def update_operator(self, pop_size): """ 更新算子:更新下一时刻的位置和速度 """ c1 = 2 # 学习因子,一般为2 c2 = 2 w = 0.4 # 自身权重因子 for i in range(pop_size): # 更新速度 self.pop_v[i] = w * self.pop_v[i] + c1 * random.uniform(0, 1) * ( self.p_best[i] - self.pop_x[i]) + c2 * random.uniform(0, 1) * (self.g_best - self.pop_x[i]) # 更新位置 self.pop_x[i] = self.pop_x[i] + self.pop_v[i] # 越界保护 for j in range(self.var_num): if self.pop_x[i][j] < self.bound[0][j]: self.pop_x[i][j] = self.bound[0][j] if self.pop_x[i][j] > self.bound[1][j]: self.pop_x[i][j] = self.bound[1][j] # 更新p_best和g_best if self.fitness(self.pop_x[i]) > self.fitness(self.p_best[i]): self.p_best[i] = self.pop_x[i] if self.fitness(self.pop_x[i]) > self.fitness(self.g_best): self.g_best = self.pop_x[i]
时间: 2024-04-28 14:22:27 浏览: 12
这段代码是粒子群算法中的更新算子,用于更新下一时刻每个个体的位置和速度。具体来说,算法首先定义了学习因子c1、c2以及自身权重因子w,然后对种群中的每个个体进行更新。
在更新速度时,算法使用当前速度self.pop_v[i]、个体历史最优位置self.p_best[i]以及全局历史最优位置self.g_best来计算下一时刻速度的值。其中,c1和c2的作用类似于权重,用于控制个体历史最优位置和全局历史最优位置对速度的影响程度。w是自身权重因子,用于控制个体自身历史速度对下一时刻速度的影响程度。
在更新位置时,算法将当前位置self.pop_x[i]加上下一时刻速度self.pop_v[i],得到下一时刻的位置。
为了确保更新后的位置不越界,算法使用了越界保护,即对于每个变量都检查是否越界,并将越界的变量值修正为边界值。
最后,算法更新个体历史最优位置self.p_best[i]和全局历史最优位置self.g_best,如果当前个体的位置比其历史最优位置更优,则将个体历史最优位置更新为当前位置;如果当前个体的位置比全局历史最优位置更优,则将全局历史最优位置更新为当前位置。
需要注意的是,更新算子是粒子群算法的核心,其更新策略的好坏直接影响算法的收敛性和性能。因此,在实现粒子群算法时,需要根据具体问题进行调整和优化。
相关问题
class Dog6: def __init__(self, initial_name): self._name = initial_name def get_self(self): return self def __str__(self): # # Implement this! # return __ def __repr__(self): return "<Dog named '" + self._name + "'>" def test_inside_a_method_self_refers_to_the_containing_object(self): fido = self.Dog6("Fido") self.assertEqual(__, fido.get_self()) # Not a string! def test_str_provides_a_string_version_of_the_object(self): fido = self.Dog6("Fido") self.assertEqual("Fido", str(fido)) def test_str_is_used_explicitly_in_string_interpolation(self): fido = self.Dog6("Fido") self.assertEqual(__, "My dog is " + str(fido)) def test_repr_provides_a_more_complete_string_version(self): fido = self.Dog6("Fido") self.assertEqual(__, repr(fido)) def test_all_objects_support_str_and_repr(self): seq = [1, 2, 3] self.assertEqual(__, str(seq)) self.assertEqual(__, repr(seq)) self.assertEqual(__, str("STRING")) self.assertEqual(__, repr("STRING"))
Here is the completed code with the missing parts:
```
class Dog6:
def __init__(self, initial_name):
self._name = initial_name
def get_self(self):
return self
def __str__(self):
return self._name
def __repr__(self):
return "<Dog named '" + self._name + "'>"
def test_inside_a_method_self_refers_to_the_containing_object(self):
fido = Dog6("Fido")
self.assertEqual(fido, fido.get_self()) # Not a string!
def test_str_provides_a_string_version_of_the_object(self):
fido = Dog6("Fido")
self.assertEqual("Fido", str(fido))
def test_str_is_used_explicitly_in_string_interpolation(self):
fido = Dog6("Fido")
self.assertEqual("My dog is Fido", "My dog is " + str(fido))
def test_repr_provides_a_more_complete_string_version(self):
fido = Dog6("Fido")
self.assertEqual("<Dog named 'Fido'>", repr(fido))
def test_all_objects_support_str_and_repr(self):
seq = [1, 2, 3]
self.assertEqual("[1, 2, 3]", str(seq))
self.assertEqual("[1, 2, 3]", repr(seq))
self.assertEqual("STRING", str("STRING"))
self.assertEqual("'STRING'", repr("STRING"))
```
Note that in `test_str_is_used_explicitly_in_string_interpolation`, the expected output is `"My dog is Fido"` instead of `"My dog is <Dog named 'Fido'>"` because the `+` operator implicitly calls `str` on the object being concatenated.
这是上题的代码:def infix_to_postfix(expression): precedence = {'!': 3, '&': 2, '|': 1, '(': 0} op_stack = [] postfix_list = [] token_list = expression.split() for token in token_list: if token.isalnum(): postfix_list.append(token) elif token == '(': op_stack.append(token) elif token == ')': top_token = op_stack.pop() while top_token != '(': postfix_list.append(top_token) top_token = op_stack.pop() else: # operator while op_stack and precedence[op_stack[-1]] >= precedence[token]: postfix_list.append(op_stack.pop()) op_stack.append(token) while op_stack: postfix_list.append(op_stack.pop()) return ' '.join(postfix_list) class Node: def __init__(self, value): self.value = value self.left_child = None self.right_child = None def build_expression_tree(postfix_expr): operator_stack = [] token_list = postfix_expr.split() for token in token_list: if token.isalnum(): node = Node(token) operator_stack.append(node) else: right_node = operator_stack.pop() left_node = operator_stack.pop() node = Node(token) node.left_child = left_node node.right_child = right_node operator_stack.append(node) return operator_stack.pop() def evaluate_expression_tree(node, variable_values): if node.value.isalnum(): return variable_values[node.value] else: left_value = evaluate_expression_tree(node.left_child, variable_values) right_value = evaluate_expression_tree(node.right_child, variable_values) if node.value == '!': return not left_value elif node.value == '&': return left_value and right_value elif node.value == '|': return left_value or right_value expression = "!a & (b | c)" postfix_expression = infix_to_postfix(expression) expression_tree = build_expression_tree(postfix_expression) variable_values = {'a': True, 'b': False, 'c': True} result = evaluate_expression_tree(expression_tree, variable_values) print(result)
好的,感谢提供代码。以下是针对你的问题的解答:
1. 打印二叉树构造过程
在 build_expression_tree 函数中,operator_stack 列表存储了构建二叉树的过程。我们可以在函数中添加打印语句来查看二叉树构建过程。
代码如下:
```
def build_expression_tree(postfix_expr):
operator_stack = []
token_list = postfix_expr.split()
for token in token_list:
if token.isalnum():
node = Node(token)
operator_stack.append(node)
else:
right_node = operator_stack.pop()
left_node = operator_stack.pop()
node = Node(token)
node.left_child = left_node
node.right_child = right_node
operator_stack.append(node)
# 打印二叉树构建过程
print([i.value for i in operator_stack])
return operator_stack.pop()
```
在运行 build_expression_tree 函数时,将会打印出二叉树构建过程。
2. 打印后缀表达式和二叉树后序遍历序列
在函数 infix_to_postfix 中,postfix_list 列表存储了后缀表达式。我们可以在函数返回后缀表达式的同时,打印出后缀表达式。
代码如下:
```
def infix_to_postfix(expression):
precedence = {'!': 3, '&': 2, '|': 1, '(': 0}
op_stack = []
postfix_list = []
token_list = expression.split()
for token in token_list:
if token.isalnum():
postfix_list.append(token)
elif token == '(':
op_stack.append(token)
elif token == ')':
top_token = op_stack.pop()
while top_token != '(':
postfix_list.append(top_token)
top_token = op_stack.pop()
else:
# operator
while op_stack and precedence[op_stack[-1]] >= precedence[token]:
postfix_list.append(op_stack.pop())
op_stack.append(token)
while op_stack:
postfix_list.append(op_stack.pop())
# 打印后缀表达式
print(f"后缀表达式:{' '.join(postfix_list)}")
return ' '.join(postfix_list)
def build_expression_tree(postfix_expr):
operator_stack = []
token_list = postfix_expr.split()
for token in token_list:
if token.isalnum():
node = Node(token)
operator_stack.append(node)
else:
right_node = operator_stack.pop()
left_node = operator_stack.pop()
node = Node(token)
node.left_child = left_node
node.right_child = right_node
operator_stack.append(node)
# 打印二叉树构建过程
print([i.value for i in operator_stack])
return operator_stack.pop()
expression = "!a & (b | c)"
postfix_expression = infix_to_postfix(expression)
expression_tree = build_expression_tree(postfix_expression)
# 打印二叉树后序遍历序列
print("二叉树后序遍历序列:", end="")
def postorder(node):
if node:
postorder(node.left_child)
postorder(node.right_child)
print(node.value, end=" ")
postorder(expression_tree)
print()
```
在运行程序时,将会先打印出后缀表达式,然后是构建二叉树的过程,最后打印出二叉树的后序遍历序列。
3. 打印二叉树评估过程
在 evaluate_expression_tree 函数中,我们可以添加打印语句来查看二叉树评估的过程。
代码如下:
```
def evaluate_expression_tree(node, variable_values):
if node.value.isalnum():
return variable_values[node.value]
else:
left_value = evaluate_expression_tree(node.left_child, variable_values)
right_value = evaluate_expression_tree(node.right_child, variable_values)
if node.value == '!':
result = not left_value
elif node.value == '&':
result = left_value and right_value
elif node.value == '|':
result = left_value or right_value
# 打印二叉树评估过程
print(f"节点 {node.value} 的值为 {result}")
return result
expression = "!a & (b | c)"
postfix_expression = infix_to_postfix(expression)
expression_tree = build_expression_tree(postfix_expression)
variable_values = {'a': True, 'b': False, 'c': True}
result = evaluate_expression_tree(expression_tree, variable_values)
print(result)
```
在运行程序时,将会先打印出后缀表达式,然后是构建二叉树的过程,接着会打印出二叉树评估的过程。
4. 显示公式真值表
可以通过遍历变量的所有取值,来计算表达式的真值表。
代码如下:
```
expression = "!a & (b | c)"
postfix_expression = infix_to_postfix(expression)
expression_tree = build_expression_tree(postfix_expression)
# 遍历变量的所有可能取值
variable_values = {'a': True, 'b': False, 'c': True}
variable_names = list(variable_values.keys())
print(" ".join(variable_names), expression)
for i in range(2**len(variable_names)):
values = [bool(int(x)) for x in bin(i)[2:].zfill(len(variable_names))]
variable_values = dict(zip(variable_names, values))
result = evaluate_expression_tree(expression_tree, variable_values)
print(" ".join([str(int(x)) for x in values]), int(result))
```
在运行程序时,将会先打印出后缀表达式,然后是构建二叉树的过程,接着会打印出真值表。