F - Number Sequence

KMP算法详解与应用

Given two sequences of numbers : a[1], a[2], ...... , a[N], and b[1], b[2], ...... , b[M] (1 <= M <= 10000, 1 <= N <= 1000000). Your task is to find a number K which make a[K] = b[1], a[K + 1] = b[2], ...... , a[K + M - 1] = b[M]. If there are more than one K exist, output the smallest one. 
Input
The first line of input is a number T which indicate the number of cases. Each case contains three lines. The first line is two numbers N and M (1 <= M <= 10000, 1 <= N <= 1000000). The second line contains N integers which indicate a[1], a[2], ...... , a[N]. The third line contains M integers which indicate b[1], b[2], ...... , b[M]. All integers are in the range of [-1000000, 1000000]. 
Output
For each test case, you should output one line which only contain K described above. If no such K exists, output -1 instead. 
Sample Input
2
13 5
1 2 1 2 3 1 2 3 1 3 2 1 2
1 2 3 1 3
13 5
1 2 1 2 3 1 2 3 1 3 2 1 2
1 2 3 2 1
Sample Output
6
-1

题意:输入两组数字:s,t找出第一个s中和t匹配的位置,

思路:KMP模板题。。。。。

下面附上代码:

#include<cstdio>
#include<iostream>
#include<cstring>
#include<algorithm>
using namespace std;
const int N=1000005;
const int M=10005;
int Next[N],s[N],t[M];
int KMP(int *s,int n,int *t,int m)
{
	int i=0,j=0;
	while(i<n)
	{
		if(j==-1||s[i]==t[j])
		{
			++i,++j;
			if(j==m)
				return i-m+1;
		}
		else 
			j=Next[j];
	}
	return -1;
}
void Getnext(int *t,int l)
{
	int i=0,j=-1;
	Next[i]=-1;
	while(i<l)
	{
		if(j==-1||t[i]==t[j])
			Next[++i]=++j;
		else 
			j=Next[j];
	}
}
int main()
{
	int T,n,m;
	scanf("%d",&T);
	while(T--)
	{
		scanf("%d %d",&n,&m);
		for(int i=0;i<n;i++)
			scanf("%d",&s[i]);
		for(int i=0;i<m;i++)
			scanf("%d",&t[i]);
		Getnext(t,m);
		printf("%d\n",KMP(s,n,t,m));
	}
	return 0;
}




# local fitness functions f_K for K = 0, 1, 2 (Table in TP) f = { 0: { '0': 2, '1': 1 }, 1: { '00': 2, '01': 3, '10': 2, '11': 0 }, 2: { '000': 0, '001': 1, '010': 1, '011': 0, '100': 2, '101': 0, '110': 0, '111': 0 } } def generate_sequence(N): # TODO ''' generates an N random bits (0, 1) sequence Parameters: ----------- N : number of bits in the sequence Returns: -------- sequence: sequence of N bits ''' return np.random.randint(0, 2, N) def compute_fitness(sequence, K): # TODO ''' computes the fitness of sequence, with respect to K, according to table f Parameters: ----------- sequence : N-bits sequence K : parameter K (0, 1, or 2) Returns: -------- fitness: fitness value of sequence ''' seq = str(sequence) N = len(seq) fitness = 0 if K == 0: # f0: 0->2, 1->1 for bit in seq: fitness += 2 if bit == '0' else 1 elif K == 1: # f1: 00->2, 01->3, 10->2, 11->0 for i in range(N): pair = seq[i] + seq[(i+1) % N] if pair == '00': fitness += 2 elif pair == '01': fitness += 3 elif pair == '10': fitness += 2 elif K == 2: # f2: 000->0 001->1 010->1 011->0 100->2 101->0 110->0 111->0 for i in range(N): triple = seq[i] + seq[(i+1) % N] + seq[(i+2) % N] if triple == '001' or triple == '010': fitness += 1 elif triple == '100': fitness += 2 return fitness def neighbors(sequence): ''' returns all neighbors of sequence () Parameters: ----------- sequence : N-bits sequence Returns: -------- generator of neighbors of sequence ''' # generate all neighbors that differ in 1 bit for i in range(len(sequence)): if sequence[i] == '0': new = '1' else: new = '0' yield (sequence[:i] + new + sequence[i + 1:]) def mean_steps(samples): ''' returns mean of steps taken to find solution (for deterministic) Parameters: ----------- samples : samples of deterministic_hillclimb runs Returns: -------- mean steps ''' # get mean of steps needed to find solution (sum of samples' steps/number of samples) return sum(x[2] for x in samples)/len(samples) def generate_samples(hillclimb_method, K): ''' generates 50 samples (solutions) for the hillclimb method (deterministic/probabilistic) Parameters: ----------- hillclimb_method : deterministic_hillclimb/probabilistic_hillclimb K : parameter K (0, 1, or 2) Returns: -------- generator of samples/solutions (sequence, fitness, number of steps) ''' if K not in [0, 1, 2]: raise Exception("Invalid K") # run for 50 times to get 50 samples/solutions for i in range(50): # generate initial sequence initial_sequence = generate_sequence(N=21) # get solution, its fitness, number of steps taken to find solution x, f_x, s = hillclimb_method(initial_sequence, K) yield (x, f_x, s) def deterministic_hillclimb(sequence, K): # TODO ''' performs deterministic_hillclimb Parameters: ----------- sequence : N-bits sequence K : parameter K (0, 1, or 2) Returns: -------- sequence : optimal/fittest sequence found max_fitness : fitness value of sequence steps: steps it took to reach optimum ''' current_sequence = sequence current_fitness = compute_fitness(current_sequence, K) steps = 0 while True: best_neighbor = None best_fitness = current_fitness for neighbor in neighbors(current_sequence): neighbor_fitness = compute_fitness(neighbor, K) if neighbor_fitness > best_fitness: best_fitness = neighbor_fitness best_neighbor = neighbor if best_neighbor is None: break current_sequence = best_neighbor current_fitness = best_fitness steps += 1 return current_sequence, current_fitness, steps # dictionary to save deterministic_hillclimb samples (sequence, max_fitness, steps) deterministic_samples = {} # dictionary to save number of steps to perform probabilistic hillclimb probabilistic_steps = {} # run deterministic_hillclimb, get mean steps for K in [0, 1, 2]: # for each K, generate 50 deterministic_hillclimb solutions, and save them deterministic_samples[K] = list(generate_samples(deterministic_hillclimb, K)) # for each K, save the number of steps to take (10*mean steps taken for deterministic_hillclimb) probabilistic_steps[K] = 10*round(mean_steps(deterministic_samples[K])) def probabilistic_hillclimb(sequence, K): # TODO ''' performs probabilistic_hillclimb Parameters: ----------- sequence : N-bits sequence K : parameter K (0, 1, or 2) Returns: -------- sequence : optimal/fittest sequence found max_fitness : fitness value of sequence steps: steps corresponding to parameter K ''' current_sequence = sequence current_fitness = evaluate_sequence(current_sequence, K) steps = 0 while True: neighbor_list = list(neighbors(current_sequence)) random.shuffle(neighbor_list) found_better = False for neighbor in neighbor_list: neighbor_fitness = evaluate_sequence(neighbor, K) if neighbor_fitness > current_fitness or random.random() < 0.1: current_sequence = neighbor current_fitness = neighbor_fitness steps += 1 found_better = True break if not found_better: break return current_sequence, current_fitness, steps # dictionary to save probabilistic_hillclimb samples (sequence, max_fitness, steps) probabilistic_samples = {} # run probabilistic_hillclimb for K in [0, 1, 2]: # for each K, generate 50 probabilistic_hillclimb solutions, and save them probabilistic_samples[K] = list(generate_samples(probabilistic_hillclimb, K))这是我的所有代码,可以帮我修改一下吗
最新发布
09-24
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