command 快捷鍵
- ls: lists all files in the current directory
- cd <path to directory>: change into the specified directory
- mkdir <directory name>: make a new directory with the given name
- mv <source path> <destination path>: move the file at the given source to the given destination
運(yùn)行python file
-
Using no command-line options will run the code in the file you provide and return you to the command line.
python3 lab00.py -
-i: The -i option runs your Python script, then opens an interactive session. In an interactive session, you run Python code line by line and get immediate feedback instead of running an entire file all at once. To exit, type exit() into the interpreter prompt. You can also use the keyboard shortcut Ctrl-D on Linux/Mac machines or Ctrl-Z Enter on Windows.
If you edit the Python file while running it interactively, you will need to exit and restart the interpreter in order for those changes to take effect.
python3 -i lab00.py -
-m doctest: Runs doctests in a particular file. Doctests are surrounded by triple quotes (""") within functions. Each test consists of >>> followed by some Python code and the expected output.
python3 -m doctest lab00.py
An order of operation:
| 運(yùn)算符 | 描述 |
|---|---|
| ** | 指數(shù) (最高優(yōu)先級(jí)) |
| ~ + - | 按位翻轉(zhuǎn), 一元加號(hào)和減號(hào) (最后兩個(gè)的方法名為 +@ 和 -@) |
| * / % // | 乘阅悍，除，取模和取整除 |
| + - | 加法減法 |
| >> << | 右移，左移運(yùn)算符 |
| & | 位 'AND' |
| ^ | | 位運(yùn)算符 |
| <= < > >= | 比較運(yùn)算符 |
| <> == != | 等于運(yùn)算符 |
| = %= /= //= -= += *= **= | 賦值運(yùn)算符 |
| is is not | 身份運(yùn)算符 |
| in not in | 成員運(yùn)算符 |
| not | 邏輯運(yùn)算符 |
| and | 邏輯運(yùn)算符 |
| or | 邏輯運(yùn)算符 |
/ // %
True Division: / (decimal division)
Floor Division: // (integer division) 整數(shù)部分
Modulo: % (remainder)
return statement
When Python executes a return statement, the function terminates immediately.
A return statement completes the evaluation of a call expression and provides its value.
def what_prints():
print('Hello World!')
return 'Exiting this function.'
print('61A is awesome!')
what_prints()
Hello World!
'Exiting this function.'
when 28 means when True means always
positive = 28
while positive:
print("positive?")
positive -= 3
Result: Infinite Loop because positive never comes to 0
difference between return and print
return ‘hello’
‘hello’
print(‘hello’)
hello
a, b = b, a+b
>>> a = 1; b = 0
>>> a, b = b, a+b
>>> print(a, b)
>>> hello
0 1
>>> a = 1; b = 0
>>> a = b
>>> b = a+b
>>> print(a, b)
0 0
>>> a = 1; b = 0
>>> a = b; b = a+b
>>> print(a, b)
0 0
>>> a = 1, b=2
File "<stdin>", line 1
SyntaxError: can't assign to literal
限制自變量：assert x > 0, 'x must be positive'
>>> def function_one(x):
... assert x > 0, 'x must be positive'
...
>>> function_one(x=-1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 2, in function_one
AssertionError: x must be positive
編程可視化
tutor.composingprograms.com
http://pythontutor.com/composingprograms.html#mode=edit
make_test_dice
def make_test_dice(*outcomes):
"""
Return a die that cycles deterministically through OUTCOMES.
>>> dice = make_test_dice(1, 2, 3)
>>> dice()
1
>>> dice()
2
>>> dice()
3
>>> dice()
1
>>> dice()
2
This function uses Python syntax/techniques not yet covered in this course.
The best way to understand it is by reading the documentation and examples.
"""
assert len(outcomes) > 0, 'You must supply outcomes to make_test_dice'
for o in outcomes:
assert type(o) == int and o >= 1, 'Outcome is not a positive integer'
index = len(outcomes) - 1
def dice():
nonlocal index
index = (index + 1) % len(outcomes)
return outcomes[index]
return dice
[score0, score1][0] 是值還是參數(shù)名梳虽？
score0 = 3
score1 = 5
[score0, score1][0] = 6 #the result shows that [score0, score1][0] means value of #0, not parameter
score0
>>> 3
score0 = 6
score0
>>> 6
f2 嵌在 f1內(nèi)挖函，將f2中的x2的值賦給f1的變量
def announce_highest(who, previous_high=0, previous_score=0):
"""
Return a commentary function that announces when WHO's score
increases by more than ever before in the game.
>>> f0 = announce_highest(1) # Only announce Player 1 score gains
>>> f1 = f0(11, 0)
>>> f2 = f1(11, 1)
1 point! That's the biggest gain yet for Player 1
>>> f3 = f2(20, 1)
>>> f4 = f3(5, 20) # Player 1 gets 4 points, then Swine Swap applies
19 points! That's the biggest gain yet for Player 1
>>> f5 = f4(20, 40) # Player 0 gets 35 points, then Swine Swap applies
20 points! That's the biggest gain yet for Player 1
>>> f6 = f5(20, 55) # Player 1 gets 15 points; not enough for a new high
"""
assert who == 0 or who == 1, 'The who argument should indicate a player.'
# BEGIN PROBLEM 7
"*** YOUR CODE HERE ***"
def say1(score0, score1):
diata = [score0, score1][who] - previous_score
if diata > previous_high:
if diata == 1:
print("1 point! That's the biggest gain yet for Player", who)
if diata != 1:
print(diata, "points! That's the biggest gain yet for Player", who)
return announce_highest(who, diata, [score0, score1][who])
else:
return announce_highest(who, previous_high, [score0, score1][who])
return say1
- [ ] # ?
def search(f):
"""Return the smallest non-negative integer x for which f(x) is a true value."""
x = 0
while True:
if f(x):
return x
x += 1
def invert(f):
"""Return a function g(y) that returns x such that f(x) == y.
>>> sqrt = invert(square)
>>> sqrt(16)
4
"""
return lambda y: search(lambda x: f(x) == y)
def invert(f) --> define inver(f) --> input func f, return lambda y: , (parent = global)
lambda y: search() --> define lam(y) --> input y, return func search, (parent = invert)
search(lambda x: ) --> use search(f) --> input func lam(x), return x when lam(x) True, (parent = global)
lambda x: f(x) == y --> define lam(x) --> input x, return True or False, (parent = lam(y))
Q: When is the return expression of a lambda expression executed?
Choose the number of the correct choice:
- When you assign the lambda expression to a name.
you cannot assign a name to lambda expression
When you pass the lambda expression into another function.
When the function returned by the lambda expression is called.
When the lambda expression is evaluated.
? 2
lambda z: print(z) returns Nothing!
>>> print_lambda = lambda z: print(z)
>>> print_lambda
? Function
-- OK! --
>>> one_thousand = print_lambda(1000)
? 1000
-- OK! --
>>> one_thousand
? 1000
-- Not quite. Try again! --
? Function
-- Not quite. Try again! --
? Error
-- Not quite. Try again! --
? Nothing
-- OK! --
Draw environment diagram
n = 9
def make_adder(n):
return lambda k: k + n
add_ten = make_adder(n+1)
result = add_ten(n)
My environment diagram answer
global frame:
? n [9]
? make_adder ------> func make_adder(n) (parent = global)
? λ ------> func λ(k) (parent = global)
? add_ten ------> func λ(k) (parent = f1) (lambda k: k + 10)
? result [19]
f1 make_adder(n+1) (parent = global):
? n [10]
? retur value: func λ
f2 add_ten λ(k) (parent = f1):
? k [9]
? return value: 19
如何判斷函數(shù)的執(zhí)行效率： Use cProfile.run()
def cycle01(f1, f2, f3):
def g1(n):
def g2(x):
round = n // 3
left_round = n % 3
i = 1
while i <= round:
x = f3(f2(f1(x)))
i = i+1
if left_round == 0:
return x
elif left_round == 1:
return f1(x)
elif left_round == 2:
return f2(f1(x))
return g2
return g1
def cycle02(f1, f2, f3):
def g1(n):
def g2(x):
i = 1
while i <= n:
if i <= n:
x = f1(x)
i += 1
else:
return x
if i <= n:
x = f2(x)
i += 1
else:
return x
if i <= n:
x = f3(x)
i += 1
else:
return x
return x
return g2
return g1
import cProfile
import re
cProfile.run("cycle01(lambda x: x+4, lambda x: x+6, lambda x: x+9)(10000000)(4)")
cProfile.run("cycle02(lambda x: x+4, lambda x: x+6, lambda x: x+9)(10000000)(4)")
""""""
輸出結(jié)果
PS E:\沐沐\Cousera\cs61a\lab\lab02> python E:\沐沐\Cousera\cs61a\run04.py
3333339 function calls in 5.530 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
3333333 0.762 0.000 0.762 0.000 <string>:1(<lambda>)
1 0.000 0.000 7.180 7.180 <string>:1(<module>)
1 0.000 0.000 0.000 0.000 run04.py:1(cycle01)
1 0.000 0.000 0.000 0.000 run04.py:2(g1)
1 4.767 4.767 7.180 7.180 run04.py:3(g2)
1 0.000 0.000 7.180 7.180 {built-in method builtins.exec}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
3333340 function calls in 7.648 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
3333334 0.850 0.000 0.850 0.000 <string>:1(<lambda>)
1 0.000 0.000 9.370 9.370 <string>:1(<module>)
1 0.000 0.000 0.000 0.000 run04.py:19(cycle02)
1 0.000 0.000 0.000 0.000 run04.py:20(g1)
1 6.797 6.797 9.370 9.370 run04.py:21(g2)
1 0.000 0.000 9.370 9.370 {built-in method builtins.exec}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
- [ ] # ? 為什么call減少了巷怜，時(shí)間長(zhǎng)了呢袱，效率變低了
import cProfile
import re
def count_partitions_1(n, m):
if n == 0:
return 1
elif n < 0:
return 0
elif m == 0:
return 0
else:
with_m = count_partitions_1(n-m, m)
without_m = count_partitions_1(n, m-1)
return with_m + without_m
def count_partitions_2(n, m):
if n == 0:
return 1
elif n < 0:
return 0
elif m == 0:
return 0
elif (n-m) <= m:
with_m = count_partitions_2(n-m, n-m)
without_m = count_partitions_2(n, m-1)
return with_m + without_m
elif (n-m) > m:
with_m = count_partitions_2(n-m, m)
without_m = count_partitions_2(n, m-1)
return with_m + without_m
cProfile.run("count_partitions_1(20,19)")
cProfile.run("count_partitions_2(20,19)")
cProfile.run("count_partitions_1(40,39)")
cProfile.run("count_partitions_2(40,39)")
cProfile.run("count_partitions_1(60,59)")
cProfile.run("count_partitions_2(60,59)")
cProfile.run("count_partitions_1(100,90)")
cProfile.run("count_partitions_2(100,90)")
輸出
PS C:\Users\nijian> python E:\沐沐\Cousera\cs61a\run.03.py
>>> cProfile.run("count_partitions_1(20,19)")
6174 function calls (4 primitive calls) in 0.006 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.005 0.005 <string>:1(<module>)
6171/1 0.005 0.000 0.005 0.005 run.03.py:4(count_partitions_1)
1 0.000 0.000 0.006 0.006 {built-in method builtins.exec}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
>>> cProfile.run("count_partitions_2(20,19)")
5428 function calls (4 primitive calls) in 0.006 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.006 0.006 <string>:1(<module>)
5425/1 0.006 0.000 0.006 0.006 run.03.py:16(count_partitions_2)
1 0.000 0.000 0.006 0.006 {built-in method builtins.exec}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
>>> cProfile.run("count_partitions_1(40,39)")
455648 function calls (4 primitive calls) in 0.408 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.408 0.408 <string>:1(<module>)
455645/1 0.408 0.000 0.408 0.408 run.03.py:4(count_partitions_1)
1 0.000 0.000 0.408 0.408 {built-in method builtins.exec}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
>>> cProfile.run("count_partitions_2(40,39)")
430616 function calls (4 primitive calls) in 0.460 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.460 0.460 <string>:1(<module>)
430613/1 0.460 0.000 0.460 0.460 run.03.py:16(count_partitions_2)
1 0.000 0.000 0.460 0.460 {built-in method builtins.exec}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
>>> cProfile.run("count_partitions_1(60,59)")
13748768 function calls (4 primitive calls) in 12.295 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 12.295 12.295 <string>:1(<module>)
13748765/1 12.295 0.000 12.295 12.295 run.03.py:4(count_partitions_1)
1 0.000 0.000 12.295 12.295 {built-in method builtins.exec}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
>>> cProfile.run("count_partitions_2(60,59)")
13278698 function calls (4 primitive calls) in 14.252 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 14.252 14.252 <string>:1(<module>)
13278695/1 14.252 0.000 14.252 14.252 run.03.py:16(count_partitions_2)
1 0.000 0.000 14.252 14.252 {built-in method builtins.exec}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
>>> cProfile.run("count_partitions_1(100,90)")
-944290126 function calls (4 primitive calls) in 3059.177 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 3059.177 3059.177 <string>:1(<module>)
-944290129/1 3059.177 -0.000 3059.177 3059.177 run.03.py:4(count_partitions_1)
1 0.000 0.000 3059.177 3059.177 {built-in method builtins.exec}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
>>> cProfile.run("count_partitions_2(100,90)")
-1008982726 function calls (4 primitive calls) in 4684.674 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 4684.674 4684.674 <string>:1(<module>)
-1008982729/1 4684.674 -0.000 4684.674 4684.674 run.03.py:16(count_partitions_2)
1 0.000 0.000 4684.674 4684.674 {built-in method builtins.exec}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
