Improvements on MVFTS.forecast_ahead for supporting CMVFTS many steps ahead forecasts
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@ -55,7 +55,7 @@ def fuzzyfy_instance_clustered(data_point, cluster, **kwargs):
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mode = kwargs.get('mode', 'sets')
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fsets = []
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for fset in cluster.search(data_point, type='name'):
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if cluster.sets[fset].membership(data_point) > alpha_cut:
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if cluster.sets[fset].membership(data_point) >= alpha_cut:
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if mode == 'sets':
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fsets.append(fset)
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elif mode =='both':
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@ -172,6 +172,7 @@ class MVFTS(fts.FTS):
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start = kwargs.get('start_at', self.max_lag)
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ndata = ndata.loc[ndata.index[start-self.max_lag:start]]
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ret = []
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for k in np.arange(start, start+steps):
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ix = ndata.index[k-self.max_lag:k]
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@ -188,11 +189,11 @@ class MVFTS(fts.FTS):
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for data_label in generators.keys():
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if data_label != self.target_variable.data_label:
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if isinstance(generators[data_label], LambdaType):
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last_data_point = ndata.loc[sample.index[-1]]
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last_data_point = ndata.loc[ndata.index[-1]]
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new_data_point[data_label] = generators[data_label](last_data_point[data_label])
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elif isinstance(generators[data_label], fts.FTS):
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model = generators[data_label]
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last_data_point = ndata.loc[[sample.index[-model.order]]]
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last_data_point = ndata.loc[[ndata.index[-model.order]]]
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if not model.is_multivariate:
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last_data_point = last_data_point[data_label].values
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@ -200,9 +201,6 @@ class MVFTS(fts.FTS):
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new_data_point[self.target_variable.data_label] = tmp
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print(k)
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print(new_data_point)
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ndata = ndata.append(new_data_point, ignore_index=True)
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return ret[-steps:]
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