From 2db3b0311ed7fd99167f20263248923e6dd2615d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Petr=C3=B4nio=20C=C3=A2ndido?= <petronio.candido@gmail.com>
Date: Tue, 13 Nov 2018 18:54:18 -0200
Subject: [PATCH] uod_clip on mvfts models

---
 pyFTS/models/multivariate/mvfts.py |  7 +++++--
 pyFTS/tests/multivariate.py        | 20 ++++++++++++++++++--
 2 files changed, 23 insertions(+), 4 deletions(-)

diff --git a/pyFTS/models/multivariate/mvfts.py b/pyFTS/models/multivariate/mvfts.py
index 31f2fa1..4522e92 100644
--- a/pyFTS/models/multivariate/mvfts.py
+++ b/pyFTS/models/multivariate/mvfts.py
@@ -38,7 +38,11 @@ class MVFTS(fts.FTS):
     def apply_transformations(self, data, params=None, updateUoD=False, **kwargs):
         ndata = data.copy(deep=True)
         for var in self.explanatory_variables:
-            ndata[var.data_label] = var.apply_transformations(data[var.data_label].values)
+            if self.uod_clip:
+                ndata[var.data_label] = np.clip(ndata[var.data_label].values,
+                                                var.partitioner.min, var.partitioner.max)
+
+            ndata[var.data_label] = var.apply_transformations(ndata[var.data_label].values)
 
         return ndata
 
@@ -130,7 +134,6 @@ class MVFTS(fts.FTS):
 
     def forecast_ahead(self, data, steps, **kwargs):
         generators = kwargs.get('generators',None)
-        start = kwargs.get('start', 0)
 
         if generators is None:
             raise Exception('You must provide parameter \'generators\'! generators is a dict where the keys' +
diff --git a/pyFTS/tests/multivariate.py b/pyFTS/tests/multivariate.py
index 1f81523..3f0898b 100644
--- a/pyFTS/tests/multivariate.py
+++ b/pyFTS/tests/multivariate.py
@@ -87,13 +87,29 @@ train_split = 24 * 800
 train_mv = dataset.iloc[:train_split]
 test_mv = dataset.iloc[train_split:]
 
+#model = Util.load_obj('/home/petronio/Downloads/ClusteredMVFTS4')
+
+
+
 vhour = variable.Variable("Hour", data_label="hour", partitioner=seasonal.TimeGridPartitioner, npart=24,
                           data=dataset,
                           partitioner_specific={'seasonality': DateTime.hour_of_day, 'type': 'common'})
-vprice = variable.Variable("Price", data_label="price", partitioner=Grid.GridPartitioner, npart=10,
+vprice = variable.Variable("Price", data_label="price", partitioner=Grid.GridPartitioner, npart=55,
                             data=train_mv)
+model = cmvfts.ClusteredMVFTS(order=2, knn=3)
+model.append_variable(vhour)
+model.append_variable(vprice)
+model.target_variable = vprice
+model.fit(train_mv)
+
+data = [[1, 1.0], [2, 2.0]]
+
+df = pd.DataFrame(data, columns=['hour','price'])
+
+forecasts = model.predict(df, steps_ahead=24, generators={'Hour': lambda x : (x+1)%24 })
 
 
+'''
 params = [
     {},
     {},
@@ -114,4 +130,4 @@ for ct, method in enumerate([mvfts.MVFTS, wmvfts.WeightedMVFTS, cmvfts.Clustered
 
 #print(model1.predict(test_mv, steps_ahead=24, generators={'Hour': lambda x : (x+1)%24 }))
 
-#"""
\ No newline at end of file
+'''
\ No newline at end of file