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54 lines
1.5 KiB
54 lines
1.5 KiB
import json
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import numpy as np
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import pyswarms as ps
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import sys
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sys.path.append('../')
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from reeps import REEPS
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with open('one_ree_settings.txt') as file:
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testing_params = json.load(file)
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beaker = REEPS(**testing_params)
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# def new_obj(predicted_dict, meas_df, epsilon):
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# meas_cols = list(meas_df)
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# pred_keys = list(predicted_dict.keys())
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# meas = meas_df[meas_cols[2]]
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# pred = (predicted_dict['re_org'] + epsilon) / (predicted_dict['re_aq'] + epsilon)
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# log_pred = np.log10(pred)
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# log_meas = np.log10(meas)
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# obj = np.sum((log_pred - log_meas) ** 2)
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# return obj
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# #
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# #
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# # def new_obj(ping):
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# # print(ping)
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# beaker.set_objective_function(new_obj)
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# objective_kwargs = {"epsilon": 1e-14}
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# beaker.set
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# noinspection PyUnusedLocal
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def optimizer(func, x_guess):
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lb = np.array([1e-1])
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ub = np.array([1e1])
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bounds = (lb, ub)
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options = {'c1': 1e-3, 'c2': 1e-3, 'w': 0.9}
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mini_optimizer = ps.single.global_best.GlobalBestPSO(n_particles=100, dimensions=1,
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options=options, bounds=bounds)
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f_opt, x_opt = mini_optimizer.optimize(func, iters=100)
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return x_opt
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minimizer_kwargs = {"method": 'SLSQP',
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"bounds": [(1e-1, 1e1)],
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"constraints": (),
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"options": {'disp': True, 'maxiter': 1000, 'ftol': 1e-6}}
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# est_enthalpy = beaker.fit(optimizer=optimizer)
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est_enthalpy = beaker.fit()
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print(est_enthalpy)
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# beaker.update_xml(est_enthalpy)
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# beaker.parity_plot()
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# print(beaker.r_squared())
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