Changed artifactId name and updated javadoc

.gitignore

@@ -2,4 +2,7 @@
 *.jar
 .project
 .classpath
-export.jardesc
\ No newline at end of file
+export.jardesc
+target/
+.idea/
+*.iml
\ No newline at end of file

pom.xml

@@ -4,8 +4,13 @@
          xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
     <modelVersion>4.0.0</modelVersion>
 
-    <groupId>org.bioimageanalysis.icy</groupId>
-    <artifactId>icy-nherve-matrix</artifactId>
+    <parent>
+        <artifactId>pom-icy</artifactId>
+        <groupId>org.bioimageanalysis.icy</groupId>
+        <version>1.0.4</version>
+    </parent>
+
+    <artifactId>nherve-matrix</artifactId>
     <version>1.1</version>
 
     <packaging>jar</packaging>
@@ -46,32 +51,4 @@
             </plugin>
         </plugins>
     </build>
-
-    <dependencies>
-        <dependency>
-            <groupId>org.bioimageanalysis.icy</groupId>
-            <artifactId>icy-kernel</artifactId>
-            <version>2.1.0</version>
-        </dependency>
-    </dependencies>
-
-    <repositories>
-        <repository>
-            <id>icy</id>
-            <url>https://icy-nexus.pasteur.fr/repository/Icy/</url>
-        </repository>
-    </repositories>
-
-    <distributionManagement>
-        <snapshotRepository>
-            <id>icy-dev</id>
-            <name>icy-dev</name>
-            <url>https://icy-nexus-dev.pasteur.cloud/repository/icy-core/</url>
-        </snapshotRepository>
-        <repository>
-            <id>icy-prod</id>
-            <name>icy-prod</name>
-            <url>https://icy-nexus.pasteur.fr/repository/icy-core/</url>
-        </repository>
-    </distributionManagement>
 </project>
\ No newline at end of file

src/main/java/plugins/nherve/matrix/CholeskyDecomposition.java

@@ -39,8 +39,7 @@ public class CholeskyDecomposition implements java.io.Serializable {
  * ------------------------ */
 
    /** Cholesky algorithm for symmetric and positive definite matrix.
-   @param  A   Square, symmetric matrix.
-   @return     Structure to access L and isspd flag.
+   @param  Arg   Square, symmetric matrix.
    */
 
    public CholeskyDecomposition (Matrix Arg) {

src/main/java/plugins/nherve/matrix/EigenvalueDecomposition.java

@@ -858,8 +858,7 @@ public class EigenvalueDecomposition implements java.io.Serializable {
  * ------------------------ */
 
    /** Check for symmetry, then construct the eigenvalue decomposition
-   @param A    Square matrix
-   @return     Structure to access D and V.
+   @param Arg    Square matrix
    */
 
    public EigenvalueDecomposition (Matrix Arg) {

src/main/java/plugins/nherve/matrix/LUDecomposition.java

@@ -3,10 +3,10 @@ package plugins.nherve.matrix;
 
    /** LU Decomposition.
    <P>
-   For an m-by-n matrix A with m >= n, the LU decomposition is an m-by-n
+   For an m-by-n matrix A with m &gt;= n, the LU decomposition is an m-by-n
    unit lower triangular matrix L, an n-by-n upper triangular matrix U,
    and a permutation vector piv of length m so that A(piv,:) = L*U.
-   If m < n, then L is m-by-m and U is m-by-n.
+   If m &lt; n, then L is m-by-m and U is m-by-n.
    <P>
    The LU decompostion with pivoting always exists, even if the matrix is
    singular, so the constructor will never fail.  The primary use of the
@@ -45,7 +45,6 @@ public class LUDecomposition implements java.io.Serializable {
 
    /** LU Decomposition
    @param  A   Rectangular matrix
-   @return     Structure to access L, U and piv.
    */
 
    public LUDecomposition (Matrix A) {

src/main/java/plugins/nherve/matrix/Matrix.java

@@ -26,13 +26,12 @@ import plugins.nherve.matrix.util.Maths;
  * 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.
@@ -43,9 +42,7 @@ import plugins.nherve.matrix.util.Maths;
  * </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. } };
@@ -202,7 +199,7 @@ public class Matrix implements Cloneable, java.io.Serializable {
 	 */
 
 	/**
-	 * Construct a matrix from a copy of a 2-D array.
+	 * @return Construct a matrix from a copy of a 2-D array.
 	 * 
 	 * @param A
 	 *            Two-dimensional array of doubles.
@@ -227,7 +224,7 @@ public class Matrix implements Cloneable, java.io.Serializable {
 	}
 
 	/**
-	 * Make a deep copy of a matrix
+	 * @return Make a deep copy of a matrix
 	 */
 
 	public Matrix copy() {
@@ -335,7 +332,7 @@ public class Matrix implements Cloneable, java.io.Serializable {
 	 * @param j
 	 *            Column index.
 	 * @return A(i,j)
-	 * @exception ArrayIndexOutOfBoundsException
+	 * @throws ArrayIndexOutOfBoundsException Array out of bounds
 	 */
 
 	public double get(int i, int j) {
@@ -434,9 +431,9 @@ public class Matrix implements Cloneable, java.io.Serializable {
 	 * 
 	 * @param r
 	 *            Array of row indices.
-	 * @param i0
+	 * @param j0
 	 *            Initial column index
-	 * @param i1
+	 * @param j1
 	 *            Final column index
 	 * @return A(r(:),j0:j1)
 	 * @exception ArrayIndexOutOfBoundsException
@@ -467,7 +464,7 @@ public class Matrix implements Cloneable, java.io.Serializable {
 	 *            Column index.
 	 * @param s
 	 *            A(i,j).
-	 * @exception ArrayIndexOutOfBoundsException
+	 * @throws ArrayIndexOutOfBoundsException array out of bounds
 	 */
 
 	public void set(int i, int j, double s) {
@@ -1207,13 +1204,14 @@ public class Matrix implements Cloneable, java.io.Serializable {
 	}
 
 	/**
-	 * Read a matrix from a stream. The format is the same the print method, so
+	 * @return Read a matrix from a stream. The format is the same the print method, so
 	 * printed matrices can be read back in (provided they were printed using US
 	 * Locale). Elements are separated by whitespace, all the elements for each
 	 * row appear on a single line, the last row is followed by a blank line.
 	 * 
 	 * @param input
 	 *            the input stream.
+	 * @throws java.io.IOException IO Exception
 	 */
 
 	public static Matrix read(BufferedReader input) throws java.io.IOException {

src/main/java/plugins/nherve/matrix/QRDecomposition.java

@@ -4,7 +4,7 @@ import plugins.nherve.matrix.util.Maths;
 
 /** QR Decomposition.
 <P>
-   For an m-by-n matrix A with m >= n, the QR decomposition is an m-by-n
+   For an m-by-n matrix A with m &gt;= n, the QR decomposition is an m-by-n
    orthogonal matrix Q and an n-by-n upper triangular matrix R so that
    A = Q*R.
 <P>
@@ -45,7 +45,6 @@ public class QRDecomposition implements java.io.Serializable {
 
    /** QR Decomposition, computed by Householder reflections.
    @param A    Rectangular matrix
-   @return     Structure to access R and the Householder vectors and compute Q.
    */
 
    public QRDecomposition (Matrix A) {

src/main/java/plugins/nherve/matrix/SingularValueDecomposition.java

@@ -4,12 +4,12 @@ import plugins.nherve.matrix.util.Maths;
 
    /** Singular Value Decomposition.
    <P>
-   For an m-by-n matrix A with m >= n, the singular value decomposition is
+   For an m-by-n matrix A with m &gt;= n, the singular value decomposition is
    an m-by-n orthogonal matrix U, an n-by-n diagonal matrix S, and
    an n-by-n orthogonal matrix V so that A = U*S*V'.
    <P>
    The singular values, sigma[k] = S[k][k], are ordered so that
-   sigma[0] >= sigma[1] >= ... >= sigma[n-1].
+   sigma[0] &gt;= sigma[1] &gt;= ... &gt;= sigma[n-1].
    <P>
    The singular value decompostion always exists, so the constructor will
    never fail.  The matrix condition number and the effective numerical
@@ -46,8 +46,7 @@ public class SingularValueDecomposition implements java.io.Serializable {
  * ------------------------ */
 
    /** Construct the singular value decomposition
-   @param A    Rectangular matrix
-   @return     Structure to access U, S and V.
+   @param Arg    Rectangular matrix
    */
 
    public SingularValueDecomposition (Matrix Arg) {

src/main/java/plugins/nherve/matrix/util/Maths.java

-package plugins.nherve.matrix.util;
-
-public class Maths {
-
-   /** sqrt(a^2 + b^2) without under/overflow. **/
-
-   public static double hypot(double a, double b) {
-      double r;
-      if (Math.abs(a) > Math.abs(b)) {
-         r = b/a;
-         r = Math.abs(a)*Math.sqrt(1+r*r);
-      } else if (b != 0) {
-         r = a/b;
-         r = Math.abs(b)*Math.sqrt(1+r*r);
-      } else {
-         r = 0.0;
-      }
-      return r;
-   }
-}
+package plugins.nherve.matrix.util;


public class Maths {



   /**
    * @return sqrt(a^2 + b^2) without under/overflow.
    * @param a double
    * @param b double
    * **/



   public static double hypot(double a, double b) {

      double r;

      if (Math.abs(a) > Math.abs(b)) {

         r = b/a;

         r = Math.abs(a)*Math.sqrt(1+r*r);

      } else if (b != 0) {

         r = a/b;

         r = Math.abs(b)*Math.sqrt(1+r*r);

      } else {

         r = 0.0;

      }

      return r;

   }
}
\ No newline at end of file