sparkMLlib中breeze常用api

package leaning import breeze.linalg._ import breeze.numerics._ import breeze.stats.distributions.Rand /** * Created by dy9776 on 2017/12/5. */ object Practise_breeze{ def main(args: Array[String]) { val matrix: DenseMatrix[Double] = DenseMatrix.zeros[Double](3,2) println(matrix) /* 0.0 0.0 0.0 0.0 0.0 0.0 */ //全0向量 val testVector: DenseVector[Double] = DenseVector.zeros[Double](2) println(testVector) //全1向量 val allOneVector=DenseVector.ones[Double](2) println(allOneVector) //按数值填充向量 val haveNumberFill =DenseVector.fill[Double](3,2) println(haveNumberFill) //生成随机向量 val rangeNUm= DenseVector.range(1, 10 , 2)//DenseVector(1, 3, 5, 7, 9) val rangeNUmD= DenseVector.rangeD(1, 9 , 2)//DenseVector(1.0, 3.0, 5.0, 7.0) val rangeNUmF= DenseVector.rangeF(1, 7 , 2)//DenseVector(1.0, 3.0, 5.0) println(rangeNUm) println(rangeNUmD) println(rangeNUmF) //单位矩阵 val unitMatrix=DenseMatrix.eye[Double](4) // println(unitMatrix) /* 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 */ //对角矩阵 val doubleVecoter=diag(DenseVector(3.0, 4.0 , 5.0)) // println(doubleVecoter) /* 3.0 0.0 0.0 0.0 4.0 0.0 0.0 0.0 5.0 */ //按照行创建矩阵 val byRowCreateMatrix= DenseMatrix( (4.0, 5.0, 6.0 ) , (7.0 ,8.0 ,9.0)) // println(byRowCreateMatrix) /* 4.0 5.0 6.0 7.0 8.0 9.0 */ //按照行创建向量 val denseCreateVector = DenseVector((4.0, 5.0, 6.0, 7.0, 8.0, 9.0)) // println(denseCreateVector) ///DenseVector((4.0,5.0,6.0,7.0,8.0,9.0) //向量装置 val vectorTranspostion= DenseVector( (4.0, 5.0, 6.0, 7.0, 8.0, 9.0) ).t println(vectorTranspostion)//Transpose(DenseVector((4.0,5.0,6.0,7.0,8.0,9.0))) //从函数创建向量 val funCreateVector=DenseVector.tabulate(5)(i=> i*i) println(funCreateVector)//DenseVector(0, 1, 4, 9, 16) val funCreateVector2=DenseVector.tabulate( 0 to 5)(i=> i*i) //矩阵行数 } } println( abs(h) ) // breeze.linalg.DenseVector[Double] = DenseVector(1.2, 0.7, 2.3) //取正数 println( signum(h) ) // breeze.linalg.DenseVector[Double] = DenseVector(-1.0, 1.0, 1.0) //符号函数 println( floor(h) ) // breeze.linalg.DenseVector[Double] = DenseVector(-2.0, 0.0, 2.0) //小于它的最大整数 println( ceil(h) ) // breeze.linalg.DenseVector[Double] = DenseVector(-1.0, 1.0, 3.0) //大于它的最小整数 println( round(h) ) // breeze.linalg.DenseVector[Long] = DenseVector(-1, 1, 2) //四舍五入 val h = DenseVector(-1.2, 0.7, 2.3) // breeze.linalg.DenseVector[Double] = DenseVector(-1.2, 0.7, 2.3) //Breeze取整函数 f.cols // 3 //矩阵列数 println(f.rows) // 3 println(f.size) //9 //矩阵长度 println(rank(f)) //2 //求矩阵的秩 */ 0.816496580927726 -0.4082482904638629 -0.4082482904638628 0.0 -0.7071067811865474 0.7071067811865477 -0.5773502691896256 -0.5773502691896257 -0.5773502691896256 /* println(v) println("==============================") println(s) //DenseVector(3.0000000000000004, 0.0, 0.0) println("==============================") */ -0.5773502691896256 0.7886751345948129 -0.21132486540518708 -0.5773502691896256 -0.2113248654051871 0.7886751345948126 -0.5773502691896255 -0.5773502691896257 -0.5773502691896256 /* println(u) val svd.SVD(u,s,v) = svd(g) //奇异值分解 */ ) -0.8186734993561815 0.61232756022881 0.4082482904638625 -0.5253220933012336 -0.08675133925662833 -0.816496580927726 Eig(DenseVector(16.116843969807043, -1.1168439698070427, -1.3036777264747022E-15),DenseVector(0.0, 0.0, 0.0),-0.23197068724628617 -0.7858302387420671 0.40824829046386363 /* println(eig(f)) //特征值和特征向量 */ -3.772083401933041E14 7.544166803866104E14 -3.772083401933055E14 7.544166803866094E14 -1.5088333607732208E15 7.544166803866108E14 -3.7720834019330525E14 7.544166803866101E14 -3.77208340193305E14 /* println(pinv(f)) //求伪逆 */ -4.503599627370498E15 9.007199254740992E15 -4.5035996273704955E15 9.007199254740998E15 -1.8014398509481984E16 9.007199254740991E15 -4.503599627370499E15 9.007199254740992E15 -4.503599627370495E15 /* println(inv(f)) //求逆 println(det(f)) // Double = 6.661338147750939E-16 //求特征值 */ 3.0 6.0 9.0 2.0 5.0 8.0 1.0 4.0 7.0 /* breeze.linalg.DenseMatrix[Double] = println(f.t) //转置 */ -1.5 -1.5 -1.5 4.0 4.0 4.0 -2.5 -2.5 -2.5 /* breeze.linalg.DenseMatrix[Double] = println(f \ g) //线性求解，AX = B，求解X val g = DenseMatrix((1.0, 1.0, 1.0), (1.0, 1.0, 1.0), (1.0, 1.0, 1.0)) val f = DenseMatrix((1.0, 2.0, 3.0), (4.0, 5.0, 6.0), (7.0, 8.0, 9.0)) //Breeze线性代数函数 println(all(e)) //false //所有元素非零 println(any(e)) //true //存在非零元素 val e = DenseVector[Int](-3, 0, 2) println(!c) //DenseVector(false, true, false) //元素非操作 println(c :| d) //DenseVector(true, true, true) //元素或操作 println(c :& d) // DenseVector(false, false, true) //元素与操作 val d = DenseVector(false, true, true) val c = DenseVector(true, false, true) //Breeze布尔函数 println(accumulate(a3)) // DenseVector(10, 21, 33, 46, 60, 75, 91, 108, 126, 145, 165) val a3 = new DenseVector[Int](Array(10 to 20: _*)) //累积和 println(trace(lowerTriangular(m1)))// res61: Double = 18.0 //对角线元素和 println(sum(m1, Axis._1))//res60: breeze.linalg.DenseVector[Double] = DenseVector(10.0, 26.0, 42.0) //每一行求和 println(sum(m1, Axis._0))//res59: breeze.linalg.DenseMatrix[Double] = 15.0 18.0 21.0 24.0 //每一列求和 println(sum(m1))//78.0 //元素求和 println("-==========================-") */ 9.0 10.0 11.0 12.0 5.0 6.0 7.0 8.0 1.0 2.0 3.0 4.0 /* println(m1) val m1 = DenseMatrix((1.0, 2.0, 3.0, 4.0), (5.0, 6.0, 7.0, 8.0), (9.0, 10.0, 11.0, 12.0)) //Breeze求和函数 println(argmin(a1))// (0,0) //元素最小值的位置 println(argmax(a1))// (1,2) //元素最大值的位置 println(min(a1))//6.0 //元素最小值 println(max(a1))//16.0 //元素最大值 println(vectorDot)//10 val vectorDot=DenseVector(1, 2, 3, 4) dot DenseVector(1, 1, 1, 1) //向量点积 */ 12.0 14.0 16.0 6.0 8.0 10.0 /* println(a1 :*=2.0) //元素追乘 */ 6.0 7.0 8.0 3.0 4.0 5.0 /* println(a1 :+=2.0) //元素追加 */ false false false false false false /* println(a1 :== a2) //元素相等 */ true true true true true true /* println(a1 :< a2) //元素比较 */ 0.4 0.45454545454545453 0.5 0.14285714285714285 0.25 0.3333333333333333 /* println(a1 :/ a2) //元素除法 */ 40.0 55.0 72.0 7.0 16.0 27.0 /* println(a1 :* a2) //元素乘法 */ 14.0 16.0 18.0 8.0 10.0 12.0 /* println(a1 + a2) //元素加法 //Breeze数值计算函数 */ 22 25 21 24 20 23 /* println(connnectVector2) println(connnectVector1)//DenseVector(20, 21, 22, 23, 24, 25) val connnectVector2=DenseVector.horzcat(DenseVector(20, 21, 22), DenseVector(23, 24, 25)) val connnectVector1=DenseVector.vertcat(DenseVector(20, 21, 22), DenseVector(23, 24, 25)) //向量的连接 */ 4.0 5.0 6.0 10.0 11.0 12.0 1.0 2.0 3.0 7.0 8.0 9.0 /* println("-==========================-") println(twoMatrixConn) val twoMatrixConn=DenseMatrix.horzcat( a1, a2) //横向连接矩阵 */ 10.0 11.0 12.0 7.0 8.0 9.0 4.0 5.0 6.0 1.0 2.0 3.0 /* println("-==========================-") println(verticalLike) val verticalLike=DenseMatrix.vertcat(a1, a2) //垂直连接矩阵 val a2 = DenseMatrix((7.0, 8.0, 9.0), (10.0, 11.0, 12.0)) val a1 = DenseMatrix((1.0, 2.0, 3.0), (4.0, 5.0, 6.0)) //5.0 5.0 println(re.toDenseMatrix) val re=m(::, 2) := 5.0 //矩阵列赋值 */ -==========m end================- 0.0 0.0 6.0 0.0 0.0 3.0 -==========m================- 0.0 0.0 0.0 0.0 -==========m1================- /* println("-==========m end================-") println(m) println("-==========m================-") println( m( 0 to 1, 1 to 2) := 0.0 ) println("-==========m1================-") //Exception in thread "main" java.lang.IndexOutOfBoundsException: Row slice of Range(1, 2) was bigger than matrix rows of 2 // println( m( 1 to 2, 1 to 2) := 0.0 ) //矩阵赋值 println(m) //DenseVector(1, 2, 3, 4) println( a(1 to 4):=DenseVector(1,2,3,4) ) //子集赋向量 //(1.0, 5.0) println(a(1 to 4 ):=5) //子集赋数值 //DenseVector(1.0, 5.0) 个人觉得很怪异，不是应该为(1.0, 6.0)吗？上面上、下三角 也好像出乎的意料 println(diag(upperTriangular(m))) //取对角线元素 // 0.0 5.0 // 1.0 2.0 //m 原始为这样的 println(m.copy) //矩阵复制 */ 0.0 5.0 1.0 2.0 /* println(upperTriangular(m)) //复制上三角 */ 4.0 5.0 1.0 0.0 /* println(lowerTriangular(m))// //复制下三角 println(toVector.toDenseMatrix)//1.0 4.0 2.0 5.0 3.0 6.0 println(toVector)//DenseVector(1.0, 4.0, 2.0, 5.0, 3.0, 6.0) val toVector=m.toDenseVector //矩阵转成向量 */ 2.0 6.0 4.0 3.0 1.0 5.0 DenseVector(2.0, 5.0) /* println(justAdjustMatrix) val justAdjustMatrix =m.reshape(3, 2) //调整矩阵形状 //Breeze元素操作 println( m(:: ,1) ) // DenseVector(2.0, 5.0) //矩阵指定列 println( m(0 ,1) ) //2.0 //指定位置 */ 4.0 5.0 6.0 1.0 2.0 3.0 /* println(m) val m = DenseMatrix((1.0, 2.0, 3.0), (4.0, 5.0, 6.0)) println( a(-1)) //20 //最后一个元素 println( a(5 to 0 by -1) )//DenseVector(6, 5, 4, 3, 2, 1) //按照指定步长去子集 这个是倒序方式 println( a(1 to -1) )//DenseVector(2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20) //指定开始位置至结尾 println( a(1 until 4) )//DenseVector(2, 3, 4) println( a(1 to 4) )//DenseVector(2, 3, 4, 5) //向量子集 println(a(0)) //1 //指定位置 println(a)//DenseVector(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20) val a = new DenseVector[Int](Array(1 to 20 : _*)) //Breeze元素访问 */ -0.3959295333472151 -0.9057610233257112 -0.5266499883462216 0.9380426076781263 0.9510499901472648 0.287812938654061 /* println(formZeroToOneRandomMatrix2) val formZeroToOneRandomMatrix2=DenseMatrix.rand(3, 2, Rand.gaussian) */ 0.9458628172312897 0.01137554621536796 0.6864375371630702 0.3123993272549075 0.8036324612618653 0.538112087890035 /* println(formZeroToOneRandomMatrix) val formZeroToOneRandomMatrix= DenseMatrix.rand(3, 2, Rand.uniform) //0 到 1 的随机矩阵 //DenseVector(0.7978222133507369, 0.48978247271729325, 0.24943434133065834, 0.6619447026155139, 0.5324942068054981, 0.9051865626036415, 0.5989291014099107, 0.9221881029987078, 0.17371486701192662) println(formZeroToOneRandomVector2) val formZeroToOneRandomVector2= DenseVector.rand( 9, Rand.uniform) // DenseVector(0.7978222133507369, 0.48978247271729325, 0.24943434133065834, 0.6619447026155139, 0.5324942068054981, 0.9051865626036415, 0.5989291014099107, 0.9221881029987078, 0.17371486701192662) println(formZeroToOneRandomVector) val formZeroToOneRandomVector= DenseVector.rand( 9, Rand.uniform) //0 到 1的随机向量 */ 7.0 9.0 4.0 6.0 1.0 3.0 /* // println(createFunctionMatrix) val createFunctionMatrix= new DenseMatrix[Double](3, 2, Array(1.0, 4.0, 7.0, 3.0, 6.0, 9.0)) //从数组创建矩阵 */ 4 5 8 13 1 2 5 10 0 1 4 9 /* // println(createFuncMatrix) } case (i ,j ) => i*i + j*j val createFuncMatrix= DenseMatrix.tabulate(3, 4) { //从函数创建矩阵 println(funCreateVector2)//DenseVector(0, 1, 4, 9, 16, 25) val funCreateVector2=DenseVector.tabulate( 0 to 5)(i=> i*i) //矩阵行数