USCAO section1.3 Barn Repair

USACO section 3.4 Closed Fences(计算几何+叉积+二分) Closed Fences A closed fence in the plane is a set of non-crossing, connected line segments with N corners (3 i, yi}, i in (1..N). Every pair of adjacent vertices defines a side of the fence. 阅读详情

Barn Repair

It was a dark and stormy night that ripped the roof and gates off the stalls that hold Farmer John's cows. Happily, many of the cows were on vacation, so the barn was not completely full.

The cows spend the night in stalls that are arranged adjacent to each other in a long line. Some stalls have cows in them; some do not. All stalls are the same width.

Farmer John must quickly erect new boards in front of the stalls, since the doors were lost. His new lumber supplier will supply him boards of any length he wishes, but the supplier can only deliver a small number of total boards. Farmer John wishes to minimize the total length of the boards he must purchase.

Given M (1 <= M <= 50), the maximum number of boards that can be purchased; S (1 <= S <= 200), the total number of stalls; C (1 <= C <= S) the number of cows in the stalls, and the C occupied stall numbers (1 <= stall_number <= S), calculate the minimum number of stalls that must be blocked in order to block all the stalls that have cows in them.

Print your answer as the total number of stalls blocked.

PROGRAM NAME: barn1

INPUT FORMAT

Line 1:M, S, and C (space separated)
Lines 2-C+1:Each line contains one integer, the number of an occupied stall.

SAMPLE INPUT (file barn1.in)

4 50 18
3
4
6
8
14
15
16
17
21
25
26
27
30
31
40
41
42
43

OUTPUT FORMAT

A single line with one integer that represents the total number of stalls blocked.

SAMPLE OUTPUT (file barn1.out)

25
[One minimum arrangement is one board covering stalls 3-8, one covering 14-21, one covering 25-31, and one covering 40-43.]

解题思路:题意是将一个区间分为N个小区间使得小区间间隔的和最小。
       1:将区间排序为有序区间
       2:算出每个区间间隔并排序
       3:将区间尾首相减在减去允许分的M个最大间隔
      当允许划分的间隔大于区间个数直接输出区间个数即可
 
/*
ID:nealgav1
PROG:barn1
LANG:C++
*/
#include<cstdio>
#include<algorithm>
#define N 1234
using namespace std;
int line[N];
bool cmp(int a,int b)
{
  return a<b;
}
int main()
{
  freopen("barn1.in","r",stdin);
  freopen("barn1.out","w",stdout);
  int n,m,cas;
  while(scanf("%d%d%d",&n,&m,&cas)!=EOF)
  {
    for(int i=0;i<cas;i++)
    scanf("%d",&line[i]);
    if(n>cas)
    printf("%d\n",cas);
    else
    {
      sort(line,line+cas,cmp);
    int ans=line[cas-1]-line[0];
    for(int i=0;i<cas-1;i++)
    line[i]=line[i+1]-line[i];
    sort(line,line+cas-1,cmp);
    int j=cas-2;
    for(int i=0;i<n-1;i++)//减少一个
    ans-=line[j--];
    printf("%d\n",ans+n);
   }
  }
}

 
 
 
 
 
Barn Repair Russ Cox

If we can purchase M boards, then we can leave unblocked M-1 runs of stalls without cows in them, in addition to any stalls on the leftmost side that don't have cows and any stalls on the rightmost side that don't have cows.

We input the list of cows in stalls, storing into an array whether or not there is a cow in a particular stall. Then we walk the array counting sizes of runs of cowless stalls. We sort the list of sizes and pick the M-1 largest ones as the stalls that will remain uncovered.

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <assert.h>

#define MAXSTALL 200
int hascow[MAXSTALL];

int
intcmp(const void *va, const void *vb)
{
	return *(int*)vb - *(int*)va;
}

void
main(void)
{
    FILE *fin, *fout;
    int n, m, nstall, ncow, i, j, c, lo, hi, nrun;
    int run[MAXSTALL];

    fin = fopen("barn1.in", "r");
    fout = fopen("barn1.out", "w");
    
    assert(fin != NULL && fout != NULL);

    fscanf(fin, "%d %d %d", &m, &nstall, &ncow);
    for(i=0; i<ncow; i++) {
	fscanf(fin, "%d", &c);
	hascow[c-1] = 1;
    }

    n = 0;	/* answer: no. of uncovered stalls */

    /* count empty stalls on left */
    for(i=0; i<nstall && !hascow[i]; i++)
	n++;
    lo = i;

    /* count empty stalls on right */
    for(i=nstall-1; i>=0 && !hascow[i]; i--)
	n++;
    hi = i+1;

    /* count runs of empty stalls */
    nrun = 0;
    i = lo;
    while(i < hi) {
	while(hascow[i] && i<hi)
	    i++;

	for(j=i; j<hi && !hascow[j]; j++)
	    ;

	run[nrun++] = j-i;
	i = j;
    }

    /* sort list of runs */
    qsort(run, nrun, sizeof(run[0]), intcmp);

    /* uncover best m-1 runs */
    for(i=0; i<nrun && i<m-1; i++)
	n += run[i];

    fprintf(fout, "%d\n", nstall-n);
    exit(0);
}
Alexandru Tudorica's solution might be simpler:
var f:text;
    a,b:array[1..1000] of longint;
    i,m,s,c,k:longint;

procedure qsort(l,r:longint);
    var i,j,x,y:longint;
begin
     i:=l; j:=r; x:=a[(l+r) div 2];
     repeat
           while a[i]<x do i:=i+1;
           while x<a[j] do j:=j-1;
           if i<=j then begin
                y:=a[i]; a[i]:=a[j]; a[j]:=y;
                i:=i+1;
                j:=j-1;
           end;
     until i>j;
     if l<j then qsort(l,j);
     if i<r then qsort(i,r);
end;

procedure qsortb(l,r:longint);
    var i,j,x,y:longint;
begin
     i:=l; j:=r; x:=b[(l+r) div 2];
     repeat
           while b[i]<x do i:=i+1;
           while x<b[j] do j:=j-1;
           if i<=j then
           begin
                y:=b[i]; b[i]:=b[j]; b[j]:=y;
                i:=i+1;
                j:=j-1;
           end;
     until i>j;
     if l<j then qsortb(l,j);
     if i<r then qsortb(i,r);
end;


begin
     assign(f,'barn1.in');
     reset(f);
     readln(f,m,k,c);
     for i:=1 to c do readln(f,a[i]);
     qsort(1,c);
     for i:=1 to c-1 do b[i]:=a[i+1]-a[i]-1;
     qsortb(1,c-1);
     for i:=c-1 downto (c-m+1) do s:=s+b[i];
     close(f);
     assign(f,'barn1.out');
     rewrite(f);
     writeln(f,a[c]-a[1]-s+1);
     close(f);
end.

 
 
 
 
 
 
 
 
 
USER: Neal Gavin Gavin [nealgav1]
TASK: barn1
LANG: C++

Compiling...
Compile: OK

Executing...
   Test 1: TEST OK [0.000 secs, 3352 KB]
   Test 2: TEST OK [0.000 secs, 3352 KB]
   Test 3: TEST OK [0.000 secs, 3352 KB]
   Test 4: TEST OK [0.000 secs, 3352 KB]
   Test 5: TEST OK [0.000 secs, 3352 KB]
   Test 6: TEST OK [0.000 secs, 3352 KB]
   Test 7: TEST OK [0.000 secs, 3352 KB]
   Test 8: TEST OK [0.000 secs, 3352 KB]
   Test 9: TEST OK [0.000 secs, 3352 KB]
   Test 10: TEST OK [0.000 secs, 3352 KB]

All tests OK.

Your program ('barn1') produced all correct answers! This is your submission #2 for this problem. Congratulations!

Here are the test data inputs:

------- test 1 ----
4 50 17
3
4
6
8
14
15
16
17
25
26
27
30
31
40
41
42
43
------- test 2 ----
2 10 4
2
4
6
8
------- test 3 ----
3 27 16
2
3
5
6
8
9
10
13
14
15
16
19
20
21
22
27
------- test 4 ----
1 200 8
101
105
102
106
103
107
104
99
------- test 5 ----
50 200 10
18
69
195
38
73
28
6
172
53
99
------- test 6 ----
50 30 6
30
25
20
15
10
5
------- test 7 ----
20 200 80
65
178
64
70
18
32
88
90
98
20
152
31
118
117
127
81
175
73
136
161
165
63
130
133
190
10
4
138
200
43
189
37
86
182
145
110
67
126
114
153
99
25
155
119
176
55
48
197
62
147
125
60
12
23
112
96
27
122
35
50
36
49
149
108
100
188
77
191
6
121
166
132
82
95
150
89
22
40
128
56
------- test 8 ----
4 200 100
72
180
46
198
196
131
165
112
52
133
187
93
57
35
128
65
127
130
12
49
88
155
122
193
101
164
98
143
54
149
38
84
45
139
79
16
102
20
14
150
188
33
176
135
29
80
19
74
11
114
95
185
137
59
32
189
66
67
191
91
77
134
18
10
7
200
8
13
55
24
142
184
17
6
109
105
43
181
85
94
151
160
115
25
116
111
37
104
144
97
90
141
120
119
152
182
123
172
40
23
------- test 9 ----
20 195 100
1
2
3
4
5
11
12
13
14
15
21
22
23
24
25
31
32
33
34
35
41
42
43
44
45
51
52
53
54
55
61
62
63
64
65
71
72
73
74
75
81
82
83
84
85
91
92
93
94
95
101
102
103
104
105
111
112
113
114
115
121
122
123
124
125
131
132
133
134
135
141
142
143
144
145
151
152
153
154
155
161
162
163
164
165
171
172
173
174
175
181
182
183
184
185
191
192
193
194
195
------- test 10 ----
1 200 2
1
200

AgentVerse实战:构建多智能体协作系统的关键技术与应用场景 本文深入探讨了AgentVerse框架在构建多智能体协作系统中的关键技术及应用场景。通过分析智能体通信协议、环境交互机制和任务分解算法,展示了如何利用大型语言模型(LLM)实现智能体间的专业化分工与动态协作。文章还提供了智能软件开发团队和智能客服系统等典型应用案例,以及部署优化实践,为开发者构建高效的多智能体系统提供实用指导。 阅读详情

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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain&#39;s apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. 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. Download.2. addpath(genpath(&#39;DeepLearnToolbox&#39;));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand(&#39;state&#39;,0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W&#39;); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand(&#39;state&#39;,0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = &#39;sigm&#39;;%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, &#39;Too big error&#39;);```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand(&#39;state&#39;,0)sae = saesetup([784 100]);sae.ae{1}.activation_function = &#39;sigm&#39;;sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)&#39;)% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = &#39;sigm&#39;;nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, &#39;Too big error&#39;);```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x&#39;,28,28,60000))/255;test_x = double(reshape(test_x&#39;,28,28,10000))/255;train_y = double(train_y&#39;);test_y = double(test_y&#39;);%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you&#39;ll get around 1.2% errorrand(&#39;state&#39;,0)cnn.layers = { struct(&#39;type&#39;, &#39;i&#39;) %input layer struct(&#39;type&#39;, &#39;c&#39;, &#39;outputmaps&#39;, 6, &#39;kernelsize&#39;, 5) %convolution layer struct(&#39;type&#39;, &#39;s&#39;, &#39;scale&#39;, 2) %sub sampling layer struct(&#39;type&#39;, &#39;c&#39;, &#39;outputmaps&#39;, 12, &#39;kernelsize&#39;, 5) %convolution layer struct(&#39;type&#39;, &#39;s&#39;, &#39;scale&#39;, 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, &#39;Too big error&#39;);```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(&#39;state&#39;,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, &#39;Too big error&#39;);%% ex2 neural net with L2 weight decayrand(&#39;state&#39;,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, &#39;Too big error&#39;);%% ex3 neural net with dropoutrand(&#39;state&#39;,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, &#39;Too big error&#39;);%% ex4 neural net with sigmoid activation functionrand(&#39;state&#39;,0)nn = nnsetup([784 100 10]);nn.activation_function = &#39;sigm&#39;; % 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, &#39;Too big error&#39;);%% ex5 plotting functionalityrand(&#39;state&#39;,0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = &#39;softmax&#39;; % 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, &#39;Too big error&#39;);%% 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(&#39;state&#39;,0)nn = nnsetup([784 20 10]); nn.output = &#39;softmax&#39;; % 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, &#39;Too big error&#39;);```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

USACO section3.1 Shaping Regions(漂浮法)

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nealgavin的专栏 1052

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nealgavin的专栏 980

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nealgavin的专栏 937

USACO section 2.2 Party Lamps

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nealgavin的专栏 875

USCAO section Mother's Milk(搜索)

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nealgavin的专栏 847

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xie0510_的博客 268

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既然你都点开这里了,那可以给我点个关注吗?可以吗?可以吗? 483

Bugku 326 Baby-heap1 题解:glibc 2.23 堆溢出到 FSOP 劫持 _IO_list_all(附完整 exp)

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