Spark中组件Mllib的学习20之假设检验-卡方检验

更多代码请见：https://github.com/xubo245/SparkLearning
Spark中组件Mllib的学习之基础概念篇
1解释
分别对Vector和Matrix进行卡方检验

2.代码：

/**
  * @author xubo
  *         ref:Spark MlLib机器学习实战
  *         more code:https://github.com/xubo245/SparkLearning
  *         more blog:http://blog.csdn.net/xubo245
  */
package org.apache.spark.mllib.learning.basic

import org.apache.spark.mllib.linalg.{Matrices, Vectors}
import org.apache.spark.mllib.stat.Statistics
import org.apache.spark.{SparkConf, SparkContext}

/**
  * Created by xubo on 2016/5/23.
  */
object ChiSqLearning {
  def main(args: Array[String]) {
    val vd = Vectors.dense(1, 2, 3, 4, 5)
    val vdResult = Statistics.chiSqTest(vd)
    println(vdResult)
    println("-------------------------------")
    val mtx = Matrices.dense(3, 2, Array(1, 3, 5, 2, 4, 6))
    val mtxResult = Statistics.chiSqTest(mtx)
    println(mtxResult)
    //print :方法、自由度、方法的统计量、p值
  }
}

3.结果：

Chi squared test summary:
method: pearson
degrees of freedom = 4 
statistic = 3.333333333333333 
pValue = 0.5036682742334986 
No presumption against null hypothesis: observed follows the same distribution as expected..
-------------------------------
Chi squared test summary:
method: pearson
degrees of freedom = 2 
statistic = 0.14141414141414144 
pValue = 0.931734784568187 
No presumption against null hypothesis: the occurrence of the outcomes is statistically independent..

参考
【1】http://spark.apache.org/docs/1.5.2/mllib-guide.html
【2】http://spark.apache.org/docs/1.5.2/programming-guide.html
【3】https://github.com/xubo245/SparkLearning