1155 Heap Paths (30 point(s))

PAT 1155 Heap Paths (30 分)- 甲级 In computer science, a heap is a specialized tree-based data structure that satisfies the heap property: if P is a parent node of C, then the key (the value) of P is either greater than or equal to (i... 阅读详情

1155 Heap Paths (30 point(s))

In computer science, a heap is a specialized tree-based data structure that satisfies the heap property: if P is a parent node of C, then the key (the value) of P is either greater than or equal to (in a max heap) or less than or equal to (in a min heap) the key of C. A common implementation of a heap is the binary heap, in which the tree is a complete binary tree. (Quoted from Wikipedia at https://en.wikipedia.org/wiki/Heap_(data_structure))

One thing for sure is that all the keys along any path from the root to a leaf in a max/min heap must be in non-increasing/non-decreasing order.

Your job is to check every path in a given complete binary tree, in order to tell if it is a heap or not.

Input Specification:

Each input file contains one test case. For each case, the first line gives a positive integer N (1<N≤1,000), the number of keys in the tree. Then the next line contains N distinct integer keys (all in the range of int), which gives the level order traversal sequence of a complete binary tree.

Output Specification:

For each given tree, first print all the paths from the root to the leaves. Each path occupies a line, with all the numbers separated by a space, and no extra space at the beginning or the end of the line. The paths must be printed in the following order: for each node in the tree, all the paths in its right subtree must be printed before those in its left subtree.

Finally print in a line Max Heap if it is a max heap, or Min Heap for a min heap, or Not Heap if it is not a heap at all.

Sample Input 1:

8
98 72 86 60 65 12 23 50

Sample Output 1:

98 86 23
98 86 12
98 72 65
98 72 60 50
Max Heap

Sample Input 2:

8
8 38 25 58 52 82 70 60

Sample Output 2:

8 25 70
8 25 82
8 38 52
8 38 58 60
Min Heap

Sample Input 3:

8
10 28 15 12 34 9 8 56

Sample Output 3:

10 15 8
10 15 9
10 28 34
10 28 12 56
Not Heap

考点:输出树的所有路径、堆的判断

注意点:

1. 对于最大堆,任何一个从根到叶子结点的的路径都应该是非递增路径;对于最小堆,任何一个从根到叶子结点的路径都应该是非递减路径,由此可以这个特性在得到路径的过程的时候就进行判断。

2. 关于树的遍历中对vector<int> v的操作。即注意当跳出递归时需要v.pop_back()。

AC代码1(利用特性)

#include<iostream>
#include<vector>
#include<cstring>
#include<algorithm>
using namespace std;
const int MAX = 1007;
int N;
int levelOrder[MAX];
vector<int> v;
bool isMax =true;bool isMin = true; 
void judge(){
	int inc,dec = 0;
	for(int i=0;i<v.size()-1;i++){
		if(isMin&&v[i+1]<v[i]) isMin=false;
		if(isMax&&v[i+1]>v[i]) isMax=false;
	}
}
void output(){
	for(int i=0;i<v.size();i++){
		if(i==0) cout<<v[i];
		else cout<<" "<<v[i];
	}
	cout<<endl;
}
void dfs(int root){
	v.push_back(levelOrder[root]);
	if(2*root>N){
		output();
		judge(); 
		return;
	}
	if(2*root+1<=N){
		dfs(2*root+1);
		v.pop_back();
	} 
	if(2*root<=N){
		dfs(2*root);
		v.pop_back();
	}
}
int main(void){
	cin>>N;
	for(int i=1;i<=N;i++) cin>>levelOrder[i];
	dfs(1);
	if(isMin) puts("Min Heap");
	else if(isMax) puts("Max Heap");
	else puts("Not Heap"); 
	return 0;
}

DFS的另一种写法 

void dfs(int root){
	v.push_back(levelOrder[root]);
	if(2*root>N){
		output();
		v.pop_back(); 
		return;
	}
	if(2*root+1<=N) dfs(2*root+1); 
	if(2*root<=N) dfs(2*root); 
	v.pop_back();
}

AC代码2(利用定义) 

#include<iostream>
#include<vector>
#include<cstring>
#include<algorithm>
using namespace std;
const int MAX = 1007;
int N;
int levelOrder[MAX];
vector<int> v;
bool isMaxHeap(){
	for(int i=1;i<=N/2;i++){
		if(2*i<=N&&levelOrder[i]<levelOrder[2*i]) return false;
		if(2*i+1<=N&&levelOrder[i]<levelOrder[2*i+1]) return false;
	}
	return true;
}
bool isMinHeap(){
	for(int i=1;i<=N/2;i++){
		if(2*i<=N&&levelOrder[i]>levelOrder[2*i]) return false;
		if(2*i+1<=N&&levelOrder[i]>levelOrder[2*i+1]) return false;
	}
	return true;
}
void output(){
	for(int i=0;i<v.size();i++){
		if(i==0) cout<<v[i];
		else cout<<" "<<v[i];
	}
	cout<<endl;
}
void dfs(int root){
	v.push_back(levelOrder[root]);
	if(2*root>N){
		output();
		return;
	}
	if(2*root+1<=N){
		dfs(2*root+1);
		v.pop_back();
	} 
	if(2*root<=N){
		dfs(2*root);
		v.pop_back();
	}
}
int main(void){
	cin>>N;
	for(int i=1;i<=N;i++) cin>>levelOrder[i];
	dfs(1);
	if(isMaxHeap()) puts("Max Heap");
	else if(isMinHeap()) puts("Min Heap");
	else puts("Not Heap");
	return 0;
}

 

【信息科学与工程学】【数据科学】数据科学领域——第三篇 数学08 几何学00总篇 编号英文名称中文名称简要说明/归属1Foundations of Geometry几何基础公理化方法, Hilbert《几何基础》2Euclidean Geometry (Synthetic)欧几里得几何(综合法)《几何原本》传统3Analytic Geometry解析几何坐标方法, Descartes, Fermat4Plane Geometry平面几何三角形、圆、共点共线等经典问题5Solid Geometry立体几何三维空间中的图形与度量6Non-Euclidean Geometry非欧几何平行公设不 阅读详情

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B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. 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plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.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');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % 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")

1155 Heap Paths (30point(s)) - C语言 PAT 甲级

* 按照由右到左 dfs 遍历完全二叉树,队列存储路径,遇到叶子节点输出路径 * 判断大小顶堆,父节点一直比子节点大为大顶堆,父节点一直比子节点小为小顶堆,否则不是堆

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PAT_甲级_1155 Heap Paths (30point(s)) (C++)【DFS/大根堆、小根堆判断】

目录 1,题目描述 题目大意 2,思路 3,AC代码 4,解题过程 1,题目描述 Sample Input 1: 8 98 72 86 60 65 12 23 50 Sample Output 1: 98 86 23 98 86 12 98 72 65 98 72 60 50 Max Heap Sample Input 2: 8 8 38 25 58 5...

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PAT Advanced 1155 Heap Paths (30)

问题描述: 1155 Heap Paths (30 分) In computer science, a heap is a specialized tree-based data structure that satisfies the heap property: if P is a parent node of C, then the key (the value) of P is eit...

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In computer science, a heap is a specialized tree-based data structure that satisfies the heap property: if P is a parent node of C, then the key (the value) of P is either greater than or equal to (i...

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