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 SGD (Stochastic Gradient Descent) example for shared memory systems
  # using Mean Squared Error objective function
  
  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'):
      infile   = "./data/batch/mse.csv"
      # Read the data, let's have 3 independent variables
      data     = readcsv(infile, range(3))
      dep_data = readcsv(infile, range(3,4))
      nVectors = data.shape[0]
  
      # configure a MSE object
      mse_algo = d4p.optimization_solver_mse(nVectors)
      mse_algo.setup(data, dep_data)
  
      # configure a SGD object
      lrs = np.array([[1.0]], dtype=np.double)
      niters = 1000
      sgd_algo = d4p.optimization_solver_sgd(mse_algo,
                                             learningRateSequence=lrs,
                                             accuracyThreshold=0.0000001,
                                             nIterations=niters)
  
      # finally do the computation
      inp = np.array([[8], [2], [1], [4]], dtype=np.double)
      res = sgd_algo.compute(inp)
  
      # The SGD result provides minimum and nIterations
      assert res.minimum.shape == inp.shape and res.nIterations[0][0] <= niters
  
      return res
  
  
  if __name__ == "__main__":
      res = main()
      print("\nMinimum:\n", res.minimum)
      print("\nNumber of iterations performed:\n", res.nIterations[0][0])
      print('All looks good!')
  