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/** * 灰度预测模型 * * @author Sean Chen * @version 1.0 2012-12-6 */public class GrayModel { private double a0, a1, a2; private int size; private double error; public GrayModel() { } public void build(double[] x0) { size = x0.length; double[] x1 = new double[size]; x1[0] = x0[0]; for (int i = 1; i < size; i++) { x1[i] = x0[i] + x1[i - 1]; } double[][] b = new double[size - 1][2]; double[][] bt = new double[2][size - 1]; double[][] y = new double[size - 1][1]; for (int i = 0; i < b.length; i++) { b[i][0] = -(x1[i] + x1[i + 1]) / 2; b[i][1] = 1; bt[0][i] = b[i][0]; bt[1][i] = 1; y[i][0] = x0[i + 1]; } double[][] t = new double[2][2]; multiply(bt, b, t); t = inverse(t); double[][] t1 = new double[2][size - 1]; multiply(t, bt, t1); double[][] t2 = new double[2][1]; multiply(t1, y, t2); a0 = t2[0][0]; double u = t2[1][0]; a2 = u / a0; a1 = x0[0] - a2; a0 = -a0; error = 0; for (int i = 0; i < x0.length; i++) { double d = (x0[i] - getX0(i)); error += d * d; } error /= x0.length; } /** * 误差 * * @return */ public double getError() { return error; } double getX1(int k) { return a1 * Math.exp(a0 * k) + a2; } double getX0(int k) { // return a0 * a1 * Math.exp(a0 * k); if (k == 0) return a1 * Math.exp(a0 * k) + a2; else return a1 * (Math.exp(a0 * k) - Math.exp(a0 * (k - 1))); } /** * 预测后续的值 * * @param index * @return */ public double nextValue(int index) { if (index < 0) throw new IndexOutOfBoundsException(); return getX0(size + index); } /** * 预测下一个值 * * @return */ public double nextValue() { return nextValue(0); } static double[][] inverse(double[][] t) { double[][] a = new double[2][2]; double det = t[0][0] * t[1][1] - t[0][1] * t[1][0]; a[0][0] = t[1][1] / det; a[0][1] = -t[1][0] / det; a[1][0] = -t[0][1] / det; a[1][1] = t[0][0] / det; return a; } static void multiply(double[][] left, double[][] right, double[][] dest) { int n1 = left.length; int m1 = left[0].length; int m2 = right[0].length; for (int k = 0; k < n1; k++) { for (int s = 0; s < m2; s++) { dest[k][s] = 0; for (int i = 0; i < m1; i++) { dest[k][s] += left[k][i] * right[i][s]; } } } } public static void main(String[] args) { GrayModel gs = new GrayModel(); // 函数 sin+cos double[] y = new double[10]; double step = 0.001; double x = 0.001; for (int i = 0; i < y.length; i++) { y[i] = Math.sin(x) + Math.cos(x); x += step; } gs.build(y); for (int i = 0; i < 5; i++) { // 真实值与预测值的差值 System.out.println(Math.sin(x) + Math.cos(x) - gs.nextValue(i)); x += step; } System.out.println(Math.sqrt(gs.getError())); }}这篇关于灰度预测模型的Java实现的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
