def transform_3d(self, X):
X_resampled = sp.zeros((X.shape[0], self.n_samples, X.shape[2]))
xnew = sp.linspace(0, 1, self.n_samples)
for i in range(X.shape[0]):
end = last_index(X[i])
for j in range(X.shape[2]):
X_resampled[i, :, j] = resampled(X[i, :end, j], n_samples=self.n_samples, kind=self.interp_kind)
# Compute indices based on alignment of dimension self.scaling_col_idx with the reference
indices_xy = [[] for _ in range(self.n_samples)]
if self.save_path and len(DTWSampler.saved_dtw_path)==(self.d+1): # verify if full dtw path already exists
current_path = DTWSampler.saved_dtw_path[i]
else:
# append path
current_path = dtw_path(X_resampled[i, :, self.scaling_col_idx], self.reference_series)
if self.save_path: # save current path is asked
DTWSampler.saved_dtw_path.append(current_path)
for t_current, t_ref in current_path:
indices_xy[t_ref].append(t_current)
for j in range(X.shape[2]):
if False and j == self.scaling_col_idx:
X_resampled[i, :, j] = xnew
else:
ynew = sp.array([sp.mean(X_resampled[i, indices, j]) for indices in indices_xy])
X_resampled[i, :, j] = ynew
return X_resampled
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