Change conversion to Euclidean distance

* Added neutral point (if present)

Closes !gsi-upm/senpy#37 (Ian's)

senpy/plugins/conversion/centroids.py

@@ -32,8 +32,17 @@ class CentroidConversion(EmotionConversionPlugin):
                     nv1[aliases.get(k2, k2)] = v2
                 ncentroids[aliases.get(k1, k1)] = nv1
             info['centroids'] = ncentroids
+
         super(CentroidConversion, self).__init__(info)
 
+        self.dimensions = set()
+        for c in self.centroids.values():
+            self.dimensions.update(c.keys())
+        self.neutralPoints = self.get("neutralPoints", dict())
+        if not self.neutralPoints:
+            for i in self.dimensions:
+                self.neutralPoints[i] = self.get("neutralValue", 0)
+
     def _forward_conversion(self, original):
         """Sum the VAD value of all categories found."""
         res = Emotion()
@@ -49,15 +58,19 @@ class CentroidConversion(EmotionConversionPlugin):
 
     def _backwards_conversion(self, original):
         """Find the closest category"""
-        dimensions = set(c.keys() for c in centroids.values())
-        neutralPoint = self.get("origin", None)
-        neutralPoint = {k:neutralPoint[k] if k in neturalPoint else 0}
-        
+        centroids = self.centroids
+        neutralPoints = self.neutralPoints
+        dimensions = self.dimensions
+
+        def distance_k(centroid, original, k):
+            # k component of the distance between the value and a given centroid
+            return (centroid.get(k, neutralPoints[k]) - original.get(k, neutralPoints[k]))**2
+
         def distance(centroid):
-            return sum((centroid.get(k, neutralPoint[k]) - original.get(k, neutralPoint[k]))**2 for k in dimensions)
+            return sum(distance_k(centroid, original, k) for k in dimensions)
+
+        emotion = min(centroids, key=lambda x: distance(centroids[x]))
 
-        emotion = min(centroids, key=lambda x: distance(centroids[x])
-                
         result = Emotion(onyx__hasEmotionCategory=emotion)
         return result