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Chapter 5 notes
init
# class name
class Student:
#cstr, self means *this
def __init__(self):
# member vars
self.firstname = "[no first name]"
self.lastname = "[no last name]"
self.eyecolor = "[no eye color]"
self.age = -1
## using instances as the init value ##
class Name:
def __init__(self):
self.firstname = "[no first name]"
self.lastname = "[no last name]"
class Student:
#cstr, self means *this
def __init__(self):
# construct a Name class as a default value to student class
self.name = Name()
self.eyecolor = "[no eye color]"
self.age = -1
# creating instance
myName = Name()
# accessing
print(myName.firstname)
# assignment
myName.firstname = "Gray"
## nesting ##
myStudent = Student()
# assignment
myStudent.name.firstname = "David"
比起直接访问数据,利用 getter 和 setter 提供了更多的便利:
init
内部定义
# parameters are required
# self will be ignored
class Name:
def __init__(self, firstname, lastname):
self.firstname = firstname
self.lastname = lastname
# construct
myName = Name("David", "John")
## parameter list with default value ##
def __init__(self,
firstname = "[no first name]",
lastname = "[no last name]"):
self.firstname = firstname
self.lastname = lastname
# call, lastname will be the default value "[no last name]"
myName2 = Name("David")
parameter 数量不满足抛出的异常为 TypeError.
Python 中没有提供 access level(比如 C++ 中公私类的申明)。python 中,如果不希望被直接调用的函数,一般前后都有两个下划线,比如 init
class ExerciseSession:
def __init__(self, name, intensity, ex_time):
self.ex_name = name
self.intensity = intensity
self.ex_time = ex_time
# getters
def get_exercise(self):
return self.ex_name
def get_intensity(self):
return self.intensity
def get_duration(self):
return self.ex_time
# setters
def set_exercise(self, ex_name):
self.ex_name = ex_name
def set_intensity(self, intensity):
self.intensity = intensity
def set_duration(self, ex_time):
self.ex_time = ex_time
类中可以提供一个 log 成员函数,用于将每次的改动记录到文本中,方便跟踪。
instance 可以作为另外一个 instance 的参数。比如:
class Person:
def __init__(self, name, age, father=None, mother=None):
self.name = name
self.age = age
self.father = father
self.mother = mother
g_dad = Person("Mr. Burdell", 53)
g_mon = Person("Mrs. Burdell", 53)
george_p = Person("George P. Burdell", 25, g_dad, g_mon)
将旧的 instance 赋值给新的 instance(name),等于给了这个 instance 修改旧 instance 内内容的权限。任何通过新 instance 修改的内容,均会直接影响旧的 Instance, 比如:
myPerson1 = Person(Name("David", "Joyner"), "brown", 30)
myPerson2 = myPerson1
myPerson2.eyecolor = "blue"
这里无论是通过 myPerson1
还是 myPerson2
访问 eyecolor
,都会得到 blue
的结果。实际上,两个 Name 指向的都是同一个变量。
可以说 instance 也是 mutable 的变量。
instance 也可以作为 argument 传递给函数。这种情况下,函数也会改变 instance 内部的变量值。访问实例如下:
# song is supposed to be an instance
def printer(song):
print(song.name)
print(song.album)
print(song.year)
print(song.artist.name)
print(song.artist.label)
注意,如果函数的 argument 不是 Mutable 的,那么函数是无法改变 argument 对应的对象的。比如:
# function that modified the instance
def capitalizeName(name):
name.firstname = name.firstname.upper()
name.lastname = name.lastname.upper()
# string is immutable
# function that has done nothing to do with the instance
def capitalizeString(instring):
instring = instring.upper()
myPerson1 = Person(Name("David", "Joyner"), "brown", 30)
# instance "myPerson2" is completely rebuilded with value from myPerson1
# instance argument "name" is completely rebuilded as well
myPerson2 = Person(Name(myPerson1.name.firstname, myPerson1.name.lastname),
myPerson1.eyecolor,
myPerson1.age)
Algorithm: Technically, a collection of steps that transforms input into output; commonly, a complex set of lots of steps that is only feasible to perform with the efficiency of a computer.
以 $5!$ 为例。要以递归的方式计算 $5!$,那么需要思考的有两个问题:
可以看出来,$5!$ 可以表示为 $5 \times 4!$,$4!$ 可以表示为 $4 \times 3!$。一直直到 $2!$ 可以表示为 $2 \times 1!$。$1!$ 的值显而易见,因此可以用做 base。而之前的通项可以表达为:
$$
n \times (n-1)!
$$
因此,如果将阶乘的设计为函数 fractoral()
,那么该函数的伪代码可以写成:
fractoral(n):
if n > 1:
return n * fractoral(n-1)
else:
return 1
需要注意的是,这里最开始的 $n$,实际上是处于 stack 的最底层。最先计算的,实际上是我们的 base case,也就是这里的 1。这个过程可以想象为,如果我们要解 $5!$,那么首先要求 $4!$,如果要求 $4!$,首先要得到 $3!$…以此类推。当我们最终达到我们已知的 base case,也就是求得 $2!$ 时,整个递归会将求得的结果带入到 stack 下方已经展开的计算部分,直到完成计算。
Fibonacci(n):
if n > 2:
return Fibonacci(n-2) + Fibonacci(n-1)
else:
return 1
假设在递归的过程中,同时还有其他的操作(比如 print)。根据该操作与递归调用的位置不同,递归分为首递归(head recursion)和尾递归(tail recursion)。参考下面的例子:
# head
def count_down_1(start):
if start <= 0:
print(start)
else:
count_down_1(start - 1)
print(start)
# tail
def count_down_2(start):
if start <= 0:
print(start)
else:
print(start)
count_down_2(start - 1)
count_down_1(5)
count_down_2(5)
他们的主要区别在于递归调用是否优先。如果优先调用递归(首递归),那么最开始的打印操作会处于 stack 的最底层。他必须要等到所有栈里的递归调用结束以后,才能开始打印。因此,count_down_1()
的打印结果会从 0
打印到 5
,也就是栈顶的打印会优先执行。count_down_2()
的打印结果会从 5
到 0
,先压入栈的,也就是栈底的打印操作先执行。
该算法的算法复杂度为 $O(n^2)$。
Bubble Sort 的核心是:
实现的细节:
def bubble_sort(lst):
is_swap = True
while is_swap:
#reset tag, assume no swapping at this round
is_swap = False
for idx in range(len(lst)- 1):
#if the previous number is greater than next number
if lst[idx] > lst[idx + 1]:
# swap
temp = lst[idx]
lst[idx] = lst[idx + 1]
lst[idx + 1] = temp
# swapping happens at this round
is_swap = True
return lst
该算法的复杂度为 $O(n^2)$。
该算法的核心是:
def selction_sort(lst):
for i in range(len(lst)):
# assume the left-most element is the miniumn
min_idx = i
# checking the rest of the list [i, end]
for idx in range(i, len(lst)):
#find the index of the miniumn
if lst[idx] < lst[min_idx]:
min_idx = idx
# insert / swap
temp = lst.pop(min_idx)
lst.insert(i, temp)
#swap might be better way
#lst[i], lst[min_idx] = lst[min_idx], lst[i]
return lst
该算法的算法复杂度是 $O(n^2)$。
Insertion sort 的核心是:
对应的位置的查找原理:
细节:
def insertion_sort(lst):
# sorting the base case
if lst[0] > lst[1]:
lst[1], lst[0] = lst[0], lst[1]
# inserting starts from 2
for i in range(2, len(lst)):
# comparing sorted numbers with the right most(new incoming) number
for idx in range(0, i):
# Since the left part of the list is sorted
# if any number in that part is larger than the new incoming number
# that number will be the first one larger than the new incoming number
# Therefore, the new incoming number should be inserted before that number
if lst[idx] > lst[i]:
insert_idx = idx
temp = lst.pop(i)
lst.insert(insert_idx, temp)
break
return lst
该算法的算法复杂度是 $O(nlog(n))$。
Merge Sort 的核心是:
实现细节:
def merge_sort(lst):
if len(lst) == 1:
return lst
else:
mid = len(lst) // 2
sorted_lst = []
left = merge_sort(lst[:mid])
right = merge_sort(lst[mid:])
while(len(left) > 0 and len(right) > 0):
if left[0] < right[0]:
sorted_lst.append(left[0])
del left[0]
else:
sorted_lst.append(right[0])
del right[0]
sorted_lst.extend(left)
sorted_lst.extend(right)
return sorted_lst
按位搜寻,算法复杂度为 $O(n)$
Binary Search 的核心:
实现细节:
min >max
# iteration Binary search
def binary_search_iter_3(lst, key):
lst.sort()
min = 0
max = len(lst) - 1
while min <= max:
mid = (min + max) // 2
if key == lst[mid]:
return True
elif key < lst[mid]:
max = mid - 1
else:
min = mid + 1
return False
# recursion binary search
def binary_search_rec_2(lst, key):
lst.sort()
if len(lst) == 0:
return False
mid = len(lst) // 2
print(lst[mid])
if lst[mid] == key:
return True
elif key < lst[mid]:
return binary_search_rec_2(lst[:mid], key)
elif key > lst[mid]:
return binary_search_rec_2(lst[mid+1:], key)
Binary search + Merge sort 这种方式的主要开销在 merge sort,其复杂度是 linear search 的 $log(n)$ 倍。但由于整个数据集只需排序一次,因此如果要反复进行不同内容的搜索,Binary search 的效率要高得多。