Updated: vector 1.01, cliviz 0.102, app 0.101

Added:
  sonatype resolver
  DemoVectorInterop
  Vec[N] * Matrix[M, N] extension method.

Removed: JaMa comment.

build.sbt

@@ -1,8 +1,9 @@
-val appVersion:String = "0.1"
+val appVersion:String = "0.101"
 val globalScalaVersion = "3.2.2"
 
 ThisBuild / organization := "ai.dragonfly"
 ThisBuild / organizationName := "dragonfly.ai"
+ThisBuild / resolvers := Resolver.sonatypeOssRepos("releases")
 ThisBuild / startYear := Some(2023)
 ThisBuild / licenses := Seq(License.Apache2)
 ThisBuild / developers := List( tlGitHubDev("dragonfly-ai", "dragonfly.ai") )
@@ -22,7 +23,7 @@ lazy val matrix = crossProject(JSPlatform, JVMPlatform, NativePlatform)
   .crossType(CrossType.Full)
   .settings(
     description := "High performance, low footprint, cross platform, matrix math and machine learning library!",
-    libraryDependencies += "ai.dragonfly" %%% "vector" % "0.1",
+    libraryDependencies += "ai.dragonfly" %%% "vector" % "0.101",
   )
   .jvmSettings(
     libraryDependencies ++= Seq("org.scala-js" %% "scalajs-stubs" % "1.1.0")
@@ -46,7 +47,7 @@ lazy val demo = crossProject(JSPlatform, JVMPlatform, NativePlatform)
     name := "demo",
     Compile / mainClass := Some("Demo"),
     libraryDependencies ++= Seq(
-      "ai.dragonfly" %%% "cliviz" % "0.101",
+      "ai.dragonfly" %%% "cliviz" % "0.102",
       "ai.dragonfly" %%% "democrossy" % "0.102"
     )
   ).jsSettings(

demo/shared/src/main/scala/Demo.scala

@@ -7,7 +7,8 @@ object Demo extends XApp(NativeConsole(style = "padding: 8px; overflow: scroll;"
   val allDemos: Array[Demonstration] = Array[Demonstration](
     DemoPCA,
     DemoLinearRegression,
-    DemoEigenDecomposition
+    DemoEigenDecomposition,
+    DemoVectorInterop
   )
 
   for (d <- allDemos) d.demonstrate

demo/shared/src/main/scala/DemoEigenDecomposition.scala

@@ -3,7 +3,6 @@ import ai.dragonfly.math.matrix.Matrix
 import ai.dragonfly.math.matrix.decomposition.*
 import ai.dragonfly.math.vector.*
 import ai.dragonfly.math.vector.Vec.*
-import ai.dragonfly.math.vector.Vec4.*
 import narr.*
 
 object DemoEigenDecomposition extends Demonstration {

demo/shared/src/main/scala/DemoVectorInterop.scala

@@ -0,0 +1,27 @@
+import ai.dragonfly.democrossy.Demonstration
+import ai.dragonfly.math.matrix.Matrix
+import ai.dragonfly.math.matrix.util.*
+import ai.dragonfly.math.matrix.decomposition.*
+import ai.dragonfly.math.vector.*
+import ai.dragonfly.math.vector.Vec.*
+import ai.dragonfly.math.vector.Vec2.*
+import narr.*
+
+import ai.dragonfly.math.Constant.*
+
+object DemoVectorInterop extends Demonstration {
+  def demo(): Unit = {
+    val M0: Matrix[2, 3] = Matrix[2, 3](
+      NArray[NArray[Double]](
+        NArray[Double](1.0, `𝜑`, `√2`),
+        NArray[Double](2.0, π, e),
+      )
+    )
+
+    val v2: Vec[2] = Vec[2](1.0, 2.0)
+
+    println(s"${v2.show}.times($M0\n) = ${v2.times[3](M0)}")
+  }
+
+  def name: String = "DemoVectorInterop"
+}

matrix/shared/src/main/scala/ai/dragonfly/math/matrix/Matrix.scala

@@ -26,54 +26,9 @@ import scala.compiletime.ops.int.*
 import scala.math.hypot
 
 /**
-  * Jama = Java Matrix class.
-  * <P>
-  * The Java Matrix Class provides the fundamental operations of numerical
-  * linear algebra.  Various constructors create Matrices from two dimensional
-  * arrays of double precision floating point numbers.  Various "gets" and
-  * "sets" provide access to submatrices and matrix elements.  Several methods
-  * implement basic matrix arithmetic, including matrix addition and
-  * multiplication, matrix norms, and element-by-element array operations.
-  * Methods for reading and printing matrices are also included.  All the
-  * operations in this version of the Matrix Class involve real matrices.
-  * Complex matrices may be handled in a future version.
-  * <P>
-  * Five fundamental matrix decompositions, which consist of pairs or triples
-  * of matrices, permutation vectors, and the like, produce results in five
-  * decomposition classes.  These decompositions are accessed by the Matrix
-  * class to compute solutions of simultaneous linear equations, determinants,
-  * inverses and other matrix functions.  The five decompositions are:
-  * <P><UL>
-  * <LI>Cholesky Decomposition of symmetric, positive definite matrices.
-  * <LI>LU Decomposition of rectangular matrices.
-  * <LI>QR Decomposition of rectangular matrices.
-  * <LI>Singular Value Decomposition of rectangular matrices.
-  * <LI>Eigenvalue Decomposition of both symmetric and nonsymmetric square matrices.
-  * </UL>
-  * <DL>
-  * <DT><B>Example of use:</B></DT>
-  * <P>
-  * <DD>Solve a linear system A x = b and compute the residual norm, ||b - A x||.
-  * <P><PRE>
-  * double[][] vals = {{1.,2.,3},{4.,5.,6.},{7.,8.,10.}};
-  * Matrix A = new Matrix(vals);
-  * Matrix b = Matrix.random(3,1);
-  * Matrix x = A.solve(b);
-  * Matrix r = A.times(x).minus(b);
-  * double rnorm = r.normInfinity;
-  * </PRE></DD>
-  * </DL>
-  * *
-  *
-  * @author The MathWorks, Inc. and the National Institute of Standards and Technology.
-  * @version 5 August 1998
+  * This library is fundamentally an adaptation of the Java Matrix library, JaMa, by MathWorks Inc. and the National Institute of Standards and Technology.
   */
 
-
-/*
-  Because scala.js does not support static methods or fields, this object is not exported.
-  However, the MatrixExports export of Matrix contains the native JS static methods for the Matrix class.
- */
 object Matrix {
   /** Construct a matrix from a copy of a 2-D array.
    *

matrix/shared/src/main/scala/ai/dragonfly/math/matrix/decomposition/SV.scala

@@ -60,7 +60,7 @@ object SV {
 
     val minDim:Int = Math.min(rows, columns);
 
-    val s: Vec[N] = Vec.fill[N](0.0) // zeros
+    val s: Vec[N] = Vec.zeros[N]
     val U: Matrix[M, N] = Matrix.zeros[M, N]
     val V: Matrix[N, N] = Matrix.zeros[N, N]
     val e: Vec[M] = Vec.fill[M](0.0)

matrix/shared/src/main/scala/ai/dragonfly/math/matrix/util/util.scala

@@ -27,6 +27,9 @@ package object util {
   extension[N <: Int] (thisVector: Vec[N])(using ValueOf[N]) {
     inline def asRowMatrix: Matrix[1, N] = Matrix[1, N](thisVector.asInstanceOf[NArray[Double]])
     inline def asColumnMatrix: Matrix[N, 1] = Matrix[N, 1](thisVector.asInstanceOf[NArray[Double]])
+
+    def times [M <: Int] (thatMatrix: Matrix[N, M])(using ValueOf[M]): Matrix[1, M] = asRowMatrix * thatMatrix
+    inline def * [M <: Int] (thatMatrix: Matrix[N, M])(using ValueOf[M]): Matrix[1, M] = times(thatMatrix)
   }
 
   /**