/**
* 计算给定实例文档属于指定类别的概率，返回的是取对数后的数值
*
* @param category
* @param doc
* @return
*/
public double getProbability(String category, Instance doc) {
double result = getCategoryProbability(category);
for (String feature : doc.getWords()) {
if (VARIABLE.containFeature(feature)) {
result += getFeatureProbability(feature, category);
}
}
return result;
}
/**
* 判断该实例所属的类别category
*
* @param doc
* @return
*/
public String getCategory(Instance doc) {
Collection<String> categories = VARIABLE.getCategories();
System.out.println(categories);
double best = Double.NEGATIVE_INFINITY;
String bestName = null;
for (String c : categories) {
double current = getProbability(c, doc);
System.out.println(c + ":" + current);
if (best < current) {
best = current;
bestName = c;
}
}
return bestName;
}
/**
* 训练一篇文档
*
* @param doc
*/
public void training(Instance doc) {
VARIABLE.addInstance(doc);
}
/**
* 保存训练结果
*
* @throws IOException
*/
void save(File file) throws IOException {
DataOutput out = new DataOutputStream(new FileOutputStream(file));
VARIABLE.write(out);
}
/**
* 加载训练结果
*
* @param file
* @throws IOException
*/
public void load(File file) throws IOException {
DataInputStream in = new DataInputStream(new FileInputStream(file));
VARIABLE = Variable.read(in);
}
/**
* 计算P(feature|cateogry),返回的是取对数后的数值
*
* @param feature
* @param category
* @return
*/
public double getFeatureProbability(String feature, String category) {
int m = VARIABLE.getFeatureCount();
return Math.log(
(VARIABLE.getDocCount(feature, category) + 1.0) / (VARIABLE.getDocCount(category) + m));
}
/**
* 计算P（C)=该类型文档总数/文档总数，返回的数对数值
*
* @param category
* @return
*/
public double getCategoryProbability(String category) {
return Math.log(VARIABLE.getDocCount(category) * 1.0f / VARIABLE.getDocCount());
}
