text: #*******************************************************************************
  # Copyright 2014-2019 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 tanh 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=None, 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=None, t=np.float64: np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)
  
  
  def main(readcsv=read_csv, method='defaultDense'):
      infile = "./data/batch/covcormoments_dense.csv"
  
      # configure a covariance object
      algo = d4p.math_tanh()
      
      # let's provide a file directly, not a table/array
      result1 = algo.compute(infile)
  
      # We can also load the data ourselfs and provide the numpy array
      algo = d4p.math_tanh(method=method)
      data = readcsv(infile)
      result2 = algo.compute(data)
  
      # covariance result objects provide correlation, covariance and mean
      assert np.allclose(result1.value, result2.value)
      assert np.allclose(result1.value, np.tanh(data.toarray() if hasattr(data, 'toarray') else data))
  
      return result1
  
  
  if __name__ == "__main__":
      res = main()
      print("TanH result (first 5 rows):\n", res.value[:5])
      print('All looks good!')
  