Performance Optimizations when skip_conditioning=True
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@ -183,6 +183,9 @@ class PCA_Distribution(SB3_Distribution):
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return traj[:, -self.window:, :]
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def _conditioning_engine(self, trajectory, pi_mean, pi_std):
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if self.skip_conditioning:
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return pi_mean, pi_std
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traj = self._pad_and_cut_trajectory(trajectory)
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y_np = np.append(np.swapaxes(traj, -1, -2),
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np.expand_dims(pi_mean, -1), -1)
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