text: import sys
  sys.path.append('..')
  import os
  import daal.algorithms.adaboost as adaB
  from daal.algorithms.adaboost import prediction, training
  from GridSearch import GridSearch
  from daal.data_management import (
  	FileDataSource, DataSourceIface, HomogenNumericTable, MergedNumericTable, NumericTableIface
  )
  
  
  DATA_PREFIX = os.path.join(os.path.dirname(sys.executable),'share','pydaal_examples','examples','data','batch')
  trainDatasetFileName = os.path.join(DATA_PREFIX, 'adaboost_train.csv')
  
  nFeatures = 20
  
  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
  trainDataSource = FileDataSource(
  	trainDatasetFileName, DataSourceIface.notAllocateNumericTable,
  	DataSourceIface.doDictionaryFromContext
  )
  
  # Create Numeric Tables for training data and labels
  trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
  trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
  mergedData = MergedNumericTable(trainData, trainGroundTruth)
  
  # Retrieve the data from the input file
  trainDataSource.loadDataBlock(mergedData)
  
  #default keyword arguments
  '''
  GridSearch(<args>, tuned_parameters = None, score=None,
  			best_score_criteria='high',
  			create_best_training_model = False,
  			save_model=False,nClasses=None )			
  '''	
  #create a dictionary of hyperparameter values in a list
  adaB_params = [{'accuracyThreshold': [0.99,0.1],
  				'maxIterations' :[1,5]}]
  #Create GridSearch object						 
  clf = GridSearch(adaB,training,prediction, 
  				tuned_parameters = adaB_params,score=None,
  				best_score_criteria='high',
  				create_best_training_model=True,
  				save_model=True,nClasses=5)		
  #Train on all combinations of hyperparameters
  result = clf.train(trainData,trainGroundTruth)
  #view all the parameters and scores in best to worst order
  result.viewAllResults()
  #view the best parameters with score
  print(result.bestResult())
  
  
  
  