I have initialized two matrices (X and Y) in Scala as follows,
var x = ofDim[Int](a1,b1)
var y = ofDim[Int](a2,b2)
x,y,a1,a2,b1 and b2 are variables.
and now i need to decide X by Y (X/Y). How can achieve that?
There is other approach that use Apache Commons. However, it is important observe that the division operation applies multiplication and inversion operations and, some matrix are inversable and others no: https://en.wikipedia.org/wiki/Invertible_matrix
The following example applies the library Apache Commons (Study.scala):
import org.apache.commons.math3.linear._
object Study {
def main(args: Array[String]): Unit = {
val xArray = Array(Array(1.0, 2.0), Array(3.0, 4.0))
val yArray = Array(Array(1.0, 2.0), Array(3.0, 4.0))
val x = new Array2DRowRealMatrix(xArray)
val y = new Array2DRowRealMatrix(yArray)
val yInverse = new LUDecomposition(y).getSolver().getInverse();
val w = x.multiply(yInverse)
for(i <- 0 until w.getRowDimension())
for(j <- 0 until w.getColumnDimension())
println(w.getEntry(i, j))
}
}
Tip: If you intend to use the scala console, you need to specify the classpath ...
scala -classpath .../commons-math3/3.2/commons-math3-3.2.jar
... in the scala session you load the algorithm ...
:load .../Study.scala
... and the results come out calling the main function of Study (approximation can be applied) ...
scala> Study.main(null)
0.99 / 1.11E-16 / 0.0 / 1.02
Try:
import breeze.linalg.{DenseMatrix, inv}
val mx = new DenseMatrix(a1, b1, x.transpose.flatten)
val my = new DenseMatrix(a2, b2, y.transpose.flatten)
mx * inv(my)
The library Beeze, as mecioned in other responses, is necessary. You can install it using SBT or Maven
The Breeze project can be download from GitHub
This is the Maven approach:
<dependency>
<groupId>org.scalanlp</groupId>
<artifactId>breeze_2.10</artifactId> <!-- or 2.11 -->
<version>0.12</version>
</dependency>
The code ...
import breeze.linalg.DenseMatrix
object Division {
def main(args: Array[String]): Unit = {
var a1 = 10
var a2 = 11
var b1 = 12
var b2 = 13
//var x = Array.ofDim[Int](a1,b1)
//var y = Array.ofDim[Int](a2,b2)
var x = DenseMatrix(a1,b1)
var y = DenseMatrix(a2,b2)
var result = x/y
print(result)
}
}
Related
I'm having a very weird phenomenon occur. I'm hashing some mockup ssoids, and timing it since our application is timing critical. The first run of the function takes ~210 milliseconds, which is ridiculous and won't work. However, the 2nd, 3rd, and 4th runs take a few thousand nanoseconds each. I'm really confused, what's going on? Is there some caching I can do to make the first run just as fast?
See the code here:
object Main {
def main(args: Array[String]): Unit = {
val qq = "847bf1dc46ca22dc93259c5e857d6333"
val oo = "847bf1dc46ca22dc9eriuerie9duy45"
val xx = "909909ewufv9026854509erie9ifkf3"
val ww = "65984jg3oiqh4g3q383423932824344"
val qqq = getBytes(qq)
val ooo = getBytes(oo)
val xxx = getBytes(xx)
val www = getBytes(ww)
val t1 = System.nanoTime
val r1 = XxHash64.hashByteArray(qqq, seed = 0)
val duration = (System.nanoTime - t1)
val t2 = System.nanoTime
val r2 = XxHash64.hashByteArray(ooo, seed = 0)
val duration2 = (System.nanoTime - t2)
val t3 = System.nanoTime
val r3 = XxHash64.hashByteArray(xxx, seed = 0)
val duration3 = (System.nanoTime - t3)
val t4 = System.nanoTime
val r4 = XxHash64.hashByteArray(www, seed = 0)
val duration4 = (System.nanoTime - t4)
println(duration)
println(duration2)
println(duration3)
println(duration4)
println(r1)
println(r2)
println(r3)
println(r4)
}
(Also note I recognize this is a slipshod way of doing timings, but I've just started.)
I want to fit a circle to given 2D points in Scala.
Apache commons math has an example for this in java, which I am trying to translate to scala (without success, because my knowledge of Java is almost non existent).
I took the example code from "http://commons.apache.org/proper/commons-math/userguide/leastsquares.html", (see end of page) which I tried to translate into scala:
import org.apache.commons.math3.linear._
import org.apache.commons.math3.fitting._
import org.apache.commons.math3.fitting.leastsquares._
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresOptimizer._
import org.apache.commons.math3._
import org.apache.commons.math3.geometry.euclidean.twod.Vector2D
import org.apache.commons.math3.util.Pair
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresOptimizer.Optimum
def circleFitting: Unit = {
val radius: Double = 70.0
val observedPoints = Array(new Vector2D(30.0D, 68.0D), new Vector2D(50.0D, -6.0D), new Vector2D(110.0D, -20.0D), new Vector2D(35.0D, 15.0D), new Vector2D(45.0D, 97.0D))
// the model function components are the distances to current estimated center,
// they should be as close as possible to the specified radius
val distancesToCurrentCenter = new MultivariateJacobianFunction() {
//def value(point: RealVector): (RealVector, RealMatrix) = {
def value(point: RealVector): Pair[RealVector, RealMatrix] = {
val center = new Vector2D(point.getEntry(0), point.getEntry(1))
val value: RealVector = new ArrayRealVector(observedPoints.length)
val jacobian: RealMatrix = new Array2DRowRealMatrix(observedPoints.length, 2)
for (i <- 0 to observedPoints.length) {
var o = observedPoints(i)
var modelI: Double = Vector2D.distance(o, center)
value.setEntry(i, modelI)
// derivative with respect to p0 = x center
jacobian.setEntry(i, 0, (center.getX() - o.getX()) / modelI)
// derivative with respect to p1 = y center
jacobian.setEntry(i, 1, (center.getX() - o.getX()) / modelI)
}
new Pair(value, jacobian)
}
}
// the target is to have all points at the specified radius from the center
val prescribedDistances = Array.fill[Double](observedPoints.length)(radius)
// least squares problem to solve : modeled radius should be close to target radius
val problem:LeastSquaresProblem = new LeastSquaresBuilder().start(Array(100.0D, 50.0D)).model(distancesToCurrentCenter).target(prescribedDistances).maxEvaluations(1000).maxIterations(1000).build()
val optimum:Optimum = new LevenbergMarquardtOptimizer().optimize(problem) //LeastSquaresOptimizer.Optimum
val fittedCenter: Vector2D = new Vector2D(optimum.getPoint().getEntry(0), optimum.getPoint().getEntry(1))
println("circle fitting wurde aufgerufen!")
println("CIRCLEFITTING: fitted center: " + fittedCenter.getX() + " " + fittedCenter.getY())
println("CIRCLEFITTING: RMS: " + optimum.getRMS())
println("CIRCLEFITTING: evaluations: " + optimum.getEvaluations())
println("CIRCLEFITTING: iterations: " + optimum.getIterations())
}
This gives no compile errors, but crashes with:
Exception in thread "main" java.lang.NullPointerException
at org.apache.commons.math3.linear.EigenDecomposition.<init>(EigenDecomposition.java:119)
at org.apache.commons.math3.fitting.leastsquares.LeastSquaresFactory.squareRoot(LeastSquaresFactory.java:245)
at org.apache.commons.math3.fitting.leastsquares.LeastSquaresFactory.weightMatrix(LeastSquaresFactory.java:155)
at org.apache.commons.math3.fitting.leastsquares.LeastSquaresFactory.create(LeastSquaresFactory.java:95)
at org.apache.commons.math3.fitting.leastsquares.LeastSquaresBuilder.build(LeastSquaresBuilder.java:59)
at twoDhotScan.FittingFunctions$.circleFitting(FittingFunctions.scala:49)
at twoDhotScan.Main$.delayedEndpoint$twoDhotScan$Main$1(hotScan.scala:14)
at twoDhotScan.Main$delayedInit$body.apply(hotScan.scala:11)
at scala.Function0.apply$mcV$sp(Function0.scala:34)
at scala.Function0.apply$mcV$sp$(Function0.scala:34)
at scala.runtime.AbstractFunction0.apply$mcV$sp(AbstractFunction0.scala:12)
at scala.App.$anonfun$main$1$adapted(App.scala:76)
at scala.collection.immutable.List.foreach(List.scala:389)
at scala.App.main(App.scala:76)
at scala.App.main$(App.scala:74)
at twoDhotScan.Main$.main(hotScan.scala:11)
at twoDhotScan.Main.main(hotScan.scala)
I guess the problem is somewhere in the definition of the function distancesToCurrentCenter. I don't even know if this MultivariateJacobianFunction is supposed to be a real function or an object or what ever.
After some long fiddeling with the code, I got it running
The NullPointerException was gone after I updated apache-commons-math3 from version 3.3 to version 3.6.1 in my build.sbt file. Don't know if I forgot a paramater of if it was a bug. There were also 2 bugs in the example on the apache-commons-math website: They had two times a .getX operator where should have been an .getY.
So here is a running example for a circle fit with known radius:
import org.apache.commons.math3.analysis.{ MultivariateVectorFunction, MultivariateMatrixFunction }
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresOptimizer.Optimum
import org.apache.commons.math3.fitting.leastsquares.{ MultivariateJacobianFunction, LeastSquaresProblem, LeastSquaresBuilder, LevenbergMarquardtOptimizer }
import org.apache.commons.math3.geometry.euclidean.twod.Vector2D
import org.apache.commons.math3.linear.{ Array2DRowRealMatrix, RealMatrix, RealVector, ArrayRealVector }
object Main extends App {
val radius: Double = 20.0
val pointsList: List[(Double, Double)] = List(
(18.36921795, 10.71416674),
(0.21196357, -22.46528791),
(-4.153845171, -14.75588526),
(3.784114125, -25.55910336),
(31.32998899, 2.546924253),
(34.61542186, -12.90323269),
(19.30193011, -28.53185596),
(16.05620863, 10.97209111),
(31.67011956, -20.05020878),
(19.91175561, -28.38748712))
/*******************************************************************************
***** Random values on a circle with centerX=15, centerY=-9 and radius 20 *****
*******************************************************************************/
val observedPoints: Array[Vector2D] = (pointsList map { case (x, y) => new Vector2D(x, y) }).toArray
val vectorFunktion: MultivariateVectorFunction = new MultivariateVectorFunction {
def value(variables: Array[Double]): Array[Double] = {
val center = new Vector2D(variables(0), variables(1))
observedPoints map { p: Vector2D => Vector2D.distance(p, center) }
}
}
val matrixFunction = new MultivariateMatrixFunction {
def value(variables: Array[Double]): Array[Array[Double]] = {
val center = new Vector2D(variables(0), variables(1))
(observedPoints map { p: Vector2D => Array((center.getX - p.getX) / Vector2D.distance(p, center), (center.getY - p.getY) / Vector2D.distance(p, center)) })
}
}
// the target is to have all points at the specified radius from the center
val prescribedDistances = Array.fill[Double](observedPoints.length)(radius)
// least squares problem to solve : modeled radius should be close to target radius
val problem = new LeastSquaresBuilder().start(Array(100.0D, 50.0D)).model(vectorFunktion, matrixFunction).target(prescribedDistances).maxEvaluations(25).maxIterations(25).build
val optimum: Optimum = new LevenbergMarquardtOptimizer().optimize(problem)
val fittedCenter: Vector2D = new Vector2D(optimum.getPoint.getEntry(0), optimum.getPoint.getEntry(1))
println("Ergebnisse des LeastSquareBuilder:")
println("CIRCLEFITTING: fitted center: " + fittedCenter.getX + " " + fittedCenter.getY)
println("CIRCLEFITTING: RMS: " + optimum.getRMS)
println("CIRCLEFITTING: evaluations: " + optimum.getEvaluations)
println("CIRCLEFITTING: iterations: " + optimum.getIterations + "\n")
}
Tested on Scala version 2.12.6, compiled with sbt version 1.2.8
Does anabody know how to do this without a fixed radius?
After some reasearch on circle fitting I've found a wonderful algorith in the paper: "Error alalysis for circle fitting algorithms" by H. Al-Sharadqah and N. Chernov (available here: http://people.cas.uab.edu/~mosya/cl/ )
I implemented it in scala:
import org.apache.commons.math3.linear.{ Array2DRowRealMatrix, RealMatrix, RealVector, LUDecomposition, EigenDecomposition }
object circleFitFunction {
def circleFit(dataXY: List[(Double, Double)]) = {
def square(x: Double): Double = x * x
def multiply(pair: (Double, Double)): Double = pair._1 * pair._2
val n: Int = dataXY.length
val (xi, yi) = dataXY.unzip
//val S: Double = math.sqrt(((xi map square) ++ yi map square).sum / n)
val zi: List[Double] = dataXY map { case (x, y) => x * x + y * y }
val x: Double = xi.sum / n
val y: Double = yi.sum / n
val z: Double = ((xi map square) ++ (yi map square)).sum / n
val zz: Double = (zi map square).sum / n
val xx: Double = (xi map square).sum / n
val yy: Double = (yi map square).sum / n
val xy: Double = ((xi zip yi) map multiply).sum / n
val zx: Double = ((zi zip xi) map multiply).sum / n
val zy: Double = ((zi zip yi) map multiply).sum / n
val N: RealMatrix = new Array2DRowRealMatrix(Array(
Array(8 * z, 4 * x, 4 * y, 2),
Array(4 * x, 1, 0, 0),
Array(4 * y, 0, 1, 0),
Array(2.0D, 0, 0, 0)))
val M: RealMatrix = new Array2DRowRealMatrix(Array(
Array(zz, zx, zy, z),
Array(zx, xx, xy, x),
Array(zy, xy, yy, y),
Array(z, x, y, 1.0D)))
val Ninverse = new LUDecomposition(N).getSolver().getInverse()
val eigenValueProblem = new EigenDecomposition(Ninverse.multiply(M))
// Get all eigenvalues
// As we need only the smallest positive eigenvalue, all negative eigenvalues are replaced by Double.MaxValue
val eigenvalues: Array[Double] = eigenValueProblem.getRealEigenvalues() map (lambda => if (lambda < 0) Double.MaxValue else lambda)
// Now get the index of the smallest positive eigenvalue, to get the associated eigenvector
val i: Int = eigenvalues.zipWithIndex.min._2
val eigenvector: RealVector = eigenValueProblem.getEigenvector(3)
val A = eigenvector.getEntry(0)
val B = eigenvector.getEntry(1)
val C = eigenvector.getEntry(2)
val D = eigenvector.getEntry(3)
val centerX: Double = -B / (2 * A)
val centerY: Double = -C / (2 * A)
val Radius: Double = math.sqrt((B * B + C * C - 4 * A * D) / (4 * A * A))
val RMS: Double = (dataXY map { case (x, y) => (Radius - math.sqrt((x - centerX) * (x - centerX) + (y - centerY) * (y - centerY))) } map square).sum / n
(centerX, centerY, Radius, RMS)
}
}
I kept all the Names form the paper (see Chaper 4 and 8 and look for the Hyperfit-Algorithm) and I tried to limit the Matrix operations.
It's still not what I need, cause this sort of algorithm (algebraic fit) has known issues with fitting partially circles (arcs) and maybe big circles.
With my data, I had once the situation that it spit out completly wrong results, and I found out that I had an Eigenvalue of -0.1...
The Eigenvector of this Value produced the right result, but it was sorted out because of the negative Eigenvalue. So this one is not always stable (as so many other circle fitting algorithms)
But what a nice Algorithm!!!
Looks a bit like dark magic to me.
If someone needs not to much precision and a lot of speed (and has data from a full circle not to big) this would be my choice.
Next thing I will try is to implement a Levenberg Marquardt Algorithm form the same page I mentioned above.
cant find scala code to find the list of coordinates which comes with in 1 mile distance from a particular coordinates 41.7523,12.8629.
how can we geofencing for above given coordinate (spark scala)
Using this function we can get one nearby point randomly within one mile radius:
def getPoints(xc:Double,yc:Double,radiusInMiles:Int)={
val ran = new scala.util.Random()
val conv = 1609.344
//Here, there are about 111,300 meters in a degree
val radiusIndeg = radiusInMiles*conv / 111300f;
val u = ran.nextDouble()
val v = ran.nextDouble()
val w = radiusIndeg*Math.sqrt(u)
val t = 2*Math.PI*v
val x = w*Math.cos(t)
val y = w*Math.sin(t)
val newX = x/Math.cos(Math.toRadians(yc))
val fLong = newX+xc
val fLat = y+yc
(fLong,fLat)
}
By calling the above function repeatedly in a for loop, we can get desired number of random points within 1 mile:
for(i<-1 to 30) yield getPoints(41.7523,12.8629,1)
To get 30 nearby points randomly,
In Scala REPL:
scala> for(i<-1 to 30) yield getPoints(41.7523,12.8629,1)
res25: scala.collection.immutable.IndexedSeq[(Double, Double)] = Vector((41.74982541032955,12.86481315224305), (41.754168266
959056,12.870364411375881), (41.75544877222746,12.85451037482713), (41.7612335738966,12.856539452781801), (41.76358834447362
,12.861061408964183), (41.763040037484664,12.867369860689339), (41.75110057115767,12.873299266989251), (41.74658541773817,12
.865223104625423), (41.74925109768552,12.868277572490877), (41.76504777008776,12.86109583406441), (41.75732730141462,12.8722
5307703036), (41.75762735062798,12.860633801016085), (41.75003276741254,12.856383089176347), (41.760707286583,12.85341268512
5267), (41.748073299368386,12.858209316913472), (41.76018412949083,12.866118423321987), (41.74213603200559,12.87308644848186
), (41.761324688000265,12.86506896052553), (41.749976...
scala> res25.foreach(println)
(41.74982541032955,12.86481315224305)
(41.754168266959056,12.870364411375881)
(41.75544877222746,12.85451037482713)
(41.7612335738966,12.856539452781801)
(41.76358834447362,12.861061408964183)
(41.763040037484664,12.867369860689339)
(41.75110057115767,12.873299266989251)
(41.74658541773817,12.865223104625423)
(41.74925109768552,12.868277572490877)
(41.76504777008776,12.86109583406441)
(41.75732730141462,12.87225307703036)
(41.75762735062798,12.860633801016085)
(41.75003276741254,12.856383089176347)
(41.760707286583,12.853412685125267)
(41.748073299368386,12.858209316913472)
(41.76018412949083,12.866118423321987)
(41.74213603200559,12.87308644848186)
(41.761324688000265,12.86506896052553)
(41.74997668526327,12.86038167090363)
(41.75228048449065,12.872686927175733)
(41.75972428137232,12.859596070561539)
(41.7562836928502,12.86187286720154)
(41.75715996439461,12.861374766455278)
(41.760604332388,12.867977103427238)
(41.74018421174905,12.865172431590485)
(41.74059829855585,12.86438943748021)
(41.7593627526156,12.873744103200057)
(41.747241804657264,12.8542871167178)
(41.76014663643563,12.858456116302811)
(41.740826160697715,12.867433800624394)
scala>
I try to implement a version of the Monte Carlo algorithm in Scala but i have a little problem.
In my first loop, i have a mismatch with Unit and Int, but I didn't know how to slove this.
Thank for your help !
import scala.math._
import scala.util.Random
import scala.collection.mutable.ListBuffer
object Main extends App{
def MonteCarlo(list: ListBuffer[Int]): List[Int] = {
for (i <- list) {
var c = 0.00
val X = new Random
val Y = new Random
for (j <- 0 until i) {
val x = X.nextDouble // in [0,1]
val y = Y.nextDouble // in [0,1]
if (x * x + y * y < 1) {
c = c + 1
}
}
c = c * 4
var p = c / i
var error = abs(Pi-p)
print("Approximative value of pi : $p \tError: $error")
}
}
var liste = ListBuffer (200, 2000, 4000)
MonteCarlo(liste)
}
A guy working usually with Python.
for loop does not return anything, so that's why your method returns Unit but expects List[Int] as return type is List[Int].
Second, you have not used scala interpolation correctly. It won't print the value of error. You forgot to use 's' before the string.
Third thing, if want to return list, you first need a list where you will accumulate all values of every iteration.
So i am assuming that you are trying to return error for all iterations. So i have created an errorList, which will store all values of error. If you want to return something else you can modify your code accordingly.
def MonteCarlo(list: ListBuffer[Int]) = {
val errorList = new ListBuffer[Double]()
for (i <- list) {
var c = 0.00
val X = new Random
val Y = new Random
for (j <- 0 until i) {
val x = X.nextDouble // in [0,1]
val y = Y.nextDouble // in [0,1]
if (x * x + y * y < 1) {
c = c + 1
}
}
c = c * 4
var p = c / i
var error = abs(Pi-p)
errorList += error
println(s"Approximative value of pi : $p \tError: $error")
}
errorList
}
scala> MonteCarlo(liste)
Approximative value of pi : 3.26 Error: 0.11840734641020667
Approximative value of pi : 3.12 Error: 0.02159265358979301
Approximative value of pi : 3.142 Error: 4.073464102067881E-4
res9: scala.collection.mutable.ListBuffer[Double] = ListBuffer(0.11840734641020667, 0.02159265358979301, 4.073464102067881E-4)
Is it somehow possible to slice updates on Matrices in breeze? I could not find implicit value for parameter op.
Breeze 0.11.2.
val idxs = Seq(0,1)
val x = DenseMatrix.rand(3,3)
val y = DenseMatrix.rand(3,3)
x(idxs,idxs)+= y(idxs, idxs) // cant find implicit conversion for += here.
Analog code with DenseVectors works properly.
val xv = DenseVector.rand(3)
val yv = DenseVector.rand(3)
x(idxs) += y(idxs)
There is ugly work-around updating rows in iterative manner.
val idxs = IndexedSeq(0, 1)
val x:DenseMatrix[Double] = DenseMatrix.zeros(3, 3)
val y = DenseMatrix.rand(3, 3)
for(r<-idxs) {
val slx = x(::, r)
val sly = y(::, r)
slx(idxs) += sly(idxs)
}
It's an oversight. Please open an issue on github.