treinamento da rede

pyFTS/models/multivariate/som.py

@@ -0,0 +1,63 @@
+"""
+Kohonen Self Organizing Maps for Fuzzy Time Series
+"""
+import pandas as pd
+import SimpSOM as sps
+from pyFTS.models.multivariate import wmvfts
+from typing import Tuple
+
+
+class SOMFTS:
+    def __init__(self,
+                 grid_dimension: Tuple,
+                 **kwargs):
+        # SOM attributes
+        self.net: sps.somNet = None
+        self.data: pd.DataFrame = None
+        self.grid_dimension: Tuple = grid_dimension
+        self.pbc = kwargs.get('PBC', True)
+
+        # fts attributes
+        self.fts_method = kwargs.get('fts_method', wmvfts.WeightedMVFTS)
+        self.order = kwargs.get('order', 2)
+        self.is_trained = False
+
+        # debug attributes
+        self.name = 'Kohonen Self Organizing Maps FTS'
+        self.shortname = 'SOM-FTS'
+
+    def __repr__(self):
+        status = "is trained" if self.is_trained else "not trained"
+        return f'{self.name}-{status}'
+
+    def __str__(self):
+        return self.name
+
+    def __del__(self):
+        del self.net
+
+    def train(self,
+              data: pd.DataFrame,
+              percentage_train: float = .7,
+              leaning_rate: float = 0.01,
+              epochs: int = 10000):
+        self.data = data
+        limit = len(self.data) * percentage_train
+        train = data[:limit]
+        x, y = self.grid_dimension
+        self.net = sps.somNet(x, y, train, self.pbc)
+        self.net.train(startLearnRate=leaning_rate,
+                       epochs=epochs)
+
+    def save_net(self,
+                 filename: str = "SomNet trained"):
+        self.net.save(filename)
+
+    def show_grid(self,
+                  graph_type: str = 'nodes_graph',
+                  **kwargs):
+        if graph_type == 'nodes_graph':
+            colnum = kwargs.get('colnum', 0)
+            self.net.nodes_graph(colnum=colnum)
+        else:
+            self.net.diff_graph()