Contents

Index

Estimation Tools

Clapeyron.ToEstimateType
ToEstimate
ToEstimate(params_dict)

Input parameters: A dictionary with the following potential entries

  • params: The name of the parameter being fitted (Symbol)
  • indices: The index of the parameter being fitted (Integer or Tuple{Integer,Integer})
  • factor: Factor to multiply parameter being fitted to have it in the correct units (Float64)
  • symmetric: For PairParam, if the parameter is symmetric or asymmetric (Bool)
  • cross_assoc: For AssocParam, if the parameter is for cross-association (Bool)
  • recombine: For PairParam, if the combining rules must be applied for unlike interactions (Bool)
  • lower: Lower bound for the parameter (Float64)
  • upper: Upper bound for the parameter (Float64)
  • guess: Initial guess for the parameter (Float64)

Output:

A ToEstimate struct

Description

Turns the input parameter dictionary into a ToEstimate struct to be used within the parameter estimation.

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Clapeyron.EstimationType
Estimation
Estimation(model,toestimate,filepaths,ignorefield,objective_form)

Input parameters:

  • model: The initial model containing the species we wish to parameterise
  • toestimate: The dictionary of parameters being fitted
  • filepaths or filepaths_weights: The location of the data files used to fit. Can also contain the weights of each dataset
  • ignorefield: Specify which EoSModel fields to ignore in the main model
  • objective_form: Specify the functional form of the objective function in the form objective_form(pred,exp)

Output:

Estimator object which contains the following:

  • model: The model whose parameters will be varied
  • initial_model: The initial model before parameterisation
  • toestimate: ToEstimate struct which contains all the information on the parameters
  • data: Vector of EstimationData structs where all the information on the data is stored
  • ignorefield: Vector of fields to ignore in the parameter estimation
  • objective_form: Function to evaluate the error measure for the objective function

The following objects are also output:

  • objective: The objective function which is used to fit the parameters
  • x0: Initial guesses for the parameters
  • upper: Upper bounds for the parameters
  • lower: Lower bounds for the parameters

Description

Produces the estimator and other useful objects used within parameter estimation

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Clapeyron.EstimationDataType
EstimationData
EstimationData(filepaths)

Input parameters:

  • filepaths or filepaths_weights: The filepath of the data used in parameter estimation. Optionally, a tuple containing the weights of each dataset.

Output:

An EstimationData struct with the following fields:

  • method: The property estimation method which is used to obtain predictions for a given input
  • inputs_name: The variable names for the inputs
  • outputs_name: The variable names for the outputs
  • inputs: Vector for each input
  • outputs: Vector for each output
  • weights: The weight for this particular dataset

Description

For a given input data set, produce an EstimationData struct.

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Clapeyron.return_modelFunction
return_model
return_model(estimation,model,params)

Input parameters:

  • estimation: The estimator object
  • model: The model whose parameters we are varying
  • params: The new parameters which we want to change

Output:

  • model: The new model with the updated parameters

Description

Based on the parameters provided and the estimator, a new model is produced from the input.

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Clapeyron.objective_functionFunction
objective_function
objective_function(estimation,params)

Input parameters:

  • estimation: The estimator object
  • params: The new parameters which we want to evaluate the objective function for

Output: The relate root mean square error given the data and parameters provided

Description

The objective function used within parameter estimation.

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