if(face.get(label[i]) * labelNumScore<minLabelScore){ continue; } labelScore=Math.min(face.get(label[i]) * labelNumScore,maxLabelScore); scoreScript.append("else if(doc['labelNum'].value==").append(labelNum.get(i)).append("){return ").append(labelScore).append(";}"); } score...
query score和function score相乘 (2) replace:仅使用function score,忽略query score (3) sum:query score和function score相加 (3) avg:平均值 (4) max: query score和function score中最大的 (5) min: query score和function score中最小的 1. 2. 3. 4. 5. 6. 使用multi_match最初始的查询评分排序...
GET /<index_name>/_search { "size": 0, "aggs": { "min_score": { "min": { "field": "score" } } } } 聚合查询 -avg应用案例: 案例说明:使用sum聚合查询来计算销售记录的总销售数量。 GET /<index_name>/_search { "size": 0, "aggs": { "total_sales": { "sum": { "field": ...
"functions":[{"filter":{"match":{"test":"bar"}},"random_score":{},"weight":23},{"filter":{"match":{"test":"cat"}},"weight":42}],"max_boost":42,"score_mode":"max","boost_mode":"multiply","min_score":42}}}
"5", "functions": [ { "filter": { "match": { "test": "bar" } }, "random_score": {}, "weight": 23 }, { "filter": { "match": { "test": "cat" } }, "weight": 42 } ], "max_boost": 42, "score_mode": "max", "boost_mode": "multiply", "min_score": 42 } ...
如果得到一个doc分数>score_min,则更新结果池和score_min。 如果累加全部倒排链最大分数之和小于score_min,则停止检索。 WAND算法跳过了文档 7,8,9 这 3 个不重要的文档。所以 WAND 算法主要是通过倒排链的数据的跳跃来提升查询处理的速度上面的检索过程,看不清楚的可以留言。 结论:wand算法可以实现一个or模糊...
"5", "functions": [ { "filter": { "match": { "test": "bar" } }, "random_score": {}, "weight": 23 }, { "filter": { "match": { "test": "cat" } }, "weight": 42 } ], "max_boost": 42, "score_mode": "max", "boost_mode": "multiply", "min_score" : 42 }...
min_score: 过滤掉评分太低的 例子: #有评分太低的, GET /songs_v1/popular/_search { "query": { "match": { "lyrics": "So many people all around the world" } } } #加上min_score 去掉评分低的 GET /songs_v1/_search { "query": { "match": { "lyrics": "so many people all ...
简单的说,就是加强score = min(加强score, max_boost) 4. function_score查询模板 如果要使用function_score改变分数,要使用function_score查询 简单的说,就是在一个function_score内部的query的全文搜索得到的_score基础上,给他加上其他字段的评分标准,就能够得到把 "全文搜索 + 其他字段" 综合起来评分的效果 ...
1.max ,min, avg, sum 以avg为例, 计算offset在所有文档中的均值 GET/logstash-bus*/_search{"query":{"bool":{"must":[{"match":{"prodSubNo":"601001"}},{"match":{"maiDianType":"request_execute"}}]}},"_source":["prodSubNo","useTime","channelInto","maiDianType","bussEventId"...