class NumberPrinter: def __init__(self, start, end): self.start = start self.end = end def print_numbers(self): numbers = [] for num in range(self.start, self.end + 1): if num % 10 != 3 and num % 3 != 0: numbers.append(str(num)) result = ' '.join(numbers) print(result) printer = NumberPrinter(1, 100) printer.print_numbers()优化一下
时间: 2024-04-11 10:26:23 浏览: 22
下面是对代码进行了一些优化的版本:
```python
class NumberPrinter:
def __init__(self, start, end):
self.start = start
self.end = end
def print_numbers(self):
numbers = [str(num) for num in range(self.start, self.end + 1) if num % 10 != 3 and num % 3 != 0]
result = ' '.join(numbers)
print(result)
printer = NumberPrinter(1, 100)
printer.print_numbers()
```
在这个优化的版本中,使用了列表推导式来构建满足条件的数字列表,避免了使用显式的循环和条件判断。这样可以更简洁地生成数字列表。其余部分与之前的实现相同。
相关问题
编写一个类装饰器,用于计算被装饰函数的执行时间。 class MeasureTime: def __init__(self, func): self.func = func def __call__(self, *args, **kwargs): start_time = time.time() result = self.func(*args, **kwargs) end_time = time.time() execution_time = end_time - start_time print(f"Method {self.func.__name__} executed in {execution_time:.4f} seconds.") return result
非常好的问题!你的问题提供了一个很好的示例,可以向其他人展示如何编写类装饰器。下面是一个示例代码,演示了如何使用类装饰器来测量函数的执行时间:
```
import time
class MeasureTime:
def __init__(self, func):
self.func = func
def __call__(self, *args, **kwargs):
start_time = time.time()
result = self.func(*args, **kwargs)
end_time = time.time()
execution_time = end_time - start_time
print(f"Method {self.func.__name__} executed in {execution_time:.4f} seconds.")
return result
@MeasureTime
def my_function():
time.sleep(1)
my_function()
```
在这个例子中,我们定义了一个名为`MeasureTime`的装饰器类,它采用一个函数作为其唯一参数。在`__call__`方法中,我们记录了函数执行的开始时间和结束时间,并计算了函数的执行时间。最后,我们打印出函数的名称和执行时间,并返回函数的结果。
在我们的示例中,我们使用`@MeasureTime`语法将`MeasureTime`类应用于`my_function`函数。这意味着当我们调用`my_function`时,实际上会调用`MeasureTime`的`__call__`方法,该方法会测量函数的执行时间并打印出结果。
希望这个示例可以帮助你理解类装饰器的工作原理!
class Path(object): def __init__(self,path,distancecost,timecost): self.__path = path self.__distancecost = distancecost self.__timecost = timecost #路径上最后一个节点 def getLastNode(self): return self.__path[-1] #获取路径路径 @property def path(self): return self.__path #判断node是否为路径上最后一个节点 def isLastNode(self, node): return node == self.getLastNode() #增加加点和成本产生一个新的path对象 def addNode(self, node, dprice, tprice): return Path(self.__path+[node],self.__distancecost + dprice,self.__timecost + tprice) #输出当前路径 def printPath(self): for n in self.__path: if self.isLastNode(node=n): print(n) else: print(n, end="->") print(f"最短路径距离(self.__distancecost:.0f)m") print(f"红绿路灯个数(self.__timecost:.0f)个") #获取路径总成本的只读属性 @property def dCost(self): return self.__distancecost @property def tCost(self): return self.__timecost class DirectedGraph(object): def __init__(self, d): if isinstance(d, dict): self.__graph = d else: self.__graph = dict() print('Sth error') #通过递归生成所有可能的路径 def __generatePath(self, graph, path, end, results, distancecostIndex, timecostIndex): current = path.getLastNode() if current == end: results.append(path) else: for n in graph[current]: if n not in path.path: self.__generatePath(graph, path.addNode(n,self.__graph[path.getLastNode()][n][distancecostIndex][timecostIndex]), end, results, distancecostIndex, timecostIndex) #搜索start到end之间时间或空间最短的路径,并输出 def __searchPath(self, start, end, distancecostIndex, timecostIndex): results = [] self.__generatePath(self.__graph, Path([start],0,0), end, results,distancecostIndex,timecostIndex) results.sort(key=lambda p: p.distanceCost) results.sort(key=lambda p: p.timeCost) print('The {} shortest path from '.format("spatially" if distancecostIndex==0 else "temporally"), start, ' to ', end, ' is:', end="") print('The {} shortest path from '.format("spatially" if timecostIndex==0 else "temporally"), start, ' to ', end, ' is:', end="") results[0].printPath() #调用__searchPath搜索start到end之间的空间最短的路径,并输出 def searchSpatialMinPath(self,start, end): self.__searchPath(start,end,0,0) #调用__searc 优化这个代码
这段代码中可以进行如下优化:
1. 将类属性的命名改为下划线开头的私有属性,避免外部直接修改属性值,可以使用@property装饰器来获取属性的值。
2. 可以将类的构造函数中的参数改为可选参数,避免在初始化时需要传入参数,可以在需要时再传入参数。
3. 在递归函数的参数中加入默认值,避免在调用递归函数时需要传入所有参数。
4. 可以使用f-string格式化输出语句,代码更加简洁易读。
5. 可以使用列表推导式对列表进行过滤。
6. 在搜索最短路径时可以使用heapq模块的堆排序来优化效率。
优化后的代码如下:
```python
import heapq
class Path(object):
def __init__(self, path = [], distance_cost = 0, time_cost = 0):
self._path = path
self._distance_cost = distance_cost
self._time_cost = time_cost
def get_last_node(self):
return self._path[-1]
def is_last_node(self, node):
return node == self.get_last_node()
def add_node(self, node, d_price, t_price):
return Path(self._path + [node], self._distance_cost + d_price, self._time_cost + t_price)
def print_path(self):
for n in self._path:
print(n, end="->" if not self.is_last_node(n) else "\n")
print(f"最短路径距离({self._distance_cost:.0f}m)")
print(f"红绿路灯个数({self._time_cost:.0f})个")
@property
def d_cost(self):
return self._distance_cost
@property
def t_cost(self):
return self._time_cost
class DirectedGraph(object):
def __init__(self, d = {}):
if isinstance(d, dict):
self._graph = d
else:
self._graph = dict()
print('Something went wrong!')
def __generate_path(self, graph, path, end, results, distance_cost_index=0, time_cost_index=0):
current = path.get_last_node()
if current == end:
results.append(path)
else:
for n in graph[current]:
if n not in path._path:
self.__generate_path(graph, path.add_node(n, self._graph[path.get_last_node()][n][distance_cost_index][time_cost_index]), end, results, distance_cost_index, time_cost_index)
def __search_path(self, start, end, distance_cost_index=0, time_cost_index=0):
results = []
self.__generate_path(self._graph, Path([start], 0, 0), end, results, distance_cost_index, time_cost_index)
results = heapq.nsmallest(1, results, key=lambda p: (p.d_cost, p.t_cost))
print(f"The {'spatially' if distance_cost_index==0 else 'temporally'} shortest path from {start} to {end} is:")
results[0].print_path()
def search_spatial_min_path(self, start, end):
self.__search_path(start, end, 0, 0)
def search_temporal_min_path(self, start, end):
self.__search_path(start, end, 1, 1)
```