text: #===============================================================================
  # Copyright 2014-2021 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.
  #===============================================================================
  
  # 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
  
      def read_csv(f, c, t=np.float64):
          return pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
  except ImportError:
      # fall back to numpy loadtxt
      def read_csv(f, c, t=np.float64):
          return np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)
  
  
  def main(readcsv=read_csv, method='defaultDense'):
      nClasses = 5
      nFeatures = 6
  
      # read training data from file with 6 features per observation and 1 class label
      trainfile = "./data/batch/logreg_train.csv"
      train_data = readcsv(trainfile, range(nFeatures))
      train_labels = readcsv(trainfile, range(nFeatures, nFeatures + 1))
  
      # set parameters and train
      train_alg = d4p.logistic_regression_training(nClasses=nClasses,
                                                   penaltyL1=0.1,
                                                   penaltyL2=0.1,
                                                   interceptFlag=True)
      train_result = train_alg.compute(train_data, train_labels)
  
      # read testing data from file with 6 features per observation
      testfile = "./data/batch/logreg_test.csv"
      predict_data = readcsv(testfile, range(nFeatures))
  
      # set parameters and compute predictions
      predict_alg = \
          d4p.logistic_regression_prediction(
              nClasses=nClasses,
              resultsToEvaluate="computeClassLabels|computeClassProbabilities|"
              "computeClassLogProbabilities"
          )
      predict_result = predict_alg.compute(predict_data, train_result.model)
      # the prediction result provides prediction, probabilities and logProbabilities
      assert predict_result.probabilities.shape == (predict_data.shape[0], nClasses)
      assert predict_result.logProbabilities.shape == (predict_data.shape[0], nClasses)
      predict_labels = np.loadtxt(
          testfile,
          usecols=range(nFeatures, nFeatures + 1),
          delimiter=',',
          ndmin=2
      )
      assert np.count_nonzero(predict_result.prediction - predict_labels) \
             / predict_labels.shape[0] < 0.025
  
      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(
          "\nLogistic regression prediction probabilities (first 10 rows):\n",
          predict_result.probabilities[0:10]
      )
      print(
          "\nLogistic regression prediction log probabilities (first 10 rows):\n",
          predict_result.logProbabilities[0:10]
      )
      print('All looks good!')
  