Spark MLlib提供了一些基本的统计学的算法，下面主要说明一下：
1、Summary statistics
对于RDD[Vector]类型，Spark MLlib提供了colStats的统计方法，该方法返回一个MultivariateStatisticalSummary的实例。他封装了列的最大值，最小值，均值、方差、总数。如下所示：
val conf = new SparkConf().setAppName("Simple Application").setMaster("yarn-cluster")
val sc = new SparkContext(conf)
val observations = sc.textFile("/user/liujiyu/spark/mldata1.txt")
.map(_.split(' ') // 转换为RDD[Array[String]]类型
.map(_.toDouble)) // 转换为RDD[Array[Double]]类型
.map(line => Vectors.dense(line)) //转换为RDD[Vector]类型
// Compute column summary statistics.
val summary: MultivariateStatisticalSummary = Statistics.colStats(observations)
println(summary.mean) // a dense vector containing the mean value for each column
println(summary.variance) // column-wise variance
println(summary.numNonzeros) // number of nonzeros in each column
2、Correlations（相关性）
计算两个序列的相关性，提供了计算Pearson’s and Spearman’s correlation.如下所示：
val conf = new SparkConf().setAppName("Simple Application").setMaster("yarn-cluster")
val sc = new SparkContext(conf)
val observations = sc.textFile("/user/liujiyu/spark/mldata1.txt")
val data1 = Array(1.0, 2.0, 3.0, 4.0, 5.0)
val data2 = Array(1.0, 2.0, 3.0, 4.0, 5.0)
val distData1: RDD[Double] = sc.parallelize(data1)
val distData2: RDD[Double] = sc.parallelize(data2) // must have the same number of partitions and cardinality as seriesX
// compute the correlation using Pearson's method. Enter "spearman" for Spearman's method. If a
// method is not specified, Pearson's method will be used by default.
val correlation: Double = Statistics.corr(distData1, distData2, "pearson")
val data: RDD[Vector] = observations // note that each Vector is a row and not a column
// calculate the correlation matrix using Pearson's method. Use "spearman" for Spearman's method.
// If a method is not specified, Pearson's method will be used by default.
val correlMatrix: Matrix = Statistics.corr(data, "pearson")
