/**
* @Title: classConditionalProbability @Description: 多项式朴素贝叶斯类条件概率
* 类条件概率P(tk|c)=(类c下单词tk在各个文档中出现过的次数之和+1)/(类c下单词总数+|V|) V是训练样本的单词表（即抽取单词，单词出现多次，只算一个），
* |V|则表示训练样本包含多少种单词。 P(tk|c)可以看作是单词tk在证明d属于类c上提供了多大的证据， 而P(c)则可以认为是类别c在整体上占多大比例(有多大可能性)
*
* @param @return
* @return BigDecimal
* @throws
*/
public static BigDecimal classConditionalProbability(String classifier, String word) {
BigDecimal molecular =
new BigDecimal(TrainSampleDataManager.wordInClassCount(word, classifier) + 1);
BigDecimal denominator =
new BigDecimal(
TrainSampleDataManager.classWordCount(classifier)
+ TrainSampleDataManager.sampleWordKindCount());
return molecular.divide(denominator, 10, BigDecimal.ROUND_CEILING);
}
/**
* @Title: classifyResult @Description: 多项式朴素贝叶斯分类结果 P(C_i|w_1,w_2...w_n) = P(w_1,w_2...w_n|C_i) *
* P(C_i) / P(w_1,w_2...w_n) = P(w_1|C_i) * P(w_2|C_i)...P(w_n|C_i) * P(C_i) / (P(w_1) * P(w_2)
* ...P(w_n))
*
* @param @return
* @return BigDecimal
* @throws
*/
public static Map<String, BigDecimal> classifyResult(Set<String> words) {
Map<String, BigDecimal> resultMap = new HashMap<String, BigDecimal>();
Set<String> classifierSet = TrainSampleDataManager.getAllClassifiers();
for (String classifier : classifierSet) {
BigDecimal probability = new BigDecimal(1.0);
for (String word : words) {
probability =
probability
.multiply(classConditionalProbability(classifier, word))
.multiply(zoomFactor);
}
resultMap.put(classifier, probability.multiply(priorProbability(classifier)));
}
classifierResult = resultMap;
return resultMap;
}
/**
* @Title: priorProbability @Description: 多项式朴素贝叶斯先验概率 先验概率P(c)= 类c下单词总数/整个训练样本的单词总数
*
* @param classifier
* @return BigDecimal
* @throws
*/
public static BigDecimal priorProbability(String classifier) {
BigDecimal molecular = new BigDecimal(TrainSampleDataManager.classWordCount(classifier));
BigDecimal denominator = new BigDecimal(TrainSampleDataManager.sampleWordCount());
return molecular.divide(denominator, 10, BigDecimal.ROUND_CEILING);
}
