Finished testing
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@ -133,9 +133,9 @@ class BayesianOptimization:
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self.Y[0] = reward
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self.Y[0] = reward
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self.best_reward[0] = np.max(self.Y)
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self.best_reward[0] = np.max(self.Y)
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else:
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else:
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self.X = np.vstack((self.X, np.around(x, decimals=8)), dtype=np.float64)
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self.X = np.vstack((self.X, np.around(x, decimals=8)))
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self.Y = np.vstack((self.Y, reward), dtype=np.float64)
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self.Y = np.vstack((self.Y, reward))
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self.best_reward = np.vstack((self.best_reward, np.max(self.Y)), dtype=np.float64)
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self.best_reward = np.vstack((self.best_reward, np.max(self.Y)))
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self.gp.fit(self.X, self.Y)
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self.gp.fit(self.X, self.Y)
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self.episode += 1
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self.episode += 1
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@ -27,4 +27,9 @@ def generate_launch_description():
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executable='iml_experiment',
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executable='iml_experiment',
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name='iml_experiment'
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name='iml_experiment'
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),
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),
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Node(
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package='franka_iml_experiment',
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executable='check_experiment_pose',
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name='check_experiment_pose'
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),
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])
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])
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