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  1. Oct 04, 2019
  2. Sep 17, 2019
    • Mikkel Friis-Møller's avatar
      new load constraint component · 50e60c2a
      Mikkel Friis-Møller authored and Riccardo Riva's avatar Riccardo Riva committed
      Added initial implementation of the predict functions
      
      Fixed pep8
      
      WIP on implementing the evaluation of a surrogate based on a dictionary
      
      Added option for evaluating a surrogate model with a dictionary
      
      Added option for using MinMaxScaler and not only StandardScaler. Improved documentation
      
      Added one more surrogate type, fixed output type and PEP8
      
      Initial implementation of predict_gradient for OpenTURNS metamodels. Improved documentation and clarified names
      
      Bugfix to predict functions and refractoring
      
      Bugfix and improved exceptions
      
      Added documentation for finite differences (to be implemented)
      
      Added implementation for the step size parameters
      
      Added implementation for finite differences
      
      Added step size and output scaling.
      
      Fixed TypeError: 'staticmethod' object is not callable
      thanks to https://stackoverflow.com/a/41921291/3676517
      
      Removed class SurrogateEvaluation and placed all of its content in the load module
      
      new structure to allow for load constraints
      
      finished new structure and updated tests
      
      included aggregated_cost.py
      
      Added function to compute the relative and absolute errors
      
      removed try except from load constraints and added setup requirement: openturns vers. >= 1.13
      
      working on ex 8
      
      Added SurrogateModel class
      
      passing input vals to load component to get the shapes right
      
      Added  warning for extrapolation
      
      Added first test for load module
      
      Added test and fixed a bug
      
      Added another test
      
      Added test for SurrogateModel class
      
      Added first test for predict_gradient() and fixed a bug
      
      Added test for PCE
      
      Fixed test_PCE()
      
      Added test for MLPRegressor
      
      Improved test
      
      Added test for wrong input type
      
      Added test for input domain boundary
      
      Added test for unknown model type
      
      Improved test for PCE and neural network
      
      Added test for input and output scaler
      
      Fixed predict_gradient() if there is an input_scaler. Remove code for finite differences, since they will be computed with OpenMDAO
      
      Fixed ouput_scaler in predict_gradient()
      
      added test of load constraint
      
      Added more test
      
      Improved test
      
      coverage up + pep8
      
      added docstrings
      
      pep8
      
      fixed loose connection of post constraint in some cases
      
      Fixed behavior of scikit-learn neural network in case of 1D output
      
      PEP8
      50e60c2a
  3. May 24, 2019
  4. May 02, 2019
    • Mikkel Friis-Møller's avatar
      Facilitated running Florisse with Topfarm · 36b1d6cc
      Mikkel Friis-Møller authored and Mads M. Pedersen's avatar Mads M. Pedersen committed
      Added possibility to specify units
      Created examples/docs/example_5_floris.py where floris and pywake are compared
      
      added Plant Energy and Floris to CI-image
      
      moved example data files
      
      resat topfarmkeys after example 6 ran
      
      updated no_print statement
      
      added plantenergy to ci-file to catch changes
      added search for topfarm.plugins to the init-file
      
      fix pep8
      
      figs
      
      extended possible design variable input types:
      [value]
      [(value,'unit')
      [(value, lower, upper)]
      [(value, lower, upper, unit)]
      
      updated example 6 accordingly
      36b1d6cc
  5. Mar 21, 2019
    • Mikkel Friis-Møller's avatar
      edited cost model wrapper to accept non-objective components · 68285f03
      Mikkel Friis-Møller authored
      established TopFarmGroup class for integrated optimization
      created example_4_integrated_optimization_aep_and_irr.py
      
      fixed pep8 and iir-example
      
      Introduced Penalty Component
      Various updates to facilitate the modular approach
      
      fixed penalty function
      
      fixed failing tests
      
      fixed more failing tests
      
      updated fuga/recorder tests to account for the new structure and increased number of elements in the recorder.
      
      upd. the input keys
      
      improved coverage
      68285f03
  6. Feb 07, 2019
  7. Oct 03, 2018
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