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copied!<p>I try to run the Java code on eclipse. There is code like "system.out.print" but when i press run the console show this...... </p> <p>Invalid layout of java.lang.String at value</p> <p>A fatal error has been detected by the Java Runtime Environment:</p> <p>Internal Error (javaClasses.cpp:136), pid=2276, tid=2148</p> <p>fatal error: Invalid layout of preloaded class</p> <p>JRE version: (7.0_45-b18) (build )</p> <p>Java VM: Java HotSpot(TM) 64-Bit Server VM (24.45-b08 mixed mode windows-amd64 compressed oops)</p> <p>Failed to write core dump. Minidumps are not enabled by default on client versions of Windows</p> <p>An error report file with more information is saved as: C:\Users\Administrator\Workspace\Present\Present\hs_err_pid2276.log</p> <p>If you would like to submit a bug report, please visit:</p> <p><a href="http://bugreport.sun.com/bugreport/crash.jsp" rel="nofollow">http://bugreport.sun.com/bugreport/crash.jsp</a></p> <p>how to fix it. help me please. And here is the code i try to run...</p> <pre><code>import Jama.Matrix; import Jama.QRDecomposition; public class LeastSquare { private final int N; // number of observations private final int degree; // degree of the polynomial regression private final Matrix beta; // the polynomial regression coefficients private double SSE; // sum of squares due to error private double SST; // total sum of squares public LeastSquare(double[] x, double[] y, int degree) { this.degree = degree; N = x.length; // build Vandermonde matrix double[][] vandermonde = new double[N][degree+1]; for (int i = 0; i < N; i++) { for (int j = 0; j <= degree; j++) { vandermonde[i][j] = Math.pow(x[i], j); } } Matrix X = new Matrix(vandermonde); // create matrix from vector Matrix Y = new Matrix(y, N); // find least squares solution QRDecomposition qr = new QRDecomposition(X); beta = qr.solve(Y); // mean of y[] values double sum = 0.0; for (int i = 0; i < N; i++) sum += y[i]; double mean = sum / N; // total variation to be accounted for for (int i = 0; i < N; i++) { double dev = y[i] - mean; SST += dev*dev; } // variation not accounted for Matrix residuals = X.times(beta).minus(Y); SSE = residuals.norm2() * residuals.norm2(); } public double beta(int j) { return beta.get(j, 0); } public int degree() { return degree; } public double R2() { if (SST == 0.0) return 1.0; // constant function return 1.0 - SSE/SST; } public double predict(double x) { // horner's method double y = 0.0; for (int j = degree; j >= 0; j--) y = beta(j) + (x * y); return y; } public String toString() { String s = ""; int j = degree; // ignoring leading zero coefficients while (j >= 0 && Math.abs(beta(j)) < 1E-5) j--; // create remaining terms for (j = j; j >= 0; j--) { if (j == 0) s += String.format("%.2f ", beta(j)); else if (j == 1) s += String.format("%.2f N + ", beta(j)); else s += String.format("%.2f N^%d + ", beta(j), j); } return s + " (R^2 = " + String.format("%.3f", R2()) + ")"; } public static void main(String[] args) { double[] x = { 10, 20, 40, 80, 160, 200 };// double[] y = { 100, 350, 1500, 6700, 20160, 40000 }; LeastSquare regression = new LeastSquare(x, y, 3); System.out.println(regression); } </code></pre> <p>}</p>

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