算法002.深度学习002.常见算法

xgboost

Catalogue 
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<dependency>
    <groupId>ml.dmlc</groupId>
    <artifactId>xgboost4j_2.12</artifactId>
    <version>1.0.0</version>
    <exclusions>
        <exclusion>
            <groupId>com.typesafe.akka</groupId>
            <artifactId>akka-actor_2.11</artifactId>
        </exclusion>
        <exclusion>
            <artifactId>scala-library</artifactId>
            <groupId>org.scala-lang</groupId>
        </exclusion>
        <exclusion>
            <artifactId>scala-compiler</artifactId>
            <groupId>org.scala-lang</groupId>
        </exclusion>
        <exclusion>
            <artifactId>asm</artifactId>
            <groupId>org.ow2.asm</groupId>
        </exclusion>
        <exclusion>
            <artifactId>objenesis</artifactId>
            <groupId>org.objenesis</groupId>
        </exclusion>
    </exclusions>
</dependency>
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private Booster model = null;
// load
model = XGBoost.loadModel(rerankProperties.getModelPath());


// calScore
private float[][] calScore(float[] featureValues, int size, int featureTotalDim) throws XGBoostError {
    DMatrix mat = new DMatrix(featureValues, size, featureTotalDim);

    if (!rerankProperties.isRankOff() && model == null) {
        load();
    }

    return model.predict(mat);
}

// predict
int samples = recalls.size();
float[] features = new float[samples * dimSize];
for (int i = 0; i < samples; i++) {
    String id = recalls.get(i).getId();
    String uid = recalls.get(i).getAId();
    Feature feature = features.get(jobId);
    CFeature cFeature = cFeatures.get(uid);
    float[] b = ModelFeature.transform(searchReq, queryFeature, aFeature, bFeature, cFeature, dimSize);
    System.arraycopy(b, 0, features, i * dimSize, b.length);
}
float[][] result = calScore(features, samples, dimSize);

模式流、模式、
Guardian Baize of Bot-flow Detection

FlowSentryZ 流量哨兵白泽(Baize)