No more stability-metric for the nn (but se instead)
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@ -354,8 +354,10 @@ def scoreUnread(G, globMu, globStd, errorFac=-0.6):
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node['se'] = globStd / math.sqrt(len(feedbacks))
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node['se'] = globStd / math.sqrt(len(feedbacks))
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feedbacks.append(node['std'])
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feedbacks.append(node['std'])
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weights.append(getWeightForType('sigma'))
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weights.append(getWeightForType('sigma'))
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feedbacks.append(1-1/len(feedbacks))
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feedbacks.append(node['se'])
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weights.append(getWeightForType('stability'))
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weights.append(getWeightForType('se'))
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#feedbacks.append(1-1/len(feedbacks))
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#weights.append(getWeightForType('stability'))
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node['mean'] = sum([fb*w for fb, w in zip(feedbacks, weights)])/len(feedbacks)
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node['mean'] = sum([fb*w for fb, w in zip(feedbacks, weights)])/len(feedbacks)
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node['score'] = node['mean'] + errorFac*node['se']
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node['score'] = node['mean'] + errorFac*node['se']
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else:
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else:
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@ -716,7 +718,7 @@ def train(gamma = 0.1):
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weights[attr] = -0.1+random.random()*1.5
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weights[attr] = -0.1+random.random()*1.5
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else:
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else:
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weights[attr] += delta
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weights[attr] += delta
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if attr not in ['sigma', 'mu', 'stability']:
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if attr not in ['sigma', 'mu', 'se']:
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weights[attr] = min(max(0, weights[attr]), 3)
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weights[attr] = min(max(0, weights[attr]), 3)
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mse = evaluateFitness(books)
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mse = evaluateFitness(books)
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if mse < best_mse: # got better
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if mse < best_mse: # got better
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