# Error during runtime NoClassDefFound in java

Raghav Purankar
Error during runtime NoClassDefFound in java
0 Answer(s)      6 years and 5 months ago
Posted in : Java Beginners

code:

```package Jama;
import Jama.util.*;
import Jama.Matrix;
import Jama.EigenvalueDecomposition;

/** Eigenvalues and eigenvectors of a real matrix.
<P>
If A is symmetric, then A = V*D*V' where the eigenvalue matrix D is
diagonal and the eigenvector matrix V is orthogonal.
I.e. A = V.times(D.times(V.transpose())) and
V.times(V.transpose()) equals the identity matrix.
<P>
If A is not symmetric, then the eigenvalue matrix D is block diagonal
with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues,
lambda + i*mu, in 2-by-2 blocks, [lambda, mu; -mu, lambda].  The
columns of V represent the eigenvectors in the sense that A*V = V*D,
i.e. A.times(V) equals V.times(D).  The matrix V may be badly
conditioned, or even singular, so the validity of the equation
A = V*D*inverse(V) depends upon V.cond().
**/

public class EigenvalueDecomposition implements java.io.Serializable {

/* ------------------------
Class variables
* ------------------------ */

/** Row and column dimension (square matrix).
@serial matrix dimension.
*/
private int n;

/** Symmetry flag.
@serial internal symmetry flag.
*/
private boolean issymmetric;

/** Arrays for internal storage of eigenvalues.
@serial internal storage of eigenvalues.
*/
private double[] d, e;

/** Array for internal storage of eigenvectors.
@serial internal storage of eigenvectors.
*/
private double[][] V;

/** Array for internal storage of nonsymmetric Hessenberg form.
@serial internal storage of nonsymmetric Hessenberg form.
*/
private double[][] H;

/** Working storage for nonsymmetric algorithm.
@serial working storage for nonsymmetric algorithm.
*/
private double[] ort;

/* ------------------------
Private Methods
* ------------------------ */

// Symmetric Householder reduction to tridiagonal form.

private void tred2 () {

//  This is derived from the Algol procedures tred2 by
//  Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
//  Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
//  Fortran subroutine in EISPACK.

for (int j = 0; j < n; j++) {
d[j] = V[n-1][j];
}

// Householder reduction to tridiagonal form.

for (int i = n-1; i > 0; i--) {

// Scale to avoid under/overflow.

double scale = 0.0;
double h = 0.0;
for (int k = 0; k < i; k++) {
scale = scale + Math.abs(d[k]);
}
if (scale == 0.0) {
e[i] = d[i-1];
for (int j = 0; j < i; j++) {
d[j] = V[i-1][j];
V[i][j] = 0.0;
V[j][i] = 0.0;
}
} else {

// Generate Householder vector.

for (int k = 0; k < i; k++) {
d[k] /= scale;
h += d[k] * d[k];
}
double f = d[i-1];
double g = Math.sqrt(h);
if (f > 0) {
g = -g;
}
e[i] = scale * g;
h = h - f * g;
d[i-1] = f - g;
for (int j = 0; j < i; j++) {
e[j] = 0.0;
}

// Apply similarity transformation to remaining columns.

for (int j = 0; j < i; j++) {
f = d[j];
V[j][i] = f;
g = e[j] + V[j][j] * f;
for (int k = j+1; k <= i-1; k++) {
g += V[k][j] * d[k];
e[k] += V[k][j] * f;
}
e[j] = g;
}
f = 0.0;
for (int j = 0; j < i; j++) {
e[j] /= h;
f += e[j] * d[j];
}
double hh = f / (h + h);
for (int j = 0; j < i; j++) {
e[j] -= hh * d[j];
}
for (int j = 0; j < i; j++) {
f = d[j];
g = e[j];
for (int k = j; k <= i-1; k++) {
V[k][j] -= (f * e[k] + g * d[k]);
}
d[j] = V[i-1][j];
V[i][j] = 0.0;
}
}
d[i] = h;
}

// Accumulate transformations.

for (int i = 0; i < n-1; i++) {
V[n-1][i] = V[i][i];
V[i][i] = 1.0;
double h = d[i+1];
if (h != 0.0) {
for (int k = 0; k <= i; k++) {
d[k] = V[k][i+1] / h;
}
for (int j = 0; j <= i; j++) {
double g = 0.0;
for (int k = 0; k <= i; k++) {
g += V[k][i+1] * V[k][j];
}
for (int k = 0; k <= i; k++) {
V[k][j] -= g * d[k];
}
}
}
for (int k = 0; k <= i; k++) {
V[k][i+1] = 0.0;
}
}
for (int j = 0; j < n; j++) {
d[j] = V[n-1][j];
V[n-1][j] = 0.0;
}
V[n-1][n-1] = 1.0;
e[0] = 0.0;
}

// Symmetric tridiagonal QL algorithm.

private void tql2 () {

//  This is derived from the Algol procedures tql2, by
//  Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
//  Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
//  Fortran subroutine in EISPACK.

for (int i = 1; i < n; i++) {
e[i-1] = e[i];
}
e[n-1] = 0.0;

double f = 0.0;
double tst1 = 0.0;
double eps = Math.pow(2.0,-52.0);
for (int l = 0; l < n; l++) {

// Find small subdiagonal element

tst1 = Math.max(tst1,Math.abs(d[l]) + Math.abs(e[l]));
int m = l;
while (m < n) {
if (Math.abs(e[m]) <= eps*tst1) {
break;
}
m++;
}

// If m == l, d[l] is an eigenvalue,
// otherwise, iterate.

if (m > l) {
int iter = 0;
do {
iter = iter + 1;  // (Could check iteration count here.)

// Compute implicit shift

double g = d[l];
double p = (d[l+1] - g) / (2.0 * e[l]);
double r = Maths.hypot(p,1.0);
if (p < 0) {
r = -r;
}
d[l] = e[l] / (p + r);
d[l+1] = e[l] * (p + r);
double dl1 = d[l+1];
double h = g - d[l];
for (int i = l+2; i < n; i++) {
d[i] -= h;
}
f = f + h;

// Implicit QL transformation.

p = d[m];
double c = 1.0;
double c2 = c;
double c3 = c;
double el1 = e[l+1];
double s = 0.0;
double s2 = 0.0;
for (int i = m-1; i >= l; i--) {
c3 = c2;
c2 = c;
s2 = s;
g = c * e[i];
h = c * p;
r = Maths.hypot(p,e[i]);
e[i+1] = s * r;
s = e[i] / r;
c = p / r;
p = c * d[i] - s * g;
d[i+1] = h + s * (c * g + s * d[i]);

// Accumulate transformation.

for (int k = 0; k < n; k++) {
h = V[k][i+1];
V[k][i+1] = s * V[k][i] + c * h;
V[k][i] = c * V[k][i] - s * h;
}
}
p = -s * s2 * c3 * el1 * e[l] / dl1;
e[l] = s * p;
d[l] = c * p;

// Check for convergence.

} while (Math.abs(e[l]) > eps*tst1);
}
d[l] = d[l] + f;
e[l] = 0.0;
}

// Sort eigenvalues and corresponding vectors.

for (int i = 0; i < n-1; i++) {
int k = i;
double p = d[i];
for (int j = i+1; j < n; j++) {
if (d[j] < p) {
k = j;
p = d[j];
}
}
if (k != i) {
d[k] = d[i];
d[i] = p;
for (int j = 0; j < n; j++) {
p = V[j][i];
V[j][i] = V[j][k];
V[j][k] = p;
}
}
}
}

// Nonsymmetric reduction to Hessenberg form.

private void orthes () {

//  This is derived from the Algol procedures orthes and ortran,
//  by Martin and Wilkinson, Handbook for Auto. Comp.,
//  Vol.ii-Linear Algebra, and the corresponding
//  Fortran subroutines in EISPACK.

int low = 0;
int high = n-1;

for (int m = low+1; m <= high-1; m++) {

// Scale column.

double scale = 0.0;
for (int i = m; i <= high; i++) {
scale = scale + Math.abs(H[i][m-1]);
}
if (scale != 0.0) {

// Compute Householder transformation.

double h = 0.0;
for (int i = high; i >= m; i--) {
ort[i] = H[i][m-1]/scale;
h += ort[i] * ort[i];
}
double g = Math.sqrt(h);
if (ort[m] > 0) {
g = -g;
}
h = h - ort[m] * g;
ort[m] = ort[m] - g;

// Apply Householder similarity transformation
// H = (I-u*u'/h)*H*(I-u*u')/h)

for (int j = m; j < n; j++) {
double f = 0.0;
for (int i = high; i >= m; i--) {
f += ort[i]*H[i][j];
}
f = f/h;
for (int i = m; i <= high; i++) {
H[i][j] -= f*ort[i];
}
}

for (int i = 0; i <= high; i++) {
double f = 0.0;
for (int j = high; j >= m; j--) {
f += ort[j]*H[i][j];
}
f = f/h;
for (int j = m; j <= high; j++) {
H[i][j] -= f*ort[j];
}
}
ort[m] = scale*ort[m];
H[m][m-1] = scale*g;
}
}

// Accumulate transformations (Algol's ortran).

for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
V[i][j] = (i == j ? 1.0 : 0.0);
}
}

for (int m = high-1; m >= low+1; m--) {
if (H[m][m-1] != 0.0) {
for (int i = m+1; i <= high; i++) {
ort[i] = H[i][m-1];
}
for (int j = m; j <= high; j++) {
double g = 0.0;
for (int i = m; i <= high; i++) {
g += ort[i] * V[i][j];
}
// Double division avoids possible underflow
g = (g / ort[m]) / H[m][m-1];
for (int i = m; i <= high; i++) {
V[i][j] += g * ort[i];
}
}
}
}
}

// Complex scalar division.

private transient double cdivr, cdivi;
private void cdiv(double xr, double xi, double yr, double yi) {
double r,d;
if (Math.abs(yr) > Math.abs(yi)) {
r = yi/yr;
d = yr + r*yi;
cdivr = (xr + r*xi)/d;
cdivi = (xi - r*xr)/d;
} else {
r = yr/yi;
d = yi + r*yr;
cdivr = (r*xr + xi)/d;
cdivi = (r*xi - xr)/d;
}
}

// Nonsymmetric reduction from Hessenberg to real Schur form.

private void hqr2 () {

//  This is derived from the Algol procedure hqr2,
//  by Martin and Wilkinson, Handbook for Auto. Comp.,
//  Vol.ii-Linear Algebra, and the corresponding
//  Fortran subroutine in EISPACK.

// Initialize

int nn = this.n;
int n = nn-1;
int low = 0;
int high = nn-1;
double eps = Math.pow(2.0,-52.0);
double exshift = 0.0;
double p=0,q=0,r=0,s=0,z=0,t,w,x,y;

// Store roots isolated by balanc and compute matrix norm

double norm = 0.0;
for (int i = 0; i < nn; i++) {
if (i < low | i > high) {
d[i] = H[i][i];
e[i] = 0.0;
}
for (int j = Math.max(i-1,0); j < nn; j++) {
norm = norm + Math.abs(H[i][j]);
}
}

// Outer loop over eigenvalue index

int iter = 0;
while (n >= low) {

// Look for single small sub-diagonal element

int l = n;
while (l > low) {
s = Math.abs(H[l-1][l-1]) + Math.abs(H[l][l]);
if (s == 0.0) {
s = norm;
}
if (Math.abs(H[l][l-1]) < eps * s) {
break;
}
l--;
}

// Check for convergence
// One root found

if (l == n) {
H[n][n] = H[n][n] + exshift;
d[n] = H[n][n];
e[n] = 0.0;
n--;
iter = 0;

// Two roots found

} else if (l == n-1) {
w = H[n][n-1] * H[n-1][n];
p = (H[n-1][n-1] - H[n][n]) / 2.0;
q = p * p + w;
z = Math.sqrt(Math.abs(q));
H[n][n] = H[n][n] + exshift;
H[n-1][n-1] = H[n-1][n-1] + exshift;
x = H[n][n];

// Real pair

if (q >= 0) {
if (p >= 0) {
z = p + z;
} else {
z = p - z;
}
d[n-1] = x + z;
d[n] = d[n-1];
if (z != 0.0) {
d[n] = x - w / z;
}
e[n-1] = 0.0;
e[n] = 0.0;
x = H[n][n-1];
s = Math.abs(x) + Math.abs(z);
p = x / s;
q = z / s;
r = Math.sqrt(p * p+q * q);
p = p / r;
q = q / r;

// Row modification

for (int j = n-1; j < nn; j++) {
z = H[n-1][j];
H[n-1][j] = q * z + p * H[n][j];
H[n][j] = q * H[n][j] - p * z;
}

// Column modification

for (int i = 0; i <= n; i++) {
z = H[i][n-1];
H[i][n-1] = q * z + p * H[i][n];
H[i][n] = q * H[i][n] - p * z;
}

// Accumulate transformations

for (int i = low; i <= high; i++) {
z = V[i][n-1];
V[i][n-1] = q * z + p * V[i][n];
V[i][n] = q * V[i][n] - p * z;
}

// Complex pair

} else {
d[n-1] = x + p;
d[n] = x + p;
e[n-1] = z;
e[n] = -z;
}
n = n - 2;
iter = 0;

// No convergence yet

} else {

// Form shift

x = H[n][n];
y = 0.0;
w = 0.0;
if (l < n) {
y = H[n-1][n-1];
w = H[n][n-1] * H[n-1][n];
}

// Wilkinson's original ad hoc shift

if (iter == 10) {
exshift += x;
for (int i = low; i <= n; i++) {
H[i][i] -= x;
}
s = Math.abs(H[n][n-1]) + Math.abs(H[n-1][n-2]);
x = y = 0.75 * s;
w = -0.4375 * s * s;
}

// MATLAB's new ad hoc shift

if (iter == 30) {
s = (y - x) / 2.0;
s = s * s + w;
if (s > 0) {
s = Math.sqrt(s);
if (y < x) {
s = -s;
}
s = x - w / ((y - x) / 2.0 + s);
for (int i = low; i <= n; i++) {
H[i][i] -= s;
}
exshift += s;
x = y = w = 0.964;
}
}

iter = iter + 1;   // (Could check iteration count here.)

// Look for two consecutive small sub-diagonal elements

int m = n-2;
while (m >= l) {
z = H[m][m];
r = x - z;
s = y - z;
p = (r * s - w) / H[m+1][m] + H[m][m+1];
q = H[m+1][m+1] - z - r - s;
r = H[m+2][m+1];
s = Math.abs(p) + Math.abs(q) + Math.abs(r);
p = p / s;
q = q / s;
r = r / s;
if (m == l) {
break;
}
if (Math.abs(H[m][m-1]) * (Math.abs(q) + Math.abs(r)) <
eps * (Math.abs(p) * (Math.abs(H[m-1][m-1]) + Math.abs(z) +
Math.abs(H[m+1][m+1])))) {
break;
}
m--;
}

for (int i = m+2; i <= n; i++) {
H[i][i-2] = 0.0;
if (i > m+2) {
H[i][i-3] = 0.0;
}
}

// Double QR step involving rows l:n and columns m:n

for (int k = m; k <= n-1; k++) {
boolean notlast = (k != n-1);
if (k != m) {
p = H[k][k-1];
q = H[k+1][k-1];
r = (notlast ? H[k+2][k-1] : 0.0);
x = Math.abs(p) + Math.abs(q) + Math.abs(r);
if (x != 0.0) {
p = p / x;
q = q / x;
r = r / x;
}
}
if (x == 0.0) {
break;
}
s = Math.sqrt(p * p + q * q + r * r);
if (p < 0) {
s = -s;
}
if (s != 0) {
if (k != m) {
H[k][k-1] = -s * x;
} else if (l != m) {
H[k][k-1] = -H[k][k-1];
}
p = p + s;
x = p / s;
y = q / s;
z = r / s;
q = q / p;
r = r / p;

// Row modification

for (int j = k; j < nn; j++) {
p = H[k][j] + q * H[k+1][j];
if (notlast) {
p = p + r * H[k+2][j];
H[k+2][j] = H[k+2][j] - p * z;
}
H[k][j] = H[k][j] - p * x;
H[k+1][j] = H[k+1][j] - p * y;
}

// Column modification

for (int i = 0; i <= Math.min(n,k+3); i++) {
p = x * H[i][k] + y * H[i][k+1];
if (notlast) {
p = p + z * H[i][k+2];
H[i][k+2] = H[i][k+2] - p * r;
}
H[i][k] = H[i][k] - p;
H[i][k+1] = H[i][k+1] - p * q;
}

// Accumulate transformations

for (int i = low; i <= high; i++) {
p = x * V[i][k] + y * V[i][k+1];
if (notlast) {
p = p + z * V[i][k+2];
V[i][k+2] = V[i][k+2] - p * r;
}
V[i][k] = V[i][k] - p;
V[i][k+1] = V[i][k+1] - p * q;
}
}  // (s != 0)
}  // k loop
}  // check convergence
}  // while (n >= low)

// Backsubstitute to find vectors of upper triangular form

if (norm == 0.0) {
return;
}

for (n = nn-1; n >= 0; n--) {
p = d[n];
q = e[n];

// Real vector

if (q == 0) {
int l = n;
H[n][n] = 1.0;
for (int i = n-1; i >= 0; i--) {
w = H[i][i] - p;
r = 0.0;
for (int j = l; j <= n; j++) {
r = r + H[i][j] * H[j][n];
}
if (e[i] < 0.0) {
z = w;
s = r;
} else {
l = i;
if (e[i] == 0.0) {
if (w != 0.0) {
H[i][n] = -r / w;
} else {
H[i][n] = -r / (eps * norm);
}

// Solve real equations

} else {
x = H[i][i+1];
y = H[i+1][i];
q = (d[i] - p) * (d[i] - p) + e[i] * e[i];
t = (x * s - z * r) / q;
H[i][n] = t;
if (Math.abs(x) > Math.abs(z)) {
H[i+1][n] = (-r - w * t) / x;
} else {
H[i+1][n] = (-s - y * t) / z;
}
}

// Overflow control

t = Math.abs(H[i][n]);
if ((eps * t) * t > 1) {
for (int j = i; j <= n; j++) {
H[j][n] = H[j][n] / t;
}
}
}
}

// Complex vector

} else if (q < 0) {
int l = n-1;

// Last vector component imaginary so matrix is triangular

if (Math.abs(H[n][n-1]) > Math.abs(H[n-1][n])) {
H[n-1][n-1] = q / H[n][n-1];
H[n-1][n] = -(H[n][n] - p) / H[n][n-1];
} else {
cdiv(0.0,-H[n-1][n],H[n-1][n-1]-p,q);
H[n-1][n-1] = cdivr;
H[n-1][n] = cdivi;
}
H[n][n-1] = 0.0;
H[n][n] = 1.0;
for (int i = n-2; i >= 0; i--) {
double ra,sa,vr,vi;
ra = 0.0;
sa = 0.0;
for (int j = l; j <= n; j++) {
ra = ra + H[i][j] * H[j][n-1];
sa = sa + H[i][j] * H[j][n];
}
w = H[i][i] - p;

if (e[i] < 0.0) {
z = w;
r = ra;
s = sa;
} else {
l = i;
if (e[i] == 0) {
cdiv(-ra,-sa,w,q);
H[i][n-1] = cdivr;
H[i][n] = cdivi;
} else {

// Solve complex equations

x = H[i][i+1];
y = H[i+1][i];
vr = (d[i] - p) * (d[i] - p) + e[i] * e[i] - q * q;
vi = (d[i] - p) * 2.0 * q;
if (vr == 0.0 & vi == 0.0) {
vr = eps * norm * (Math.abs(w) + Math.abs(q) +
Math.abs(x) + Math.abs(y) + Math.abs(z));
}
cdiv(x*r-z*ra+q*sa,x*s-z*sa-q*ra,vr,vi);
H[i][n-1] = cdivr;
H[i][n] = cdivi;
if (Math.abs(x) > (Math.abs(z) + Math.abs(q))) {
H[i+1][n-1] = (-ra - w * H[i][n-1] + q * H[i][n]) / x;
H[i+1][n] = (-sa - w * H[i][n] - q * H[i][n-1]) / x;
} else {
cdiv(-r-y*H[i][n-1],-s-y*H[i][n],z,q);
H[i+1][n-1] = cdivr;
H[i+1][n] = cdivi;
}
}

// Overflow control

t = Math.max(Math.abs(H[i][n-1]),Math.abs(H[i][n]));
if ((eps * t) * t > 1) {
for (int j = i; j <= n; j++) {
H[j][n-1] = H[j][n-1] / t;
H[j][n] = H[j][n] / t;
}
}
}
}
}
}

// Vectors of isolated roots

for (int i = 0; i < nn; i++) {
if (i < low | i > high) {
for (int j = i; j < nn; j++) {
V[i][j] = H[i][j];
}
}
}

// Back transformation to get eigenvectors of original matrix

for (int j = nn-1; j >= low; j--) {
for (int i = low; i <= high; i++) {
z = 0.0;
for (int k = low; k <= Math.min(j,high); k++) {
z = z + V[i][k] * H[k][j];
}
V[i][j] = z;
}
}
}

/* ------------------------
Constructor
* ------------------------ */

/** Check for symmetry, then construct the eigenvalue decomposition
@param A    Square matrix
@return     Structure to access D and V.
*/

public EigenvalueDecomposition (Matrix Arg) {
double[][] A = Arg.getArray();
n = Arg.getColumnDimension();
V = new double[n][n];
d = new double[n];
e = new double[n];

issymmetric = true;
for (int j = 0; (j < n) & issymmetric; j++) {
for (int i = 0; (i < n) & issymmetric; i++) {
issymmetric = (A[i][j] == A[j][i]);
}
}

if (issymmetric) {
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
V[i][j] = A[i][j];
}
}

// Tridiagonalize.
tred2();

// Diagonalize.
tql2();

} else {
H = new double[n][n];
ort = new double[n];

for (int j = 0; j < n; j++) {
for (int i = 0; i < n; i++) {
H[i][j] = A[i][j];
}
}

// Reduce to Hessenberg form.
orthes();

// Reduce Hessenberg to real Schur form.
hqr2();
}
}

/* ------------------------
Public Methods
* ------------------------ */

/** Return the eigenvector matrix
@return     V
*/

public Matrix getV () {
return new Matrix(V,n,n);
}

/** Return the real parts of the eigenvalues
@return     real(diag(D))
*/

public double[] getRealEigenvalues () {
return d;
}

/** Return the imaginary parts of the eigenvalues
@return     imag(diag(D))
*/

public double[] getImagEigenvalues () {
return e;
}

/** Return the block diagonal eigenvalue matrix
@return     D
*/

public Matrix getD () {
Matrix X = new Matrix(n,n);
double[][] D = X.getArray();
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
D[i][j] = 0.0;
}
D[i][i] = d[i];
if (e[i] > 0) {
D[i][i+1] = e[i];
} else if (e[i] < 0) {
D[i][i-1] = e[i];
}
}
return X;
}
}
```

this gives NoClassDefFound error during runtime. i have done following tasks: D:\jama-1.0.2>set path=c:\jdk1.5.0_22\bin D:\jama-1.0.2>set classpath=.; D:\jama-1.0.2>javac EigenvalueDecomposition.java D:\jama-1.0.2>java EigenvalueDecomposition now it gives NoClassDefFound Error. pls help me to resolve problem thanks

Error during runtime NoClassDefFound in java
Error during runtime NoClassDefFound in java  code: package Jama...; } } this gives NoClassDefFound error during runtime. i have done...:\jama-1.0.2>java EigenvalueDecomposition now it gives NoClassDefFound Error. pls
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Error during runtime NoClassDefFound in java  code: package Jama...; } } this gives NoClassDefFound error during runtime. i have done...:\jama-1.0.2>java EigenvalueDecomposition now it gives NoClassDefFound Error. pls
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