在做資料探勘和機器學習專案的時候發現矩陣運算需要經常用到，雖然Java中提供了Jama包能實現大部分需求，但是無法滿足定製化需求。我們寫spark程式的時候一般使用scala,所以用scala實現了一些矩陣的類。程式碼實現了矩陣加、乘、轉置、求協方差、求平均等。後續會繼續擴充，用以實現特許矩陣操作需求。

class Matrix(private val data:Array[Double],private val rownum:Int){
    val colnum = (data.length.toDouble/rownum).ceil.toInt
    private val matrix:Array[Array[Double]]={ 
        val
 
 matrix:Array[Array[Double]] = Array.ofDim[Double](rownum,colnum)
        for(i <- 0 until rownum){
            for(j <- 0 until colnum){
                val index = i * colnum + j
                matrix(i)(j) = if(data.isDefinedAt(index)) data(index) else 0
            }
        }
        matrix
    }

    override
 
 def toString = {
        var str = ""
        matrix.map((p:Array[Double]) => {p.mkString(" ")}).mkString("\n")
    }

    def mat(row:Int,col:Int) = {
        matrix(row - 1)(col - 1)
    }

    def *(a:Matrix) = {
        if(this.colnum != a.rownum){
        }else{
            val data:ArrayBuffer[Double] = ArrayBuffer()
            for
 
(i <- 0 until this.rownum){
                for(j <- 0 until a.colnum){
                    var num = 0.0
                    for(k <- 0 until this.colnum){
                        num += this.matrix(i)(k) * a.matrix(k)(j)
                    }
                data += num
                }
            }
        new Matrix(data.toArray,this.rownum)
        }
    }

    def *(a:Double) = {
        val data:ArrayBuffer[Double] = ArrayBuffer()
        for(i <- 0 until this.rownum){
            for(j <- 0 until this.colnum){
                data += this.matrix(i)(j) * a
            }
        }
        new Matrix(data.toArray,this.rownum)
    }

    def +(a:Matrix) = {
        if(this.rownum != a.rownum || this.colnum != a.colnum){
        }else{
            val data:ArrayBuffer[Double] = ArrayBuffer()
            for(i <- 0 until this.rownum){
                for(j <- 0 until this.colnum){
                    data += this.matrix(i)(j) + a.matrix(i)(j)
                }
            }
            new Matrix(data.toArray,this.rownum)
        }       
    }

    def transpose() = {
        val transposeMatrix = for (i <- Array.range(0,colnum)) yield {
             for (rowArray <- this.matrix) yield rowArray(i)
            }
        new Matrix(transposeMatrix.flatten,colnum)
    }

    def cov() = {
        val data:ArrayBuffer[Double] = ArrayBuffer()
        for(i <- 0 until this.transpose.rownum){
            for(j <- 0 until this.colnum){
                var num = 0.0
                for(k <- 0 until this.transpose.colnum){
                    num += this.transpose.matrix(i)(k) * this.matrix(k)(j)
                }
            data += num
            }
        }
        new Matrix(data.toArray,this.transpose.rownum)*(1.toDouble/this.rownum)
    }

    def mean() = {
        val meanMatrix:Array[Array[Double]] = Array.ofDim[Double](rownum,colnum)
        val propertyMean:Array[Double] = new Array[Double](colnum)
        for(j <- 0 until colnum){
            var propertyValueSum = 0.0
            for(i <- 0 until rownum){
                propertyValueSum += this.matrix(i)(j)
            }
            propertyMean(j) = propertyValueSum/rownum
        }
        for(j <- 0 until colnum){
            for(i <- 0 until rownum){
                meanMatrix(i)(j) = this.matrix(i)(j) - propertyMean(j)
            }
        }
        new Matrix(meanMatrix.flatten,rownum)
    }
}

實驗驗證：
val matrix = new Matrix(getDateFromFile,150)
println(matrix.mean.cov)

驗證資料（矩陣）：
5.1, 3.5, 1.4 , 0.2
4.9, 3.0, 1.4 , 0.2
4.7, 3.2, 1.3 , 0.2
4.6, 3.1, 1.5 , 0.2
5.0, 3.6, 1.4 , 0.2
5.4, 3.9, 1.7 , 0.4
4.6, 3.4, 1.4 , 0.3
5.0, 3.4, 1.5 , 0.2
4.4, 2.9, 1.4 , 0.2
4.9, 3.1, 1.5 , 0.1
5.4, 3.7, 1.5 , 0.2
4.8, 3.4, 1.6 , 0.2
4.8, 3.0, 1.4 , 0.1
4.3, 3.0, 1.1 , 0.1
5.8, 4.0, 1.2 , 0.2
5.7, 4.4, 1.5 , 0.4
5.4, 3.9, 1.3 , 0.4
5.1, 3.5, 1.4 , 0.3
5.7, 3.8, 1.7 , 0.3
5.1, 3.8, 1.5 , 0.3
5.4, 3.4, 1.7 , 0.2
5.1, 3.7, 1.5 , 0.4
4.6, 3.6, 1.0 , 0.2
5.1, 3.3, 1.7 , 0.5
4.8, 3.4, 1.9 , 0.2
5.0, 3.0, 1.6 , 0.2
5.0, 3.4, 1.6 , 0.4
5.2, 3.5, 1.5 , 0.2
5.2, 3.4, 1.4 , 0.2
4.7, 3.2, 1.6 , 0.2
4.8, 3.1, 1.6 , 0.2
5.4, 3.4, 1.5 , 0.4
5.2, 4.1, 1.5 , 0.1
5.5, 4.2, 1.4 , 0.2
4.9, 3.1, 1.5 , 0.1
5.0, 3.2, 1.2 , 0.2
5.5, 3.5, 1.3 , 0.2
4.9, 3.1, 1.5 , 0.1
4.4, 3.0, 1.3 , 0.2
5.1, 3.4, 1.5 , 0.2
5.0, 3.5, 1.3 , 0.3
4.5, 2.3, 1.3 , 0.3
4.4, 3.2, 1.3 , 0.2
5.0, 3.5, 1.6 , 0.6
5.1, 3.8, 1.9 , 0.4
4.8, 3.0, 1.4 , 0.3
5.1, 3.8, 1.6 , 0.2
4.6, 3.2, 1.4 , 0.2
5.3, 3.7, 1.5 , 0.2
5.0, 3.3, 1.4 , 0.2
7.0, 3.2, 4.7 , 1.4
6.4, 3.2, 4.5 , 1.5
6.9, 3.1, 4.9 , 1.5
5.5, 2.3, 4.0 , 1.3
6.5, 2.8, 4.6 , 1.5
5.7, 2.8, 4.5 , 1.3
6.3, 3.3, 4.7 , 1.6
4.9, 2.4, 3.3 , 1.0
6.6, 2.9, 4.6 , 1.3
5.2, 2.7, 3.9 , 1.4
5.0, 2.0, 3.5 , 1.0
5.9, 3.0, 4.2 , 1.5
6.0, 2.2, 4.0 , 1.0
6.1, 2.9, 4.7 , 1.4
5.6, 2.9, 3.6 , 1.3
6.7, 3.1, 4.4 , 1.4
5.6, 3.0, 4.5 , 1.5
5.8, 2.7, 4.1 , 1.0
6.2, 2.2, 4.5 , 1.5
5.6, 2.5, 3.9 , 1.1
5.9, 3.2, 4.8 , 1.8
6.1, 2.8, 4.0 , 1.3
6.3, 2.5, 4.9 , 1.5
6.1, 2.8, 4.7 , 1.2
6.4, 2.9, 4.3 , 1.3
6.6, 3.0, 4.4 , 1.4
6.8, 2.8, 4.8 , 1.4
6.7, 3.0, 5.0 , 1.7
6.0, 2.9, 4.5 , 1.5
5.7, 2.6, 3.5 , 1.0
5.5, 2.4, 3.8 , 1.1
5.5, 2.4, 3.7 , 1.0
5.8, 2.7, 3.9 , 1.2
6.0, 2.7, 5.1 , 1.6
5.4, 3.0, 4.5 , 1.5
6.0, 3.4, 4.5 , 1.6
6.7, 3.1, 4.7 , 1.5
6.3, 2.3, 4.4 , 1.3
5.6, 3.0, 4.1 , 1.3
5.5, 2.5, 4.0 , 1.3
5.5, 2.6, 4.4 , 1.2
6.1, 3.0, 4.6 , 1.4
5.8, 2.6, 4.0 , 1.2
5.0, 2.3, 3.3 , 1.0
5.6, 2.7, 4.2 , 1.3
5.7, 3.0, 4.2 , 1.2
5.7, 2.9, 4.2 , 1.3
6.2, 2.9, 4.3 , 1.3
5.1, 2.5, 3.0 , 1.1
5.7, 2.8, 4.1 , 1.3
6.3, 3.3, 6.0 , 2.5
5.8, 2.7, 5.1 , 1.9
7.1, 3.0, 5.9 , 2.1
6.3, 2.9, 5.6 , 1.8
6.5, 3.0, 5.8 , 2.2
7.6, 3.0, 6.6 , 2.1
4.9, 2.5, 4.5 , 1.7
7.3, 2.9, 6.3 , 1.8
6.7, 2.5, 5.8 , 1.8
7.2, 3.6, 6.1 , 2.5
6.5, 3.2, 5.1 , 2.0
6.4, 2.7, 5.3 , 1.9
6.8, 3.0, 5.5 , 2.1
5.7, 2.5, 5.0 , 2.0
5.8, 2.8, 5.1 , 2.4
6.4, 3.2, 5.3 , 2.3
6.5, 3.0, 5.5 , 1.8
7.7, 3.8, 6.7 , 2.2
7.7, 2.6, 6.9 , 2.3
6.0, 2.2, 5.0 , 1.5
6.9, 3.2, 5.7 , 2.3
5.6, 2.8, 4.9 , 2.0
7.7, 2.8, 6.7 , 2.0
6.3, 2.7, 4.9 , 1.8
6.7, 3.3, 5.7 , 2.1
7.2, 3.2, 6.0 , 1.8
6.2, 2.8, 4.8 , 1.8
6.1, 3.0, 4.9 , 1.8
6.4, 2.8, 5.6 , 2.1
7.2, 3.0, 5.8 , 1.6
7.4, 2.8, 6.1 , 1.9
7.9, 3.8, 6.4 , 2.0
6.4, 2.8, 5.6 , 2.2
6.3, 2.8, 5.1 , 1.5
6.1, 2.6, 5.6 , 1.4
7.7, 3.0, 6.1 , 2.3
6.3, 3.4, 5.6 , 2.4
6.4, 3.1, 5.5 , 1.8
6.0, 3.0, 4.8 , 1.8
6.9, 3.1, 5.4 , 2.1
6.7, 3.1, 5.6 , 2.4
6.9, 3.1, 5.1 , 2.3
5.8, 2.7, 5.1 , 1.9
6.8, 3.2, 5.9 , 2.3
6.7, 3.3, 5.7 , 2.5
6.7, 3.0, 5.2 , 2.3
6.3, 2.5, 5.0 , 1.9
6.5, 3.0, 5.2 , 2.0
6.2, 3.4, 5.4 , 2.3
5.9, 3.0, 5.1 , 1.8

驗證結果：
0.6811222222222222 -0.03900666666666667 1.2651911111111114 0.513457777777778
-0.03900666666666667 0.18675066666666673 -0.31956800000000013 -0.11719466666666663
1.2651911111111114 -0.31956800000000013 3.092424888888886 1.2877448888888894
0.513457777777778 -0.11719466666666663 1.2877448888888894 0.5785315555555559