text: #*******************************************************************************
  # Copyright 2014-2018 Intel Corporation
  # All Rights Reserved.
  #
  # This software is licensed under the Apache License, Version 2.0 (the
  # "License"), the following terms apply:
  #
  # 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.
  #*******************************************************************************
  
  # daal4py logistic regression 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():
      nClasses = 2
      nFeatures = 20
  
      # read training data from file with 20 features per observation and 1 class label
      trainfile = "./data/batch/binary_cls_train.csv"
      train_data = read_csv(trainfile, range(nFeatures))
      train_labels = read_csv(trainfile, range(nFeatures, nFeatures + 1))
  
      # set parameters and train
      train_alg = d4p.logistic_regression_training(nClasses=nClasses, interceptFlag=True)
      train_result = train_alg.compute(train_data, train_labels)
  
      # read testing data from file with 20 features per observation
      testfile = "./data/batch/binary_cls_test.csv"
      predict_data = read_csv(testfile, range(nFeatures))
      predict_labels = read_csv(testfile, range(nFeatures, nFeatures + 1))
  
      # set parameters and compute predictions
      predict_alg = d4p.logistic_regression_prediction(nClasses=nClasses)
      predict_result = predict_alg.compute(predict_data, train_result.model)
  
      # the prediction result provides prediction
      assert predict_result.prediction.shape == (predict_data.shape[0], train_labels.shape[1])
  
      return (train_result, predict_result, predict_labels)
  
  
  if __name__ == "__main__":
      (train_result, predict_result, predict_labels) = main()
      print("\nLogistic Regression coefficients:\n", train_result.model.Beta)
      print("\nLogistic regression prediction results (first 10 rows):\n", predict_result.prediction[0:10])
      print("\nGround truth (first 10 rows):\n", predict_labels[0:10])
      print('All looks good!')
  