本文整理汇总了Java中org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS类的典型用法代码示例。如果您正苦于以下问题：Java LogisticRegressionWithLBFGS类的具体用法？Java LogisticRegressionWithLBFGS怎么用？Java LogisticRegressionWithLBFGS使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
LogisticRegressionWithLBFGS类属于org.apache.spark.mllib.classification包，在下文中一共展示了LogisticRegressionWithLBFGS类的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: trainWithLBFGS
import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS; //导入依赖的package包/类
@SuppressWarnings("unchecked")
public T trainWithLBFGS(){
//Train the model
if(modelName.equals("LogisticRegressionModel")){
LogisticRegressionModel lrmodel = new LogisticRegressionWithLBFGS()
.setNumClasses(numClasses)
.run(trainingData.rdd());
System.out.println("\n--------------------------------------\n weights: " + lrmodel.weights());
System.out.println("--------------------------------------\n");
this.model = (T)(Object) lrmodel;
}
//Evalute the trained model
EvaluateProcess<T> evalProcess = new EvaluateProcess<T>(model, modelName, validData, numClasses);
evalProcess.evalute(numClasses);
return model;
}
示例2: generateKMeansModel
import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS; //导入依赖的package包/类
public LogisticRegressionModel generateKMeansModel(JavaRDD<LabeledPoint> parsedData,
LogisticRegressionDetectionAlgorithm logisticRegressionDetectionAlgorithm,
LogisticRegressionModelSummary logisticRegressionModelSummary) {
LogisticRegressionModel model
= new LogisticRegressionWithLBFGS()
.setNumClasses(logisticRegressionDetectionAlgorithm.getNumClasses())
.run(parsedData.rdd());
logisticRegressionModelSummary.setLogisticRegressionDetectionAlgorithm(logisticRegressionDetectionAlgorithm);
return model;
}
示例3: ModelLogisticRegression
import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS; //导入依赖的package包/类
public ModelLogisticRegression(JavaRDD<LabeledPoint> training) {
super();
// Run training algorithm to build the model.
model = new LogisticRegressionWithLBFGS().setNumClasses(2).run(training.rdd());
// Clear the prediction threshold so the model will return probabilities
model.clearThreshold();
}
示例4: trainWithLBFGS
import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS; //导入依赖的package包/类
/**
* This method uses LBFGS optimizer to train a logistic regression model for a given dataset
*
* @param trainingDataset Training dataset as a JavaRDD of labeled points
* @param noOfClasses No of classes
* @param regularizationType Regularization type
* @return Logistic regression model
*/
public LogisticRegressionModel trainWithLBFGS(JavaRDD<LabeledPoint> trainingDataset, String regularizationType,
int noOfClasses) {
LogisticRegressionWithLBFGS lbfgs = new LogisticRegressionWithLBFGS();
if (MLConstants.L1.equals(regularizationType)) {
lbfgs.optimizer().setUpdater(new L1Updater());
} else if (MLConstants.L2.equals(regularizationType)) {
lbfgs.optimizer().setUpdater(new SquaredL2Updater());
}
lbfgs.setIntercept(true);
return lbfgs.setNumClasses(noOfClasses < 2 ? 2 : noOfClasses).run(trainingDataset.rdd());
}