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:
  #
  # 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.
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  # See the License for the specific language governing permissions and
  # limitations under the License.
  #*******************************************************************************
  
  # daal4py Linear Regression example for streaming on 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, s=0, n=None, t=np.float64: pandas.read_csv(f, usecols=c, delimiter=',', header=None, skiprows=s, nrows=n, dtype=t)
  except:
      # fall back to numpy genfromtxt
      def read_csv(f, c, s=0, n=np.iinfo(np.int64).max):
          a = np.genfromtxt(f, usecols=c, delimiter=',', skip_header=s, max_rows=n)
          if a.shape[0] == 0:
              raise Exception("done")
          if a.ndim == 1:
              return a[:, np.newaxis]
          return a
  
  def main(readcsv=read_csv, method='defaultDense'):
      infile = "./data/batch/linear_regression_train.csv"
      testfile = "./data/batch/linear_regression_test.csv"
  
      # Configure a Linear regression training object for streaming
      train_algo = d4p.linear_regression_training(interceptFlag=True, streaming=True)
  
      chunk_size = 250
      lines_read = 0
      # read and feed chunk by chunk
      while True:
          # Read data in chunks
          # Let's have 10 independent, and 2 dependent variables (for each observation)
          try:
              indep_data = readcsv(infile, range(10), lines_read, chunk_size)
              dep_data   = readcsv(infile, range(10,12), lines_read, chunk_size)
          except:
              break
          # Now feed chunk
          train_algo.compute(indep_data, dep_data)
          lines_read += indep_data.shape[0]
  
      # All chunks are done, now finalize the computation
      train_result = train_algo.finalize()
          
      # Now let's do some prediction
      predict_algo = d4p.linear_regression_prediction()
      # read test data (with same #features)
      pdata = readcsv(testfile, range(10))
      ptdata = readcsv(testfile, range(10,12))
      # now predict using the model from the training above
      predict_result = predict_algo.compute(pdata, train_result.model)
  
      # The prediction result provides prediction
      assert predict_result.prediction.shape == (pdata.shape[0], dep_data.shape[1])
  
      return (train_result, predict_result, ptdata)
  
  
  if __name__ == "__main__":
      (train_result, predict_result, ptdata) = main()
      print("\nLinear Regression coefficients:\n", train_result.model.Beta)
      print("\nLinear Regression prediction results: (first 10 rows):\n", predict_result.prediction[0:10])
      print("\nGround truth (first 10 rows):\n", ptdata[0:10])
      print('All looks good!')
  