Derivative issue

[im-Kitsch]

Hi,

thanks for the greate work. I checked your code, for function BayesianInteractionPrimitive. basis_inverse_transform, if we require derivate and scaling the observation, the derivate should not be unscaled as same as observation. Observation is: obsscale+mu, while derivative only need obsscale. I guess this is a bug. Is it right?

Thanks

intprim/intprim/bayesian_interaction_primitives.py

Lines 244 to 254 in 8994b15

	def basis_inverse_transform(self, x, weights, deriv = False):
	trajectory = np.zeros((self.basis_model.num_observed_dof, x.shape[0]), dtype = intprim.constants.DTYPE)
	for idx in range(x.shape[0]):
	trajectory[:, idx] = self.basis_model.apply_coefficients(x[idx], weights, deriv)
	if(self.scaling_groups is not None):
	for group, scaler in zip(self.scaling_groups, self.scalers):
	trajectory[group, :] = scaler.inverse_transform(trajectory[group, :].reshape(-1, 1)).reshape(trajectory[group, :].shape)
	return trajectory