
  BatchContainer<algorithmFPType, method, cpu>::~BatchContainer()
  {
      __DAAL_DEINITIALIZE_KERNELS();
  }
  
  template <typename algorithmFPType, Method method, CpuType cpu>
  services::Status BatchContainer<algorithmFPType, method, cpu>::compute()
  {
      Input *input = static_cast<Input *>(_in);
      Result *result = static_cast<Result *>(_res);
  
      const NumericTable *x = input->get(data).get();
      const NumericTable *y = input->get(dependentVariable).get();
  
      decision_forest::regression::Model *m = result->get(model).get();
  
      const Parameter *par = static_cast<decision_forest::regression::training::Parameter*>(_par);
      daal::services::Environment::env &env = *_env;
  
      __DAAL_CALL_KERNEL(env, internal::RegressionTrainBatchKernel,
          __DAAL_KERNEL_ARGUMENTS(algorithmFPType, method), compute, daal::services::internal::hostApp(*input), x, y, *m, *result, *par);
  }
  
  template <typename algorithmFPType, Method method, CpuType cpu>
  services::Status BatchContainer<algorithmFPType, method, cpu>::setupCompute()
  {
      Result *result = static_cast<Result *>(_res);
      decision_forest::regression::Model *m = result->get(model).get();
      decision_forest::regression::internal::ModelImpl* pImpl = dynamic_cast<decision_forest::regression::internal::ModelImpl*>(m);
      DAAL_ASSERT(pImpl);
      pImpl->clear();
      return services::Status();
  }
  
  }
  }
  }
  }
  }
  #endif
  