|  | @@ -25,18 +25,15 @@ public class RandomPredictModel extends ThresholdPredictModel {
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				|  |  |          int hash=modelParam.getMid().hashCode();
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				|  |  |          hash=hash<0?-hash:hash;
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				|  |  |          double score=(hash+ RandWContainer.getRandW())%100/100d;
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				|  |  | -        double threshold=Double.parseDouble(
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				|  |  | -                modelParam.getExtraParam().getOrDefault(modelParam.getAppType()+"_"+modelParam.getUserExtraFuture("shareType").toString().replace("return","").replace("mids",""),-1
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				|  |  | -                ).toString());
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				|  |  | -        if(threshold<0d){
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				|  |  | -            threshold=Double.parseDouble(
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				|  |  | -                    modelParam.getExtraParam().getOrDefault("default_threshold","0.5")
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				|  |  | -                    .toString());
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				|  |  | +
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				|  |  | +        String keySuffix = modelParam.getUserExtraFuture("shareType").toString().replace("return", "").replace("mids", "");
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				|  |  | +        Integer appType = modelParam.getAppType();
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				|  |  | +
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				|  |  | +        double threshold = Double.parseDouble(modelParam.getExtraParam().getOrDefault(appType + "_" + keySuffix, -1).toString());
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				|  |  | +        if (threshold < 0d) {
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				|  |  | +            threshold = Double.parseDouble(modelParam.getExtraParam().getOrDefault("default_" + keySuffix, "0.5").toString());
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				|  |  |          }
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				|  |  | -        double correction=Double.parseDouble(
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				|  |  | -                modelParam.getExtraParam()
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				|  |  | -                        .getOrDefault("E620_"+modelParam.getAppType()+"_"+DateUtils.getCurrentHour(),1
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				|  |  | -                ).toString());
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				|  |  | +        double correction = Double.parseDouble(modelParam.getExtraParam().getOrDefault("E620_" + appType + "_" + DateUtils.getCurrentHour(), 1).toString());
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				|  |  |          threshold=threshold*correction;
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				|  |  |          Map<String, Object> result = new HashMap<>();
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				|  |  |          result.put("ad_predict", score<threshold?2:1);
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