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@ -32,36 +32,58 @@ class CentroidConversion(EmotionConversionPlugin):
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nv1[aliases.get(k2, k2)] = v2
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ncentroids[aliases.get(k1, k1)] = nv1
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info['centroids'] = ncentroids
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super(CentroidConversion, self).__init__(info)
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self.dimensions = set()
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for c in self.centroids.values():
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self.dimensions.update(c.keys())
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self.neutralPoints = self.get("neutralPoints", dict())
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if not self.neutralPoints:
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for i in self.dimensions:
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self.neutralPoints[i] = self.get("neutralValue", 0)
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def _forward_conversion(self, original):
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"""Sum the VAD value of all categories found."""
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"""Sum the VAD value of all categories found weighted by intensity.
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Intensities are scaled by onyx:maxIntensityValue if it is present, else maxIntensityValue
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is assumed to be one. Emotion entries that do not have onxy:hasEmotionIntensity specified
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are assumed to have maxIntensityValue. Emotion entries that do not have
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onyx:hasEmotionCategory specified are ignored."""
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res = Emotion()
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maxIntensity = float(original.get("onyx:maxIntensityValue", 1))
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for e in original.onyx__hasEmotion:
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category = e.onyx__hasEmotionCategory
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if category in self.centroids:
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for dim, value in self.centroids[category].items():
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try:
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res[dim] += value
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except Exception:
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res[dim] = value
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category = e.get("onyx:hasEmotionCategory", None)
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if not category:
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continue
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intensity = e.get("onyx:hasEmotionIntensity", maxIntensity) / maxIntensity
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if not intensity:
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continue
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centroid = self.centroids.get(category, None)
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if centroid:
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for dim, value in centroid.items():
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neutral = self.neutralPoints[dim]
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if dim not in res:
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res[dim] = 0
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res[dim] += (value - neutral) * intensity + neutral
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return res
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def _backwards_conversion(self, original):
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"""Find the closest category"""
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dimensions = list(self.centroids.values())[0]
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centroids = self.centroids
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neutralPoints = self.neutralPoints
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dimensions = self.dimensions
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def distance_k(centroid, original, k):
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# k component of the distance between the value and a given centroid
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return (centroid.get(k, neutralPoints[k]) - original.get(k, neutralPoints[k]))**2
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def distance(centroid):
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return sum(distance_k(centroid, original, k) for k in dimensions)
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def distance(e1, e2):
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return sum((e1[k] - e2.get(k, 0)) for k in dimensions)
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emotion = min(centroids, key=lambda x: distance(centroids[x]))
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emotion = ''
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mindistance = 10000000000000000000000.0
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for state in self.centroids:
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d = distance(self.centroids[state], original)
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if d < mindistance:
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mindistance = d
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emotion = state
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result = Emotion(onyx__hasEmotionCategory=emotion)
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result.onyx__algorithmConfidence = distance(centroids[emotion])
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return result
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def convert(self, emotionSet, fromModel, toModel, params):
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