minor upgrades in wavelets

aimclub · Oct 21, 2024 · df16eef · df16eef
1 parent 25f5497
commit df16eef
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Showing 3 changed files with 714 additions and 11 deletions.
diff --git a/examples/tutorial/time_series/ts_classification/classification_example.ipynb b/examples/tutorial/time_series/ts_classification/classification_example.ipynb
diff --git a/examples/tutorial/time_series/ts_forecasting/__init__.py b/examples/tutorial/time_series/ts_forecasting/__init__.py
diff --git a/fedot_ind/core/operation/transformation/basis/wavelet.py b/fedot_ind/core/operation/transformation/basis/wavelet.py
@@ -25,6 +25,7 @@ def __init__(self, params: Optional[OperationParameters] = None):
         super().__init__(params)
         self.n_components = params.get('n_components')
         self.wavelet = params.get('wavelet')
+        self.use_low_freq = params.get('low_freq', False)
         self.basis = None
         self.discrete_wavelets = DISCRETE_WAVELETS
         self.continuous_wavelets = CONTINUOUS_WAVELETS
@@ -66,7 +67,8 @@ def threshold(Monoid): return ListMonad([Monoid[0][
 
         basis = Either.insert(data).then(decompose).then(threshold).value[0]
         basis = np.concatenate(basis)
-        return basis
+
+        return basis[-1, :] if self.use_low_freq else basis
 
     def _get_multidim_basis(self, data):
         def decompose(multidim_signal):