46 lines
2.7 KiB
Mathematica
46 lines
2.7 KiB
Mathematica
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function results_HTRNN = run_HTRNN(robot, benchmarkSettings, experimentDataStruct, optionsHTRNN, progressBar)
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% Authors: Quentin Leboutet, Julien Roux, Alexandre Janot and Gordon Cheng
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%% Define result data structure:
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if benchmarkSettings.displayProgression == true
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waitbar(0, progressBar, sprintf('HTRNN: First Iteration...'));
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end
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results_HTRNN.benchmarkSettings = benchmarkSettings; % Data structure containing the benchmark settings
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results_HTRNN.options = optionsHTRNN; % Options that are specific to the identification method.
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results_HTRNN.Betas = zeros(benchmarkSettings.numberOfInitialEstimates, benchmarkSettings.numberOfExperimentsPerInitialPoint, numel(benchmarkSettings.Beta_obj)); % Identified parameters
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results_HTRNN.times = zeros(benchmarkSettings.numberOfInitialEstimates, benchmarkSettings.numberOfExperimentsPerInitialPoint); % Computation times
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results_HTRNN.iterations = zeros(benchmarkSettings.numberOfInitialEstimates, benchmarkSettings.numberOfExperimentsPerInitialPoint); % Number of iterations
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%% Run the identification code:
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for initEst = 1:benchmarkSettings.numberOfInitialEstimates % For each set of initial parameters
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Beta_0 = benchmarkSettings.Initial_Beta(:,initEst);
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for expNb = 1:benchmarkSettings.numberOfExperimentsPerInitialPoint % For each trajectory noise
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tic
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[Beta_HTRNN, it_HTRNN] = HTRNN_identification(robot, experimentDataStruct, expNb, benchmarkSettings, Beta_0, optionsHTRNN);
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results_HTRNN.times(initEst, expNb)=toc;
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if benchmarkSettings.displayProgression == true
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waitbar(((initEst-1)*benchmarkSettings.numberOfExperimentsPerInitialPoint+expNb)/((benchmarkSettings.numberOfInitialEstimates-1)*benchmarkSettings.numberOfExperimentsPerInitialPoint+benchmarkSettings.numberOfExperimentsPerInitialPoint), progressBar, sprintf('Hopfield NN: %d%% done...', floor(100*((initEst-1)*benchmarkSettings.numberOfExperimentsPerInitialPoint+expNb)/((benchmarkSettings.numberOfInitialEstimates-1)*benchmarkSettings.numberOfExperimentsPerInitialPoint+benchmarkSettings.numberOfExperimentsPerInitialPoint))));
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end
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if optionsHTRNN.verbose == true
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fprintf('HTRNN status: initial estimate %d, experiment %d \n', initEst, expNb );
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fprintf('HTRNN status: initial parameter error = %d\n', norm(Beta_0-benchmarkSettings.Beta_obj));
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fprintf('HTRNN status: estimated parameter error = %d\n', norm(Beta_HTRNN-benchmarkSettings.Beta_obj));
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disp('---------------------------------------------')
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end
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results_HTRNN.Betas(initEst,expNb,:)=Beta_HTRNN;
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results_HTRNN.iteration(initEst,expNb)=it_HTRNN;
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end
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end
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end
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