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中每個主題的詞語分佈p（w | t）我需要獲得以Java編程的Mallet找到的每個主題的單詞分佈（不在CLI中，如how to get a probability distribution for a topic in mallet?中所述）。對於我的意思的例子：Introduction to Latent Dirichlet Allocation：Mallet
Topic A: 30% broccoli, 15% bananas, 10% breakfast, 10% munching, … (at which point, you could interpret topic A to be about food)
Topic B: 20% chinchillas, 20% kittens, 20% cute, 15% hamster, … (at which point, you could interpret topic B to be about cute animals)
槌提供每個主題令牌「砝碼」，並在http://comments.gmane.org/gmane.comp.ai.mallet.devel/2064有人試圖寫一個方法讓每個主題字槌的分佈。
我修改了這個方法，使得所有的權重除以上面郵件列表中討論的總和。
以下方法（當添加到ParallelTopicModel.java時）是否正確計算Mallet中每個主題詞（p | w | t）的分佈？
/**
* Get the normalized topic word weights (weights sum up to 1.0)
* @param topic the topic
* @return the normalized topic word weights (weights sum up to 1.0)
*/
public ArrayList<double[]> getNormalizedTopicWordWeights(int topic) {
ArrayList<double[]> tokenWeights = new ArrayList<double[]>();
for (int type = 0; type < numTypes; type++) {
int[] topicCounts = typeTopicCounts[type];
double weight = beta;
int index = 0;
while (index < topicCounts.length && topicCounts[index] > 0) {
int currentTopic = topicCounts[index] & topicMask;
if (currentTopic == topic) {
weight += topicCounts[index] >> topicBits;
break;
}
index++;
}
double[] tokenAndWeight = { (double) type, weight };
tokenWeights.add(tokenAndWeight);
}
// normalize
double sum = 0;
// get the sum
for (double[] tokenAndWeight : tokenWeights) {
sum += tokenAndWeight[1];
}
// divide each element by the sum
ArrayList<double[]> normalizedTokenWeights = new ArrayList<double[]>();
for (double[] tokenAndWeight : tokenWeights) {
tokenAndWeight[1] = tokenAndWeight[1]/sum;
normalizedTokenWeights.add(tokenAndWeight);
}
return normalizedTokenWeights;
}