Error in popsize < sampsize : non-conformable arrays
时间: 2024-05-26 20:12:36 浏览: 26
This error message occurs when you are trying to compare or perform an operation between two arrays that have different dimensions or shapes. Specifically, it is telling you that the array for 'popsize' has a dimension that is smaller than the array for 'sampsize'.
To fix this error, you need to make sure that the two arrays have the same dimensions or shape, or adjust your code to perform the operation in a way that is compatible with the dimensions of the arrays.
相关问题
请提供一段python代码:ski-opt GA 优化器,初始种群是20组gamma和nu,适应度函数自己拟定和种群没有一点关系
以下是一段使用SciPy库中的differential_evolution函数实现的GA优化器代码示例:
```python
import numpy as np
from scipy.optimize import differential_evolution
# 自定义适应度函数
def my_fitness(x):
gamma, nu = x
# TODO: 根据实际需求编写适应度函数
return gamma ** 2 + nu ** 2
# 定义初始种群
init_pop = np.random.uniform(low=-10.0, high=10.0, size=(20, 2))
# 使用differential_evolution函数进行GA优化
result = differential_evolution(func=my_fitness, bounds=[(-10, 10), (-10, 10)], popsize=20, init=init_pop)
# 输出优化结果
print(result.x)
```
在以上代码中,自定义了适应度函数my_fitness,该函数的输入为一个长度为2的numpy数组x,其中x[0]表示gamma,x[1]表示nu。适应度函数的返回值越小,则说明该组gamma和nu越优秀。
接下来使用SciPy库中的differential_evolution函数,对my_fitness函数进行GA优化。其中:
- func:需要优化的目标函数(即适应度函数)
- bounds:每个参数的取值范围
- popsize:每一代的种群数量
- init:初始种群
运行该代码后,differential_evolution函数会返回一个结果,其中result.x即为最优的参数(gamma和nu)组合。
User class ControlArc {std::string star; std::string startTime; std::string endTime; std::string circleId; };由上面类组成的std::vector<std::vector<ControlArc>> controlArcList,在task.h文件的plan类中已经通过读外部json实例化,遗传算法每个染色体由4个基因,每个基因都是controlArcList 随机产生一定个数的vector<ControlArc>组成的数组,适应度函数是每条染色体上(endTime-startTime)值最小,请给出完整的c++遗传算法代码,其中要写.h文件和.cpp文件
下面是遗传算法的C++实现,包括.h文件和.cpp文件,代码中使用了遗传算法的常用基本操作,包括初始化种群、选择、交叉、变异等。其中,适应度函数为计算每个染色体上(endTime-startTime)值的最小值。
task.h:
```c++
#ifndef TASK_H
#define TASK_H
#include <vector>
#include <string>
class ControlArc {
public:
std::string star;
std::string startTime;
std::string endTime;
std::string circleId;
};
class Plan {
public:
std::vector<std::vector<ControlArc>> controlArcList;
Plan() {}
Plan(std::vector<std::vector<ControlArc>> controlArcList) {
this->controlArcList = controlArcList;
}
};
#endif // TASK_H
```
ga.h:
```c++
#ifndef GA_H
#define GA_H
#include <vector>
#include "task.h"
class GA {
public:
GA(int popSize, int maxGen, double pc, double pm, int geneSize, int arcNum, std::vector<std::vector<ControlArc>> controlArcList);
void run();
double fitness(Plan& p);
private:
int popSize; // 种群规模
int maxGen; // 最大迭代次数
double pc; // 交叉概率
double pm; // 变异概率
int geneSize; // 染色体长度
int arcNum; // vector<ControlArc> 数组中元素的个数
std::vector<std::vector<ControlArc>> controlArcList; // 控制弧列表
std::vector<Plan> pop; // 种群
Plan bestIndiv; // 最优个体
double bestFitness; // 最优适应度
Plan decode(std::vector<std::vector<ControlArc>> gene);
std::vector<std::vector<ControlArc>> encode(Plan indiv);
void initPop();
void select(std::vector<Plan>& newPop);
void crossover(Plan& p1, Plan& p2);
void mutate(Plan& indiv);
};
#endif // GA_H
```
ga.cpp:
```c++
#include <algorithm>
#include <cstdlib>
#include <ctime>
#include <iostream>
#include "ga.h"
GA::GA(int popSize, int maxGen, double pc, double pm, int geneSize, int arcNum, std::vector<std::vector<ControlArc>> controlArcList) {
this->popSize = popSize;
this->maxGen = maxGen;
this->pc = pc;
this->pm = pm;
this->geneSize = geneSize;
this->arcNum = arcNum;
this->controlArcList = controlArcList;
this->bestFitness = 9999999.0;
}
void GA::run() {
srand((unsigned int)time(NULL)); // 随机数生成器初始化
initPop(); // 初始化种群
for (int i = 0; i < maxGen; i++) { // 迭代maxGen次
std::vector<Plan> newPop; // 新种群
select(newPop); // 选择
for (int j = 0; j < popSize / 2; j++) { // 交叉
Plan& p1 = newPop[j * 2];
Plan& p2 = newPop[j * 2 + 1];
crossover(p1, p2);
}
for (int j = 0; j < popSize; j++) { // 变异
mutate(newPop[j]);
}
pop = newPop;
for (int j = 0; j < popSize; j++) { // 更新最优个体和最优适应度
double fit = fitness(pop[j]);
if (fit < bestFitness) {
bestFitness = fit;
bestIndiv = pop[j];
}
}
std::cout << "第" << i + 1 << "代,最优适应度:" << bestFitness << std::endl;
}
}
double GA::fitness(Plan& p) {
std::vector<std::vector<ControlArc>> gene = encode(p);
double minEndTime = 9999999.0;
for (int i = 0; i < gene.size(); i++) {
std::vector<ControlArc>& arcs = gene[i];
double endTime = 0.0;
for (int j = 0; j < arcs.size(); j++) {
double t = atof(arcs[j].endTime.c_str()) - atof(arcs[j].startTime.c_str());
if (t > 0) { // 时间差小于0的不计算
endTime += t;
}
}
if (endTime < minEndTime) {
minEndTime = endTime;
}
}
return minEndTime;
}
Plan GA::decode(std::vector<std::vector<ControlArc>> gene) {
return Plan(gene);
}
std::vector<std::vector<ControlArc>> GA::encode(Plan indiv) {
return indiv.controlArcList;
}
void GA::initPop() {
for (int i = 0; i < popSize; i++) {
std::vector<std::vector<ControlArc>> gene(geneSize, std::vector<ControlArc>(arcNum));
for (int j = 0; j < geneSize; j++) {
for (int k = 0; k < arcNum; k++) {
gene[j][k] = controlArcList[rand() % controlArcList.size()][rand() % controlArcList[0].size()];
}
}
pop.push_back(decode(gene));
}
bestIndiv = pop[0];
bestFitness = fitness(bestIndiv);
}
void GA::select(std::vector<Plan>& newPop) {
double sumFitness = 0.0;
std::vector<double> fitnessArr(popSize);
for (int i = 0; i < popSize; i++) {
double fit = fitness(pop[i]);
sumFitness += fit;
fitnessArr[i] = sumFitness;
}
for (int i = 0; i < popSize; i++) {
double fit = (double)rand() / RAND_MAX * sumFitness;
int j;
for (j = 0; j < popSize - 1; j++) {
if (fit <= fitnessArr[j]) {
break;
}
}
newPop.push_back(pop[j]);
}
}
void GA::crossover(Plan& p1, Plan& p2) {
if ((double)rand() / RAND_MAX < pc) { // 满足概率要求
int point1 = rand() % geneSize;
int point2 = rand() % geneSize;
if (point1 > point2) { // 确保point1小于point2
int temp = point1;
point1 = point2;
point2 = temp;
}
std::vector<std::vector<ControlArc>> gene1 = encode(p1);
std::vector<std::vector<ControlArc>> gene2 = encode(p2);
for (int i = point1; i <= point2; i++) { // 交叉基因
std::vector<ControlArc> temp = gene1[i];
gene1[i] = gene2[i];
gene2[i] = temp;
}
p1 = decode(gene1);
p2 = decode(gene2);
}
}
void GA::mutate(Plan& indiv) {
if ((double)rand() / RAND_MAX < pm) { // 满足概率要求
std::vector<std::vector<ControlArc>> gene = encode(indiv);
int point = rand() % geneSize;
for (int i = 0; i < arcNum; i++) { // 变异基因
gene[point][i] = controlArcList[rand() % controlArcList.size()][rand() % controlArcList[0].size()];
}
indiv = decode(gene);
}
}
```
使用方法:
```c++
#include <vector>
#include <fstream>
#include "ga.h"
#include "task.h"
using namespace std;
int main()
{
int popSize = 50; // 种群规模
int maxGen = 100; // 最大迭代次数
double pc = 0.8; // 交叉概率
double pm = 0.2; // 变异概率
int geneSize = 4; // 染色体长度
int arcNum = 10; // vector<ControlArc> 数组中元素的个数
// 读取控制弧列表
ifstream fin("controlArcList.json");
nlohmann::json j;
fin >> j;
vector<vector<ControlArc>> controlArcList = j.get<vector<vector<ControlArc>>>();
GA ga(popSize, maxGen, pc, pm, geneSize, arcNum, controlArcList);
ga.run();
return 0;
}
```