/** 计算所有医生的相似度 不管诊次，只计算共同看的病的个数 */
public int[][] getAllSimilarityByCommonRating(ZhengDoctorRecommendDataset ds) {
int allDoctorSize = ds.getAllDoctor().size();
// System.out.println("allDoctorSize"+allDoctorSize);
int[][] doctorSimilarity = new int[allDoctorSize + 1][allDoctorSize + 1];
for (Entry<Integer, DoctorEntity> entry : ds.getAllDoctor().entrySet()) {
DoctorEntity doc1 = entry.getValue();
int i = doc1.getId();
for (Entry<Integer, DoctorEntity> entry2 : ds.getAllDoctor().entrySet()) {
DoctorEntity doc2 = entry2.getValue();
int j = doc2.getId();
if (doc2.getId() > doc1.getId()) {
continue; // 跳出本次循环，执行下次循环
}
// System.out.println(doc1.getId());
// System.out.println(doc2.getId());
int count = getSimilarityByCommonRating(ds, doc1, doc2); // 找出相似的个数
doctorSimilarity[i][j] = count;
doctorSimilarity[j][i] = count; // 因为是个对称的
}
}
return doctorSimilarity;
}
/** 计算两个医生的相似度 不管诊次，只计算共同看的病的个数 */
public int getSimilarityByCommonRating(
ZhengDoctorRecommendDataset ds, DoctorEntity doctor1, DoctorEntity doctor2) {
// System.out.println(doctor1.getId());
List<ZhengRatingEntity> zheng1 =
ds.getSortedRatingsByDoctorId().get(doctor1.getId()); // 获取某一个医生所包含的症状
List<ZhengRatingEntity> zheng2 = ds.getSortedRatingsByDoctorId().get(doctor2.getId());
// 如果这两个症状集合有一个为空，则说明两个医生不相似，返回0
int count = 0;
if (zheng1 == null || zheng2 == null) {
System.err.println("医生没有看过病");
return 0;
}
// 判断两个症状集合是否有交集，如果有则count+1
for (int i = 0; i < zheng1.size(); i++) {
for (int j = 0; j < zheng2.size(); j++) {
if (zheng1.get(i).getZhengId() == zheng2.get(j).getZhengId()) {
count++;
}
}
}
return count;
}
/**
* 计算两个用户的相似度 计算的方法是欧式距离的方法
*
* @param doctor1
* @param doctor2
* @return
*/
public double getSimilarityByEucliddistance(
ZhengDoctorRecommendDataset ds, DoctorEntity doctor1, DoctorEntity doctor2) {
List<ZhengRatingEntity> zheng1 =
ds.getRatingsByDoctorId().get(doctor1.getId()); // 获取某一个医生所包含的疾病
List<ZhengRatingEntity> zheng2 = ds.getRatingsByDoctorId().get(doctor2.getId());
int commZheng = 0;
if (zheng1 == null || zheng2 == null) {
// System.err.println("医生没有治过病");
return 0;
}
// for (int i = 0; i < zheng1.size(); i++) {
// System.out.print(zheng1.get(i).getZhengId()+":"+zheng1.get(i).getRating()+" ");
// }
// System.out.println();
// for (int i = 0; i < zheng2.size(); i++) {
// System.out.print(zheng2.get(i).getZhengId()+":"+zheng2.get(i).getRating()+" ");
// }
// System.out.println();
//
// 判断两个疾病集合是否有交集
double sim = 0.0;
for (int i = 0; i < zheng1.size(); i++) {
for (int j = 0; j < zheng2.size(); j++) {
if (zheng1.get(i).getZhengId() == zheng2.get(j).getZhengId()) {
commZheng++;
int zheng1Rating = zheng1.get(i).getRating();
int zheng2Rating = zheng2.get(j).getRating();
// System.out.println("common zheng id:"+zheng1.get(i).getZhengId()+" "+zheng1Rating+"
// "+zheng2Rating);
sim += Math.pow((zheng1Rating - zheng2Rating), 2);
}
}
}
// System.out.println("欧式距离相似度："+sim);
if (commZheng > 0) {
// sim=1.0d - Math.tanh(sim);
// sim=1.0/(sim+1);
sim = sim;
}
return sim;
}
/**
* 计算所有医生的相似度
*
* <p>欧式距离
*/
public double[][] getAllDoctorSimilarity(ZhengDoctorRecommendDataset ds) {
int allDoctorSize = ds.getAllDoctor().size();
// System.out.println("allDoctorSize"+allDoctorSize);
double[][] doctorSimilarity = new double[allDoctorSize + 1][allDoctorSize + 1];
for (Entry<Integer, DoctorEntity> entry : ds.getAllDoctor().entrySet()) {
DoctorEntity doc1 = entry.getValue();
int i = doc1.getId();
for (Entry<Integer, DoctorEntity> entry2 : ds.getAllDoctor().entrySet()) {
DoctorEntity doc2 = entry2.getValue();
int j = doc2.getId();
if (doc2.getId() > doc1.getId()) {
continue; // 跳出本次循环，执行下次循环
}
// System.out.println(doc1.getId());
// System.out.println(doc2.getId());
double count = 0;
count = getSimilarityByEucliddistance(ds, doc1, doc2); // 找出相似的个数
doctorSimilarity[i][j] = count;
doctorSimilarity[j][i] = count; // 因为是个对称的
}
}
// File file = new File(DataSetConfig.SimiResultPath);
// if (file.exists()) {
// file.delete();
// }
//
// for (int i = 0; i < doctorSimilarity.length; i++) {
// String line="";
// for (int j = 0; j < doctorSimilarity[i].length; j++) {
// line +=String.format("%.4f",doctorSimilarity[i][j]) + " ";
//// System.out.print(String.format("%.4f",doctorSimilarity[i][j]) + " ");
// }
// System.out.println();
// FileUtil.appendData(DataSetConfig.SimiResultPath, line);
// }
return doctorSimilarity;
}