算法系列-----矩阵（二）-------------单位矩阵的加法和减法

	/**
	 * 两个一维数组相加
	 * 
	 * @param args
	 *            参数a，b是两个浮点型（double）的一维数组
	 * @return 返回值是一个浮点型一维矩阵
	 */
	public static double[] plus(double[] a, double[] b) {
		int hang = a.length;
		double[] result = new double[hang];
		for (int i = 0; i < hang; i++) {

			result[i] = a[i] + b[i];

		}
		return result;
	}

	public static void print_array(double[] result){
		for(int i=0;i<result.length;i++){
			System.out.print("\t" + result[i]);
		}
		System.out.println();
	}

	/**
	 * 矩阵相加的函数
	 * 
	 * @param args
	 *            参数a，b是两个浮点型（double）的二维数组
	 * @return 返回值是一个浮点型二维数组（矩阵a与b的和）
	 */
	public static double[][] plus(double[][] a, double[][] b) {
		int hang = a.length;
		int lie = a[0].length;
		double[][] result = new double[hang][lie];
		for (int i = 0; i < hang; i++) {
			for (int j = 0; j < lie; j++) {
				result[i][j] = a[i][j] + b[i][j];
			}
		}
		return result;
	}

	/**
	 * 矩阵相减的函数
	 * 
	 * @param args
	 *            参数a，b是两个浮点型（double）的二维数组，
	 * @return 返回值是一个浮点型二维数组（矩阵a与b的差）
	 */
	public static double[][] minus(double[][] a, double[][] b) {
		int hang = a.length;
		int lie = a[0].length;
		double[][] result = new double[hang][lie];
		for (int i = 0; i < hang; i++) {
			for (int j = 0; j < lie; j++) {
				result[i][j] = a[i][j] - b[i][j];
			}
		}
		return result;
	}

	public static void main(String[] args) {
		double[] a = { 3, 2, 1, 4};
		double[] b = { 5, 6, 9, 8};
		double[] s_test = plus(a,b);
		
		double[][] d_a = new double[][]{{1,2},{3,4}};
		double[][] d_b = new double[][]{{7, 8}, {6, 5}};
		double[][] d_test = plus(d_a,d_b);
		double[][] minus_test=minus(d_a,d_b);
		System.out.println("一维相加");
		System.out.println("--------------------------------");
		print_array(s_test);
		System.out.println("矩阵相加");
		System.out.println("--------------------------------");
		print(d_test);
		System.out.println("矩阵相减");
		System.out.println("--------------------------------");
		print(minus_test);
	}

一维相加
--------------------------------
	8.0	8.0	10.0	12.0
矩阵相加
--------------------------------
	8.0	10.0
	9.0	9.0
矩阵相减
--------------------------------
	-6.0	-6.0
	-3.0	-1.0