@ConditionalOnProperty(value = "elasticsearch.enabled", havingValue = "true")
时间: 2023-09-28 10:14:01 浏览: 43
这是一个Spring Boot的注解`@ConditionalOnProperty`,它用于在满足指定条件时才加载Bean。在这个例子中,它的条件是`elasticsearch.enabled`属性值为`true`。如果该属性值为`true`,则相关的Bean会被加载;如果该属性值不为`true`或未配置,相关的Bean将不会被加载。这个注解通常用于根据配置文件中的属性来动态决定是否加载某些Bean。
相关问题
Unable to find a @SpringBootConfiguration, you need to use @ContextConfiguration or @SpringBootTest(classes=...) with your test
If you are encountering the error message "Unable to find a @SpringBootConfiguration, you need to use @ContextConfiguration or @SpringBootTest(classes=...) with your test," it means that Spring Boot is unable to detect the configuration class for your test.
To resolve this issue, you have a few options:
1. Use @ContextConfiguration: Annotate your test class with @ContextConfiguration and specify the location of your configuration file(s). For example:
```java@ContextConfiguration(locations = "classpath:applicationContext.xml")
public class MyTest {
// ...
}
```
2. Use @SpringBootTest(classes=...): Annotate your test class with @SpringBootTest and specify the configuration class(es) using the "classes" attribute. For example:
```java@SpringBootTest(classes = MyConfiguration.class)
public class MyTest {
// ...
}
```
Make sure to replace `MyConfiguration` with the actual class that contains your Spring Boot configuration.
3. Check your test package structure: Ensure that your test class is located in the same or a sub-package of your main application class. By default, Spring Boot will automatically scan for configuration classes within its package and sub-packages.
By applying one of these solutions, you should be able to resolve the error and properly configure Spring Boot for your tests.
import cmaimport numpy as np# 定义目标函数def quadratic(bd_X, bd_Y, x3, x4): # ... 这里是你的目标函数代码 ...# 创建优化器对象x0 = np.array([10, 10, 0, 0]) # 初始向量sigma0 = 1.0 # 初始标准差es = cma.CMAEvolutionStrategy(x0, sigma0)# 运行优化过程for i in range(100): # 迭代100次 solutions = es.ask() # 获取一组候选解向量 fitness_list = [quadratic(*x) for x in solutions] # 计算目标函数值 es.tell(solutions, fitness_list) # 更新优化器状态 # 输出当前最佳的解和目标函数值 best_solution = es.result.xbest best_fitness = es.result.fbest print("迭代次数:", i) print("当前最佳解:", best_solution) print("目标函数值:", best_fitness)# 输出优化结果best_solution = es.result.xbestbest_fitness = es.result.fbestprint("优化结果:", best_solution)print("目标函数值:", best_fitness)
这段代码缺少模块的导入语句,我补充一下:
```python
import cma
import numpy as np
```
完整代码如下:
```python
import cma
import numpy as np
# 定义目标函数
def quadratic(bd_X, bd_Y, x3, x4):
# ... 这里是你的目标函数代码 ...
# 创建优化器对象
x0 = np.array([10, 10, 0, 0]) # 初始向量
sigma0 = 1.0 # 初始标准差
es = cma.CMAEvolutionStrategy(x0, sigma0)
# 运行优化过程
for i in range(100): # 迭代100次
solutions = es.ask() # 获取一组候选解向量
fitness_list = [quadratic(*x) for x in solutions] # 计算目标函数值
es.tell(solutions, fitness_list) # 更新优化器状态
# 输出当前最佳的解和目标函数值
best_solution = es.result.xbest
best_fitness = es.result.fbest
print("迭代次数:", i)
print("当前最佳解:", best_solution)
print("目标函数值:", best_fitness)
# 输出优化结果
best_solution = es.result.xbest
best_fitness = es.result.fbest
print("优化结果:", best_solution)
print("目标函数值:", best_fitness)
```
在上述代码中,我们首先导入了`cma`和`numpy`两个模块,然后定义了目标函数`quadratic`。接着,我们创建了一个优化器对象`es`,并使用`ask`方法获取一组候选解向量,用目标函数计算它们的目标函数值,然后使用`tell`方法更新优化器状态。在每次迭代完成后,我们输出当前最佳解和目标函数值,并最终输出优化结果。