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Cholesky decomposition generating NaNs in Java

I'm not sure whether this is a maths.se or a SO question, but I'm going with SO as I think it's related to my Java.
I'm following a text book on Gaussian Processes (R&W) and implementing some examples in Java. One common step for several examples is to generate a Cholesky decomposition of a covariance matrix. In my attempt I can get successful results for matrices up to a limited size (33x33). However, for anything larger a NaN appears in the diagonal (at 32,32) and so all subsequent values in the matrix are likewise NaNs.
The code is shown below, and the source of the NaN is indicated in the cholesky method. Essentially the covariance element a[32][32] is 1.0, but the value of sum is a little over this (1.0000001423291431), so the square root is imaginary. So my questions are:
Is this an expected result from linear algebra, or, e.g., an
artefact of my implementation?
How is this problem best avoided in practice?
Note that I'm not looking for recommendations of libraries to use. This is simply for my own understanding.
Apologies for the length, but I've tried to provide a complete MWE:
import static org.junit.Assert.assertFalse;
import org.junit.Test;
public class CholeskyTest {
#Test
public void testCovCholesky() {
final int n = 34; // Test passes for n<34
final double[] xData = getSpread(-5, 5, n);
double[][] cov = covarianceSE(xData);
double[][] lower = cholesky(cov);
for(int i=0; i<n; ++i) {
for(int j=0; j<n; ++j) {
assertFalse("NaN at " + i + "," + j, Double.isNaN(lower[i][j]));
}
}
}
/**
* Generate n evenly space values from min to max inclusive
*/
private static double[] getSpread(final double min, final double max, final int n) {
final double[] values = new double[n];
final double delta = (max - min)/(n - 1);
for(int i=0; i<n; ++i) {
values[i] = min + i*delta;
}
return values;
}
/**
* Calculate the covariance matrix for the given observations using
* the squared exponential (SE) covariance function.
*/
private static double[][] covarianceSE (double[] v) {
final int m = v.length;
double[][] k = new double[m][];
for(int i=0; i<m; ++i) {
double vi = v[i];
double row[] = new double[m];
for(int j=0; j<m; ++j) {
double dist = vi - v[j];
row[j] = Math.exp(-0.5*dist*dist);
}
k[i] = row;
}
return k;
}
/**
* Calculate lower triangular matrix L such that LL^T = A
* Using Cholesky decomposition from
* https://rosettacode.org/wiki/Cholesky_decomposition#Java
*/
private static double[][] cholesky(double[][] a) {
final int m = a.length;
double[][] l = new double[m][m];
for(int i = 0; i< m;i++){
for(int k = 0; k < (i+1); k++){
double sum = 0;
for(int j = 0; j < k; j++){
sum += l[i][j] * l[k][j];
}
l[i][k] = (i == k) ? Math.sqrt(a[i][i] - sum) : // Source of NaN at 32,32
(1.0 / l[k][k] * (a[i][k] - sum));
}
}
return l;
}
}

Hmm, I think I've found an answer to my own question, from the same textbook I was following. From R&W p.201:
In practice it may be necessary to add a small multiple of the
identity matrix $\epsilon I$ to the covariance matrix for numerical
reasons. This is because the eigenvalues of the matrix K can decay
very rapidly [...] and without this stabilization the Cholesky
decomposition fails. The effect on the generated samples is to add
additional independent noise of variance $epsilon$.
So the following change seems to be sufficient:
private static double[][] cholesky(double[][] a) {
final int m = a.length;
double epsilon = 0.000001; // Small extra noise value
double[][] l = new double[m][m];
for(int i = 0; i< m;i++){
for(int k = 0; k < (i+1); k++){
double sum = 0;
for(int j = 0; j < k; j++){
sum += l[i][j] * l[k][j];
}
l[i][k] = (i == k) ? Math.sqrt(a[i][i]+epsilon - sum) : // Add noise to diagonal values
(1.0 / l[k][k] * (a[i][k] - sum));
}
}
return l;
}

I just finished writing my own version of a Cholesky Decomposition routine in C++ and JavaScript. Instead of computing L, it computes U, but I would be curious to test it with the matrix that causes the NaN error. Would you be able to post the matrix here, or contact me (info in Profile.)

Getting the math right for a Hidden Markov Model in Java

In an effort to learn and use hidden markov models, I am writing my own code to implement them. I am using this wiki article to help with my work. I do not wish to resort to pre-written libraries, because I have found I can achieve a better understanding if I write it myself. And no, this isn't a school assignment! :)
Unfortunately, my highest level of education consists of high school computer science and statistics. I have no background in Machine Learning besides the casual poking around with ANN libraries and TensorFlow. I am therefore having a bit of trouble translating mathematical equations into code. Specifically, I'm worried my implementations of the alpha and beta functions aren't functionally correct. If anyone can assist in describing where I messed up and how to correct my mistakes to have a functioning HMM implementation, it'd be greatly appreciated.
Here are my class-wide globals:
public int n; //number of states
public int t; //number of observations
public int time; //iteration holder
public double[][] emitprob; //Emission parameter
public double[][] stprob; //State transition parameter
public ArrayList<String> states, observations, x, y;
My constructor:
public Model(ArrayList<String> sts, ArrayList<String> obs)
{
//the most important algorithm we need right now is
//unsupervised learning through BM. Supervised is
//pretty easy.
//need hashtable of count objects... Aya...
//perhaps a learner...?
states = sts;
observations = obs;
n = states.size();
t = observations.size();
x = new ArrayList();
y = new ArrayList();
time = 0;
stprob = new double[n][n];
emitprob = new double[n][t];
stprob = newDistro(n,n);
emitprob = newDistro(n,t);
}
The newDistro method is for creating a new, uniform, normal distribution:
public double[][] newDistro(int x, int y)
{
Random r = new Random(System.currentTimeMillis());
double[][] returnme = new double[x][y];
double sum = 0;
for(int i = 0; i < x; i++)
{
for(int j = 0; j < y; j++)
{
returnme[i][j] = Math.abs(r.nextInt());
sum += returnme[i][j];
}
}
for(int i = 0; i < x; i++)
{
for(int j = 0; j < y; j++)
{
returnme[i][j] /= sum;
}
}
return returnme;
}
My viterbi algorithm implementation:
public ArrayList<String> viterbi(ArrayList<String> obs)
{
//K means states
//T means observations
//T arrays should be constructed as K * T (N * T)
ArrayList<String> path = new ArrayList();
String firstObservation = obs.get(0);
int firstObsIndex = observations.indexOf(firstObservation);
double[] pi = new double[n]; //initial probs of first obs for each st
int ts = obs.size();
double[][] t1 = new double[n][ts];
double[][] t2 = new double[n][ts];
int[] y = new int[obs.size()];
for(int i = 0; i < obs.size(); i++)
{
y[i] = observations.indexOf(obs.get(i));
}
for(int i = 0; i < n; i++)
{
pi[i] = emitprob[i][firstObsIndex];
}
for(int i = 0; i < n; i++)
{
t1[i][0] = pi[i] * emitprob[i][y[0]];
t2[i][0] = 0;
}
for(int i = 1; i < ts; i++)
{
for(int j = 0; j < n; j++)
{
double maxValue = 0;
int maxIndex = 0;
//first we compute the max value
for(int q = 0; q < n; q++)
{
double value = t1[q][i-1] * stprob[q][j];
if(value > maxValue)
{
maxValue = value; //the max
maxIndex = q; //the argmax
}
}
t1[j][i] = emitprob[j][y[i]] * maxValue;
t2[j][i] = maxIndex;
}
}
int[] z = new int[ts];
int maxIndex = 0;
double maxValue = 0.0d;
for(int k = 0; k < n; k++)
{
double myValue = t1[k][ts-1];
if(myValue > maxValue)
{
myValue = maxValue;
maxIndex = k;
}
}
path.add(states.get(maxIndex));
for(int i = ts-1; i >= 2; i--)
{
z[i-1] = (int)t2[z[i]][i];
path.add(states.get(z[i-1]));
}
System.out.println(path.size());
for(String s: path)
{
System.out.println(s);
}
return path;
}
My forward algorithm, which takes place of the alpha function as described later:
public double forward(ArrayList<String> obs)
{
double result = 0;
int length = obs.size()-1;
for(int i = 0; i < n; i++)
{
result += alpha(i, length, obs);
}
return result;
}
The remaining functions are for implementing the Baum-Welch Algorithm.
The alpha function is what I'm afraid I'm doing wrong of the most on here. I had trouble understanding which "direction" it needs to iterate over the sequence - Do I start from the last element (size-1) or the first (at index zero) ?
public double alpha(int j, int t, ArrayList<String> obs)
{
double sum = 0;
if(t == 0)
{
return stprob[0][j];
}
else
{
String lastObs = obs.get(t);
int obsIndex = observations.indexOf(lastObs);
for(int i = 0; i < n; i++)
{
sum += alpha(i, t-1, obs) * stprob[i][j] * emitprob[j][obsIndex];
}
}
return sum;
}
I'm having similar "correctness" issues with my beta function:
public double beta(int i, int t, ArrayList<String> obs)
{
double result = 0;
int obsSize = obs.size()-1;
if(t == obsSize)
{
return 1;
}
else
{
String lastObs = obs.get(t+1);
int obsIndex = observations.indexOf(lastObs);
for(int j = 0; j < n; j++)
{
result += beta(j, t+1, obs) * stprob[i][j] * emitprob[j][obsIndex];
}
}
return result;
}
I'm more confident in my gamma function; However, since it explicitly requires use of alpha and beta, obviously I'm worried it'll be "off" somehow.
public double gamma(int i, int t, ArrayList<String> obs)
{
double top = alpha(i, t, obs) * beta(i, t, obs);
double bottom = 0;
for(int j = 0; j < n; j++)
{
bottom += alpha(j, t, obs) * beta(j, t, obs);
}
return top / bottom;
}
Same for my "squiggle" function - I do apologize for naming; Not sure of the actual name for the symbol.
public double squiggle(int i, int j, int t, ArrayList<String> obs)
{
String lastObs = obs.get(t+1);
int obsIndex = observations.indexOf(lastObs);
double top = alpha(i, t, obs) * stprob[i][j] * beta(j, t+1, obs) * emitprob[j][obsIndex];
double bottom = 0;
double innerSum = 0;
double outterSum = 0;
for(i = 0; i < n; i++)
{
for(j = 0; j < n; j++)
{
innerSum += alpha(i, t, obs) * stprob[i][j] * beta(j, t+1, obs) * emitprob[j][obsIndex];
}
outterSum += innerSum;
}
return top / bottom;
}
Lastly, to update my state transition and emission probability arrays, I have implemented these functions as aStar and bStar.
public double aStar(int i, int j, ArrayList<String> obs)
{
double squiggleSum = 0;
double gammaSum = 0;
int T = obs.size()-1;
for(int t = 0; t < T; t++)
{
squiggleSum += squiggle(i, j, t, obs);
gammaSum += gamma(i, t, obs);
}
return squiggleSum / gammaSum;
}
public double bStar(int i, String v, ArrayList<String> obs)
{
double top = 0;
double bottom = 0;
for(int t = 0; t < obs.size()-1; t++)
{
if(obs.get(t).equals(v))
{
top += gamma(i, t, obs);
}
bottom += gamma(i, t, obs);
}
return top / bottom;
}
In my understanding, since the b* function includes a piecewise function that returns either 1 or 0, I think implementing it in an "if" statement and only adding the result if the string is equal to the observation history is the same as what is described, since the function would render the call to gamma 0, thus saving a little computation time. Is this correct?
In summation, I want to get my math right, to ensure a successful (albeit simple) HMM implementation. As for the Baum-Welch algorithm, I am having trouble understanding how to implment the complete function - would it be as simple as running aStar over all states (as an n * n FOR loop) and bStar for all observations, inside a loop with a convergence function? Also, what would be a best-practice function for checking for convergence without overfitting?
Please let me know of everything I need to do in order to get this right.
Thank you heavily for any help you can give me!

To avoid underflow, one should use a scaling factor in the forward and backward algorithms. To get the correct result, one uses nested for loops and the steps are forward in the forward method.
The backward method is similar to the forward function.
You invoke them from the method of the Baum-Welch algorithm.

Java: Create a duplicate of an array without making it a reference

I have written a series of matrix operations where I take a 2 dimensional float array, treat it as a matrix, and perform matrix operations on it to acquire an inverse. My problem has been that although the array I am using with the class methods isn't part of the class, every time I run the method with the array as the parameter, the array itself also becomes modified.
First I will describe how I got the inverse of my matrix and then I will show the output.
The steps to taking the inverse of a matrix are as follows:
Get the cofactor matrix (i.e. create a matrix of matrix minors of the original matrix and then negate every other entry. If C = Cofactor Matrix, M = Matrix of Minors, i is the current row, and j is the current column, then C[ i ][ j ] = M[ i ][ j ]*( -1 )^( i + j )
Convert the cofactor matrix to the adjugate (also known as adjoint) matrix by transposing (replacing row, column entry by its analogous column, row entry and vice versa) the cofactor matrix. If C = Cofactor Matrix, A = Adjugate Matrix, i is the current row, and j is the current column, then A[ i ][ j ] = C[ j ][ i ]
Finally, take one over determinant of the original matrix and multiply the adjugate matrix by that value. If I = Inverse Matrix, A = Adjugate Matrix and D = Determinant, then I = (1/D)*A
In order to test if you have truly acquired the Matrix Inverse of a Matrix, one can multiply the original matrix by its inverse to get the identity matrix.
If I = Inverse, O = Original Matrix, and id = Identity Matrix then O*I = id
Now I will present the code where I implement these operations. For the sake of conciseness, I will not describe how to get the Matrix of Minors or the Determinant, but the problem I have been encountering will become apparent anyways.
public class MatrixOperations {
//Note: this method works fine. There are no problems.
public float determinant(float [][] a)
{
float [][] temp_mat;
float res = 0;
//assuming a square matrix
/*If it's a 2X2, then use the formula for a determinant of
2X2 matrices.*/
if(a.length == 2)
{
return a[0][0]*a[1][1]-a[0][1]*a[1][0];
}
/*Otherwise do the determinant formula recursively until your
determinant is made up of 2X2 matrix determinants and scalar products*/
else
{
temp_mat = new float[a.length-1][a.length-1];
int placej = 0;
int placei = 0;
for(int k = 0; k<a.length;k++)
{
for(int j = 0; j<a.length; j++)
{
for(int i = 1; i < a.length; i++)
{
placei = i-1;
if(j != k)
{
if(j < k)
{
temp_mat[placei][j] = a[i][j];
}
else if(j > k)
{
if (i == 1){
placej = j-1;
}
temp_mat[placei][placej] = a[i][j];
}
}
}
}
res+=a[0][k]*determinant(temp_mat)*(int)Math.pow(-1, k);
}
return res;
}
}
//Note: this method also works fine
//Scalar product method
public float[][] mul(float[][] m, float r)
{
float[][] res = new float[m.length][m.length];
for(int i = 0; i < m.length; i++)
{
for(int j = 0; j < m.length; j++)
{
res[i][j]= m[i][j]*r;
}
}
return res;
}
//Note: This method also works fine
public float[][] mul(float[][] m,float[][] n)
{
float[][] res = new float[m.length][m.length];
for(int i = 0; i < m.length; i++)
{
for(int j = 0; j < m.length; j++)
{
for(int k = 0; k < m.length; k++)
{
res[i][j] += m[i][k]*m[k][i];
}
}
}
return res;
}
//The method for creating a matrix of minors
//Here I start having problems
public float[][] minor(float [][] m)
{
float [][] minor_mat = new float [m.length][m.length];
//If the matrix is greater than a 2X2, use this to generate a matrix of minors
if(m.length > 2)
{
float [][] current_minor = new float [m.length-1][m.length-1];
int placei = 0;
int placej = 0;
for(int i = 0; i < m.length; i++)
{
for(int j = 0; j < m.length; j++)
{
for(int k = 0; k < m.length; k++)
{
for(int l = 0; l < m.length; l++)
{
if(i != k && j != l)
{
if(k<i)
placei = k;
else if(k>i)
placei = k-1;
if(l<j)
placej = l;
else if(l>j)
placej = l-1;
current_minor[placei][placej] = m[k][l];
}
}
}
minor_mat[i][j] = this.determinant(current_minor);
}
}
}
//otherwise use the definition for 2X2 matrix of minors
else
{
//even though minor_mat is using m.clone() rather than m, when I return the result, m has still been modified for some reason.
minor_mat = m.clone()
float temp;
temp = minor_mat[0][0];
minor_mat[0][0] = minor_mat[1][1];
minor_mat[1][1] = temp;
temp = minor_mat[0][1];
minor_mat[0][1] = minor_mat[1][0];
minor_mat[1][0] = temp;
}
return minor_mat;
}
//the same problem occurs here as it did in the minor method
//m appears to get modified even though I only use m.clone()
public float[][] cofactor(float [][] m)
{
float[][] res = m.clone();
res = this.minor(res)
for(int i = 0; i < m.length; i++)
{
for(int j = 0; j < m.length; j++)
{
res[i][j] = res[i][j]*(int)Math.pow(-1, i + j);
}
}
return res;
}
//The following transpose, adjugate, and inverse methods have the same problem
public float[][] transpose(float[][] m)
{
float[][] res = new float[m.length][m.length];
float temp = 0;
for(int i = 0; i < m.length; i++)
{
for(int j = 0; j < m.length; j++)
{
temp = m[i][j];
res[i][j] = m[j][i];
res[j][i] = temp;
}
}
return res;
}
public float[][] adjugate(float[][] m)
{
float[][] res = this.transpose(this.cofactor(m));
return res;
}
public float[][] inverse(float[][] m)
{
float[][] res = this.mul(this.adjugate(m), (1/this.determinant(m)));
return res;
}
//print out the matrix in square form
public void matrixprint(float [][] m)
{
for(int i = 0; i < m.length; i++)
{
System.out.println("");
for(int j = 0; j < m[i].length; j++){
System.out.print(m[i][j] + " ");
}
}
System.out.println("\n");
}
}
Now the main class and the main method that creates an instance of the MatrixOperations class and uses its methods on a 2X2 matrix.
public class Main {
public static void main(String[] args) {
MatrixOperations mo = new MatrixOperations();
//Create a 2X2 matrix called "matrix" and set its elements
//Then perform each step on "matrix" and finally test if you have acquired the correct inverse
float [][] matrix = new float[2][2];
matrix[0][0] = 2;
matrix [0][1] = 5;
matrix [1][0] = 4;
matrix [1][1] = 3;
System.out.println("Matrix = ");
mo.matrixprint(matrix);
System.out.println("Minor = ");
mo.matrixprint(mo.minor(matrix));
System.out.println("Matrix = ");
mo.matrixprint(matrix);
System.out.println("Cofactor = ");
mo.matrixprint(mo.cofactor(matrix));
System.out.println("Matrix = ");
mo.matrixprint(matrix);
System.out.println("Adjugate = ");
mo.matrixprint(mo.adjugate(matrix));
System.out.println("Matrix = ");
mo.matrixprint(matrix);
System.out.println("Determinant = ");
System.out.println(mo.determinant(matrix));
System.out.println("Matrix = ");
mo.matrixprint(matrix);
System.out.println("Inverse = ");
mo.matrixprint(mo.inverse(matrix));
System.out.println("Matrix = ");
mo.matrixprint(matrix);
System.out.println("Identity = ");
mo.matrixprint(mo.mul(mo.inverse(matrix), matrix));
}
}
Now you will see that when I show the output, every time I use a method on "matrix", and reprint "matrix", "matrix" itself has been modified even though my methods only use a copy of "matrix" and not "matrix" itself.
Output:
Matrix =
2.0 5.0
4.0 3.0
Minor =
3.0 4.0
5.0 2.0
Matrix =
3.0 4.0
5.0 2.0
Cofactor =
3.0 -4.0
-5.0 2.0
Matrix =
3.0 -4.0
-5.0 2.0
Adjugate =
3.0 5.0
4.0 2.0
Matrix =
3.0 4.0
5.0 2.0
Determinant =
-14.0
Matrix =
3.0 4.0
5.0 2.0
Inverse =
-0.21428573 0.35714287
0.2857143 -0.14285715
Matrix =
3.0 -4.0
-5.0 2.0
Identity =
0.1479592 0.1479592
0.12244898 0.12244898
Any help/explanation as to why this happens would be appreciated.

This line does a shallow clone;
float[][] res = m.clone();
This copies the res which is array fo references to arrays. but not any of the arrays res points to. Most likely what you wanted is
float[][] res = new float[m.length][];
for (int i = 0; i < m.length; i++)
res[i] = m[i].clone();

It's because you are passing reference of matrix object in the methods of MatrixOperations class. It's not a copy of matrix object.
From Java doc:
Reference data type parameters, such as objects, are also passed into
methods by value. This means that when the method returns, the
passed-in reference still references the same object as before.

A two-dimensional array is just an array of arrays.
clone() on an array just does a shallow clone.
So you have a new cloned outer array, but it references the same entries (the inner arrays).
After cloning the outer array, iterate over the outer array and clone all inner arrays to get a deep clone.

How call and repeat a 2d array?

Basically my HW says to ask user for matrix of A. Then ask user by how much he would like to power Matrix A.
So basically,
I need to find a way to raise a matrix to the power. I can multiply them, but it's harder to raise them to the power because I must multiply it by itself. So What I do is create a variable to hold the matrix like so
for (i = 0; i < matrixARowSize; i++)
{
for (j = 0; j < matrixAColumnSize; j++)
{
for (k = 0; k < matrixARowSize; k++)
{
sum += matrixA[i][j] * matrixA[i][j];
}
matrixC[i][j] = sum;
sum = 0;
}
}
Then I would have to multiply to itself as much as the user wants to.
Eg:
matrixC[i][j] * matrixC[i][j]*matrixC[i][j] ...// etc
up to whatever power the user wants. I can do that with many If statements yes, but I also need to be able to add them together like so:
matrixC^6 + matrixC^5 + matrixC^4 ...
etc from whatever power the user wants. (Highest is 6).
Any suggestions on how to do this?

You can do this:
int raiseMethod(int val, int pow) {
int temp = val;
for (int i = 1; i < pow; i++) {
temp *= val;
}
return temp;
}
for (int i = 0; i < arrayColummns; i++) {
for (int j = 0; j < arrayRows; j++) {
array[i][j] = raiseMethod(array[i][j], powerToRaise);
}
}
This way, the array will be auto-updated with it's raised value on each position.

I believe you are looking for the Math.pow() method, which raises one number to the power of another, e.g.
sum += (int) Math.power(matrixA[i][j], raiseByPower);

You can do the binary multiplication of the matrix.

This Matrix structure contains everything you need.
#include <stdio.h>
#include <string.h>
const int SIZE = 6;
struct Matrix
{
int m[SIZE][SIZE];
Matrix()
{
memset(m,0,sizeof(m));
}
Matrix( int a[SIZE][SIZE] )
{
for(int i = 0;i<SIZE;++i)for(int j = 0;j<SIZE;++j)
{
m[i][j] = a[i][j];
}
}
Matrix operator * ( const Matrix &a )
{
Matrix ret;
for(int k = 0;k<SIZE;++k) for(int i = 0;i<SIZE;++i) for(int j = 0;j<SIZE;++j)
{
ret.m[i][j] += m[i][k] * a.m[k][j];
}
return ret;
}
Matrix operator ^ ( int P )
{
Matrix ret , a(this->m);
for(int i = 0;i<SIZE;++i)
ret.m[i][i] = 1;
while(P)
{
if( P&1 )
ret = ret * a;
a = a * a;
P >>= 1;
}
return ret;
}
Matrix operator + (const Matrix &a)
{
Matrix ret;
for(int i = 0;i<SIZE;++i) for(int j = 0;j<SIZE;++j)
{
ret.m[i][j] = m[i][j] + a.m[i][j];
}
return ret;
}
};
You can use this structure like the following:
Matrix A, B;
Matrix res = (A^6) + (B^5);
This Power function does log(n) multiplications of matrix.

Matrix multiplication - single-dimension * multi-dimensional

I need to multiply two matrices. I understand pretty well how matrices work however in Java I am finding this a bit complex, so I researched a bit and found this.
public static int[][] multiply(int a[][], int b[][]) {
int aRows = a.length,
aColumns = a[0].length,
bRows = b.length,
bColumns = b[0].length;
int[][] resultant = new int[aRows][bColumns];
for(int i = 0; i < aRows; i++) { // aRow
for(int j = 0; j < bColumns; j++) { // bColumn
for(int k = 0; k < aColumns; k++) { // aColumn
resultant[i][j] += a[i][k] * b[k][j];
}
}
}
return resultant;
This code works fine. However the problem with this is that I need to multiply a single dimension matrix (1*5) by a multidimensional matrix (5*4), so the result will be (1*4) matrix and later on in the same program multiply a (1*4) matrix by a (4*3) matrix resulting in (1*3).
And I need to store the single dimension matrix in a normal array (double []) not multidimensional one!
I altered this code to the following but it still doesn't resolve the correct results.
public static double[] multiplyMatrices(double[] A, double[][] B) {
int xA = A.length;
int yB = B[0].length;
double[] C = new double[yB];
for (int i = 0; i < yB; i++) { // bColumn
for (int j = 0; j < xA; j++) { // aColumn
C[i] += A[j] * B[j][i];
}
}
return C;
Thanks in advance for any tips you may give :)

You can use RealMatrix to make it easier.
RealMatrix result = MatrixUtils.createRealMatrix(a).multiply(MatrixUtils.createRealMatrix(b));
double[] array = result.getRow(0);