Java学习手册:(数据结构与算法-数组)Combinations(leetcode77)

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题目:

给出两个整数n和k,求在1...n这n个数字中选出k个数字的所有组合。

输入:

n = 4 , k = 2 

输出:

[ [1 ,2], [1 ,3], [1 ,4], [2 ,3], [2 ,4], [3 ,4], ]

方法一思路:

使用回溯算法求解组合问题

方法一代码如下:

package com.haobi;

import java.util.ArrayList;
import java.util.List;

public class Combinations {
	
	// res数组用来存储所有结果
	private static List<String> res = new ArrayList<>();
	
	public static void main(String[] args) {
		List<String> list = combine(4, 2);
		for(String s : list) {
			System.out.print(s+" ");
		}
	}
	
	/**
	 * 递归函数
	 * 求解C(n,k),当前已经找到的组合存储在c中,需要从start开始搜索新的元素
	 * @param n   示例:4  
	 * @param k   示例:2
	 * @param statr  从start开始搜索
	 * @param c
	 */
	private static void generateCombinations(int n, int k, int start, ArrayList<Integer> c) {
		if(c.size() == k) {//递归终止条件:向下递归调用直至c中有k个元素
			res.add(c.toString());
			return;
		}
		//将当前遍历的i加入数组c中
		for(int i=start;i<=n;i++) {
			//将
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% Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

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