成功秀了一波scala spark ML逻辑斯蒂回归

package com.test
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.classification.{LogisticRegressionModel, LogisticRegressionWithLBFGS}
import org.apache.spark.mllib.evaluation.MulticlassMetrics
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.util.MLUtilsobject logsitiRcongin { def main(args: Array[String]): Unit = {
val conf = new SparkConf().setMaster("local").setAppName("df")
val sc = new SparkContext(conf) // Load training data in LIBSVM format.
val data = MLUtils.loadLibSVMFile(sc, "E:\\spackLearn\\spark-2.3.3-bin-hadoop2.7\\data\\mllib\\sample_libsvm_data.txt") // Split data into training (60%) and test (40%).
val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L)
val training = splits(0).cache()
val test = splits(1) // Run training algorithm to build the model
val model = new LogisticRegressionWithLBFGS()
.setNumClasses(10)
.run(training) // Compute raw scores on the test set.
val predictionAndLabels = test.map { case LabeledPoint(label, features) =>
val prediction = model.predict(features)
(prediction, label)
} // Get evaluation metrics.
val metrics = new MulticlassMetrics(predictionAndLabels)
val accuracy = metrics.accuracy
println(s"最后的得分：Accuracy = $accuracy") // Save and load model
model.save(sc, "data/model/scalaLogisticRegressionWithLBFGSModel")
val sameModel = LogisticRegressionModel.load(sc, "data/model/scalaLogisticRegressionWithLBFGSModel") while (true){
} }
}