text: #*******************************************************************************
  # Copyright 2014-2020 Intel Corporation
  # All Rights Reserved.
  #
  # This software is licensed under the Apache License, Version 2.0 (the
  # "License"), the following terms apply:
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  # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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  #*******************************************************************************
  
  # daal4py Naive Bayes Classification example for shared memory systems
  
  import daal4py as d4p
  import numpy as np
  
  # let's try to use pandas' fast csv reader
  try:
      import pandas
      read_csv = lambda f, c, t=np.float64: pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
  except:
      # fall back to numpy loadtxt
      read_csv = lambda f, c, t=np.float64: np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)
  
  
  def main(readcsv=read_csv, method='defaultDense'):
      # input data file
      infile = "./data/batch/naivebayes_train_dense.csv"
      testfile = "./data/batch/naivebayes_test_dense.csv"
  
      # Configure a training object (20 classes)
      talgo = d4p.multinomial_naive_bayes_training(20, method=method)
      
      # Read data. Let's use 20 features per observation
      data   = readcsv(infile, range(20))
      labels = readcsv(infile, range(20,21))
      tresult = talgo.compute(data, labels)
  
      # Now let's do some prediction
      palgo = d4p.multinomial_naive_bayes_prediction(20, method=method)
      # read test data (with same #features)
      pdata = readcsv(testfile, range(20))
      plabels = readcsv(testfile, range(20,21))
      # now predict using the model from the training above
      presult = palgo.compute(pdata, tresult.model)
  
      # Prediction result provides prediction
      assert(presult.prediction.shape == (pdata.shape[0], 1))
  
      return (presult, plabels)
  
  
  if __name__ == "__main__":
      (presult, plabels) = main()
      print("\nNaiveBayes classification results (first 20 observations):\n", presult.prediction[0:20])
      print("\nGround truth (first 20 observations)\n", plabels[0:20])
      print('All looks good!')
  