2ab1119596b420cc696a783271d617562ccf7e28,examples/plot_EEG_Example.py,,,#,123

Before Change

train, test = train_test_split(np.arange(len(eeg["classes"])), random_state=0)

rfc_param_grid = {"n_estimators": [8, 16, 32, 64, 128, 256, 512, 1024]}
model_cesium = build_model_from_featureset(fset_cesium.isel(name=train),
                                          RandomForestClassifier(max_features="auto",
                                                                 random_state=0),
                                          params_to_optimize=rfc_param_grid)

After Change

fset_dwt = featurize.featurize_time_series(times=None, values=eeg["dwts"], errors=None,
                                           features_to_use=list(guo_features.keys()),
                                           custom_functions=guo_features)
print(fset_dwt.head())

//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// The output featureset has the same form as before, except now the ``channel``

Instances