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PWC optimization - printing parameters for non gradient based algorithm #239

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PWC optimization - printing parameters for non gradient based algorithm #239

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8 changes: 4 additions & 4 deletions c3/optimizers/optimizer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -256,12 +256,12 @@ def fct_to_min(
[np.ndarray, tf.constant]
Value of the goal function. Float if input is np.array else tf.constant
"""
pars = []
# We use zip to create pairs of Quantity objects and their new bare numeric parameter values.
# Then we compute the new physical value and store it in the status.
for par, y in zip(self.pmap.get_parameters(), np.array(input_parameters)):
pars.append(par.get_other_value(y).tolist())
self.optim_status["params"] = pars
self.pmap.set_parameters_scaled(input_parameters)
self.optim_status["params"] = [
par.numpy().tolist() for par in self.pmap.get_parameters()
]
if isinstance(input_parameters, np.ndarray):
current_params = tf.constant(input_parameters)
goal = self.goal_function(current_params)
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