本文整理汇总了Scala中org.apache.spark.ml.regression.GeneralizedLinearRegression类的典型用法代码示例。如果您正苦于以下问题：Scala GeneralizedLinearRegression类的具体用法？Scala GeneralizedLinearRegression怎么用？Scala GeneralizedLinearRegression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。
在下文中一共展示了GeneralizedLinearRegression类的1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Scala代码示例。
示例1: GLMRegression
//设置package包名称以及导入依赖的类
package com.databricks.spark.sql.perf.mllib.regression
import org.apache.spark.ml.evaluation.{Evaluator, RegressionEvaluator}
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.ml.regression.GeneralizedLinearRegression
import org.apache.spark.ml.{Estimator, ModelBuilder, Transformer}
import com.databricks.spark.sql.perf.mllib.OptionImplicits._
import com.databricks.spark.sql.perf.mllib._
import com.databricks.spark.sql.perf.mllib.data.DataGenerator
object GLMRegression extends BenchmarkAlgorithm with TestFromTraining with
TrainingSetFromTransformer with ScoringWithEvaluator {
override protected def initialData(ctx: MLBenchContext) = {
import ctx.params._
DataGenerator.generateContinuousFeatures(
ctx.sqlContext,
numExamples,
ctx.seed(),
numPartitions,
numFeatures)
}
override protected def trueModel(ctx: MLBenchContext): Transformer = {
import ctx.params._
val rng = ctx.newGenerator()
val coefficients =
Vectors.dense(Array.fill[Double](ctx.params.numFeatures)(2 * rng.nextDouble() - 1))
// Small intercept to prevent some skew in the data.
val intercept = 0.01 * (2 * rng.nextDouble - 1)
val m = ModelBuilder.newGLR(coefficients, intercept)
m.set(m.link, link.get)
m.set(m.family, family.get)
m
}
override def getEstimator(ctx: MLBenchContext): Estimator[_] = {
import ctx.params._
new GeneralizedLinearRegression()
.setLink(link)
.setFamily(family)
.setRegParam(regParam)
.setMaxIter(maxIter)
.setTol(tol)
}
override protected def evaluator(ctx: MLBenchContext): Evaluator =
new RegressionEvaluator()
}