torch
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@ -11,6 +11,7 @@ print(y)
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# 过滤不符合条件的元素 从列表中选择符合条件的元素组成新的列表
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print('过滤不符合条件的元素 从列表中选择符合条件的元素组成新的列表'.center(50,'*'))
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x = [-1,-4,6,7.5,-2.3,9,-11]
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y = [i for i in x if i>0]
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print(y)
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@ -20,6 +21,30 @@ x = [(x, y) for x in [1, 2, 3] for y in [3, 1, 4] if x != y]
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print("[(x, y) for x in [1, 2, 3] for y in [3, 1, 4] if x != y]: ",x)
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import numpy as np
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x1 = [[x] for x in range(3)]
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print("x1 = [[x] for x in range(3)] ",x1)
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x2 = [x for x in range(3)]
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print("[x2= x for x in range(3)] ",x2)
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x3 = [np.random.randn(ch1) for ch1 in [4]]
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print("x3 = ",x3)
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x4 = [np.random.randn(ch1) for ch1 in [4,1,5]]
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print("x4 = ",x4)
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x5 = np.random.random((4,1))
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print("x5 = ",x5)
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np.random.seed(1)
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x6 = [np.random.randn(ch1,ch2) for ch1,ch2 in zip([4],[1])]
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print("x6 = ",x6)
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w7 = [np.random.randn(ch1) for ch1 in ([4,2])]
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print("w7 = ",w7)
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# 注意： ch1 的列要与 ch2 行相同
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w8 = [np.random.randn(ch1, ch2) for ch1, ch2 in zip([2,3], [3,1])]
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print("w8 = ",w8)
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w8 = [np.random.randn(ch1, ch2) for ch1, ch2 in zip([2,3], [3,1])]
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print("w8 = ",w8)
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@ -1,4 +1,6 @@
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# https://blog.csdn.net/zjuxsl/article/details/77104382
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# 一、lambda函数也叫匿名函数，即，函数没有具体的名称。先来看一个最简单例子：
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def f(x):
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return x**2
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print(f(4))
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@ -6,18 +8,19 @@ print(f(4))
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g = lambda x : x**2
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print (g(4))
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# lambda语句中，冒号前是参数，可以有多个，用逗号隔开，冒号右边的返回值。lambda语句构建的其实是一个函数对象
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foo = [2, 18, 9, 22, 17, 24, 8, 12, 27]
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print (filter(lambda x: x % 3 == 0, foo))
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# lambda语句中，冒号前是参数，可以有多个，用逗号隔开，冒号右边的返回值。
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# lambda语句构建的其实是一个函数对象
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from functools import reduce
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reduce(lambda x,y:x+y, [1,2,3]) #6
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reduce(lambda x,y:x * y, [1,2,4]) #8
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reduce(lambda x,y: x and y, [True,False,True]) #False
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def f(x, y):
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return x + y
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reduce(lambda x, y: f(x, y), [1, 2, 3]) # 6
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# 二、lambda和普通的函数相比，就是省去了函数名称而已，同时这样的匿名函数，又不能共享在别的地方调用。
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#
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# 其实说的没错，lambda在Python这种动态的语言中确实没有起到什么惊天动地的作用，因为有很多别的方法能够代替lambda。
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#
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# 1. 使用Python写一些执行脚本时，使用lambda可以省去定义函数的过程，让代码更加精简。
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#
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# 2. 对于一些抽象的，不会别的地方再复用的函数，有时候给函数起个名字也是个难题，使用lambda不需要考虑命名的问题。
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#
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# 3. 使用lambda在某些时候让代码更容易理解。
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@ -1,4 +1,13 @@
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# reduce() 函数会对参数序列中元素进行累积。
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from functools import reduce
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def f(x, y):
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return x + y
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reduce(f, [1, 2, 3]) # 6
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lst=[1,2,3,4]
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print(reduce(lambda x,y: x*y+1, lst))
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@ -1,8 +1,8 @@
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class Spring(object):
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#season 为类的属性
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season = "the spring of class"
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print(Spring.__dict__)
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print(Spring.__dict__['season'])
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print("Spring.__dict__:",Spring.__dict__)
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print("Spring.__dict__['season'] ",Spring.__dict__['season'])
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# 实例属性的__dict__是空的，因为season是属于类属性
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s= Spring()
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print(s.__dict__)
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print("s.__dict__ ",s.__dict__)
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@ -26,6 +26,11 @@ class Hero(Person):
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person = Person()
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print ('Person.__dict__: ', Person.__dict__)
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print ('person.__dict__: ', person.__dict__)
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# person.age = 22
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person.aaa = 22
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person.sex = 'girl'
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print ('person.__dict__: ', person.__dict__)
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hero = Hero()
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print ('Hero.__dict__: ', Hero.__dict__ )
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print ('hero.__dict__: ', hero.__dict__)
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@ -1,138 +0,0 @@
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# In this notebook, you will simulate a system with of three nuclei AA , BB and CC where AA decays into BB and BB decays into CC . If exposed to a neutron flux nucleus CC can be activated into a nucleus AA .
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import numpy
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from matplotlib import pyplot as plt
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import random
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# Implement a function that tells whether a transition has occured,
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# based on the transition probability and a random number. Use the random number r from random.random()
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# and use the procedure described in the notes so that the checks can work in a reproducible way.
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def has_transitioned(p):
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r = random.random()
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return True if p>r else False
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random.seed(9867)
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assert [ has_transitioned(0.5) for i in range(10)] == [False, False, True, False, False, False, False, True, False, True]
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def evolveOne(currentState, rules):
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# YOUR CODE HERE
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for r in rules:
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if currentState == r[0]:
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return r[1] if has_transitioned(r[2]) else r[0]
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else:
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continue
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else:
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return currentState
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# these tests are worth 1 mark
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alwaysDecayRules = [
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('A', 'B', 1.0),
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('B', 'C', 1.0)
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]
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assert evolveOne('A', alwaysDecayRules) == 'B'
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assert evolveOne('B', alwaysDecayRules) == 'C'
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# these tests are worth 2 mark
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random.seed(112211)
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testRules = [
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('A', 'B', 0.33),
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('B', 'C', 0.75)
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]
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assert evolveOne('A', testRules) == 'A'
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assert evolveOne('A', testRules) == 'A'
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assert evolveOne('A', testRules) == 'A'
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assert evolveOne('A', testRules) == 'A'
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assert evolveOne('A', testRules) == 'B'
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assert evolveOne('B', testRules) == 'B'
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assert evolveOne('B', testRules) == 'C'
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assert evolveOne('B', testRules) == 'C'
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assert evolveOne('B', testRules) == 'C'
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assert evolveOne('B', testRules) == 'C'
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# with no rules there should be no change
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assert evolveOne('C', testRules) == 'C'
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# Now implement a function that takes a list of states and transition them according to the rules passed as argument.
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# This function should return a new vector of states, it should not modify the state passed as an argument!
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def evolveMany(states, rules):
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newState = []
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# YOUR CODE HERE
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for s in states:
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newState.append(evolveOne(s,rules))
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return newState
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# these tests are worth 1 mark
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random.seed(112287)
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testRules = [
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('A', 'B', 0.33),
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('B', 'C', 0.75)
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]
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initialTestArray = ['A','B','C']*5
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evolvedTest = evolveMany(initialTestArray, testRules)
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targetArray = ['B', 'C', 'C', 'A', 'C', 'C', 'A', 'B', 'C', 'A', 'C', 'C', 'B', 'C', 'C']
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assert evolvedTest == targetArray
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# checks the initial array is left unchanged
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assert initialTestArray == ['A','B','C']*5
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# Define a function that evolves a system that starts with initial amounts NA, NB and NC of AA ,
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# BB and CC nuclei and evolved it in n_timestep from time t=0t=0 to t=tmaxt=tmax .
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# The function should return three arrays, one for each atom type, of the number of nuclei
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# of that type at each time step. Each array should contain n_timestep+1 elements including the initial amount.
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def evolve_system(NA, NB, NC, rules, n_step):
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state = (['A'] * NA)+(['B'] * NB)+(['C'] * NC)
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A_count = numpy.empty(n_step + 1, dtype=int)
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B_count = numpy.empty(n_step + 1, dtype=int)
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C_count = numpy.empty(n_step + 1, dtype=int)
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# YOUR CODE HERE
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A_count[0] = NA
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B_count[0] = NB
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C_count[0] = NC
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for i in range(n_step):
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state = evolveMany(state, rules)
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A_count[i+1] = state.count('A')
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B_count[i+1] = state.count('B')
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C_count[i+1] = state.count('C')
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return A_count, B_count, C_count
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# these tests are worth 2 marks
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rules = [
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('A', 'B', 0.0033),
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('B', 'C', 0.0075),
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('C', 'A', 0.009)
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]
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r1, r2, r3 = evolve_system(0, 0, 250, rules, 17)
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assert len(r1) == 18
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assert len(r2) == 18
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assert len(r3) == 18
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# these tests are worth 2 marks
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testrules = [
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('A', 'B', 0.086),
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('B', 'C', 0.075),
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('C', 'A', 0.19)
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]
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random.seed(9485)
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r1, r2, r3 = evolve_system(200, 200, 200, testrules, 20)
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print(r1)
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print(r2)
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print(r3)
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assert (r1 == [200, 213, 233, 250, 258, 251, 266, 263, 259, 260, 265, 259, 256,
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255, 258, 256, 259, 253, 249, 247, 253]).all()
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assert (r2 == [200, 198, 201, 206, 205, 214, 214, 212, 216, 221, 225, 234, 236,
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238, 234, 235, 231, 245, 253, 256, 252]).all()
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assert (r3 == [200, 189, 166, 144, 137, 135, 120, 125, 125, 119, 110, 107, 108,
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107, 108, 109, 110, 102, 98, 97, 95]).all()
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@ -0,0 +1,9 @@
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import numpy as np
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import matplotlib.pyplot as plt
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x = np.linspace(0,100,100)
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np.random.seed(111)
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y1 = np.random.normal(0,1,100)
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# y1 = np.linalg.norm(y1,ord = 2)
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plt.plot(x,y1)
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plt.show()
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import numpy as np
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# 可以由元组创建 ， 也可由列表创建
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a = np.array(((1,2,3,4,5),(6,7,8,9,10),(11,12,13,14,15),(22,33,44,55,66)))
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print(f"a = {a}")
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a1 = a[np.ix_([0,2,3],[1,2])]
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print(f"a1 = {a1}")
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a2 = a[1,2]
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print(f"a2 = {a2}")
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a3 = a[0:2,:]
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print(f"a3 = {a3}")
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# 数组改变形状
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a4 = a.reshape(2,10)
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print(f"a4 = {a4}")
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a5 = a.ravel()
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print(f"a5 = {a5}")
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a6 = a.ravel()
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print(f"a6 = {a6}")
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a7 = a.reshape(-1)
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print(f"a7 = {a7}")
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a8 = np.vstack([a7,a6])
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print(f"a8 = {a8}")
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a9 = np.hstack([a7,a6])
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print(f"a9 = {a9}")
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a = np.array(((1,2,3,4,5),(6,7,8,9,10),(11,12,13,14,15),(22,33,44,55,66)))
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b = np.array(((22,33,44,55,66),(6,7,8,9,10),(11,12,13,14,15),(1,2,3,4,5)))
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a10 = a[a==b]
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print(f"a10 = {a10}")
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a11 = np.where(a>10,100,a)
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print(f"a11 = {a11}")
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a12 = np.where(a>10,100,-100)
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print(f"a12 = {a12}")
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import numpy as np
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a = np.arange(0,12,2).reshape(2,3)
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b = np.arange(3)
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print(f"a = {a}")
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print(f"b = {b}")
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a1 = a+b
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print(f"a1 = {a1}")
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a2 = np.row_stack([a,b])
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print(f"a2 = {a2}")
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a = np.arange(12).reshape(4,3)
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b = np.array([5,15,25])
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a3 = a+b
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print(f"a3 = {a3}")
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import numpy as np
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a = np.array([[1,2,3],[3,2,1]])
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b = np.array([[1,2],[2,1],[3,1]])
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aa,bb = np.array([[1,2,3],[1,1,1]]),np.array([[3,2,1],[1,1,1]])
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# 二维数组的点积
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a1 = np.dot(a,b)
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a11 = np.matmul(a,b)
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a111 = np.matmul(aa,bb.T)
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print(f"a1 = {a1} a11 = {a11} a111 = {a111}")
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# 二维数组的乘积
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a2 = a*0.5
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a22 = a*a
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a222 = a*(a[:,0].reshape(2,1))
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a2222 = a*(a[0,:].reshape(1,-1))
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print(f"a2 = {a2}, "
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f"a22 = {a22},\n"
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f"a222 = {a222}\n,"
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f"a2222 = {a2222}\n,"
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f"a2 sum = {a2[:,0].sum()}")
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# a22 = a*(b[0].reshape(-1).T)
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# print(f"a22 a*b = {a22}")
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# 二维数组的行
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c = np.arange(16).reshape(4,-1)
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# 二维数组的对角线元素
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a3 = np.diag(c)
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print(f"a3 = {a3}")
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# 以元素为对角线产生二维数组
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a4 = np.diag(a3)
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print(f"a4 = {a4}")
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# 特征根与特征向量
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a5 = np.linalg.eig(c)
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print(f"a5 = {a5}")
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# 线性方程组求解 ax=b
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a = np.array([[1,2,3],[2,3,1],[3,2,1]])
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b = np.array([24,39,26])
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x = np.linalg.solve(a,b)
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print(f"x = {x}")
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# 范数的计算
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c = np.arange(1,3).reshape(-1)
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a6 = np.linalg.norm(c,ord = 2)
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print(f"c = {c}, a6 = {a6}")
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a7 = np.zeros((3,4))
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print(f"a7 = {a7}")
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a8 = np.eye(2,3)
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print(f"a8 = {a8}")
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a9 = np.ones((3,3))
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print(f"a9 = {a9}")
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# 矩阵主对角线元素之和
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a10 = np.trace(a9)
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print(f"a10 = {a10}")
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# 两个数组内积
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# 矩阵a的列 必须与 矩阵b的行相同
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a111 = np.array([[1,2,3],[1,1,1]])
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b111 = np.array([[3,2,1],[1,1,1]])
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c111 = np.array([[1],[2]])
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a11 = np.inner(a111,b111)
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a12 = np.dot(a111,b111.T)
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# 两个数组对应元素相乘
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b11 = a111*b111
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print(f"b11 = {b11}")
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b112 = np.multiply(a111,b111)
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print(f"b112 = {b112}")
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b12 = a111*2
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print(f"b12 = {b12}")
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b13 = a111*c111
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print(f"b13 = {b13}")
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# 以下四种一样
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a13 = np.mat(a111)@np.mat(b111.T)
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a14 = np.mat(a111)*np.mat(b111.T)
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a15 = np.dot(np.mat(a111),np.mat(b111.T))
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a16 = np.inner(np.mat(a111),np.mat(b111))
|
||||
print(f"a11 = {a11}")
|
||||
print(f"a12 = {a12}")
|
||||
print(f"a13 = {a13}")
|
||||
print(f"a14 = {a14}")
|
||||
print(f"a15 = {a15}")
|
||||
print(f"a16 = {a16}")
|
||||
|
||||
a17 = np.zeros((3,2))
|
||||
print(f"a17 = {a17}")
|
||||
|
||||
a18 = np.empty((3,2))
|
||||
print(f"a18 = {a18}")
|
||||
|
||||
a19 = np.array([1,1])
|
||||
a18[1]= a19
|
||||
print(a18)
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
# 导入第三方模块
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
# 构造数据
|
||||
edu = [0.2515,0.3724,0.3336,0.0368,0.0057]
|
||||
labels = ['中专','大专','本科','硕士','其他']
|
||||
|
||||
# 绘制饼图
|
||||
plt.pie(x = edu, # 绘图数据
|
||||
labels=labels, # 添加教育水平标签
|
||||
autopct='%.1f%%' # 设置百分比的格式，这里保留一位小数
|
||||
)
|
||||
# 显示图形
|
||||
plt.show()
|
||||
|
|
@ -0,0 +1,15 @@
|
|||
from mpl_toolkits.mplot3d import Axes3D
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
import numpy as np
|
||||
from matplotlib import cm
|
||||
|
||||
fig=plt.figure()
|
||||
ax=fig.add_subplot(111,projection='3d')
|
||||
|
||||
u=np.linspace(-1,1,100)
|
||||
x,y=np.meshgrid(u,u)
|
||||
z=x**2+y**2
|
||||
|
||||
ax.plot_surface(x,y,z,rstride=4,cstride=4,cmap=cm.YlGnBu_r)
|
||||
plt.show()
|
||||
|
|
@ -5,15 +5,15 @@ import numpy as np
|
|||
x = np.linspace(0, 2 * np.pi, 50)
|
||||
y = np.sin(x)
|
||||
|
||||
plt.plot(x, y)
|
||||
plt.show()
|
||||
plt.plot(x, y)
|
||||
plt.plot(x, y * 2)
|
||||
plt.title("sin(x) & 2sin(x)")
|
||||
plt.show()
|
||||
# plt.plot(x, y)
|
||||
# plt.show()
|
||||
# plt.plot(x, y)
|
||||
# plt.plot(x, y * 2)
|
||||
# plt.title("sin(x) & 2sin(x)")
|
||||
# plt.show()
|
||||
|
||||
plt.plot(x, y, label="sin(x)")
|
||||
plt.plot(x, y * 2, label="2sin(x)")
|
||||
# plt.legend()
|
||||
plt.legend(loc='best')
|
||||
plt.legend(loc='best bbbbbbbbbb')
|
||||
plt.show()
|
||||
|
|
@ -0,0 +1,31 @@
|
|||
YearsExperience,Salary
|
||||
1.1,39343.00
|
||||
1.3,46205.00
|
||||
1.5,37731.00
|
||||
2.0,43525.00
|
||||
2.2,39891.00
|
||||
2.9,56642.00
|
||||
3.0,60150.00
|
||||
3.2,54445.00
|
||||
3.2,64445.00
|
||||
3.7,57189.00
|
||||
3.9,63218.00
|
||||
4.0,55794.00
|
||||
4.0,56957.00
|
||||
4.1,57081.00
|
||||
4.5,61111.00
|
||||
4.9,67938.00
|
||||
5.1,66029.00
|
||||
5.3,83088.00
|
||||
5.9,81363.00
|
||||
6.0,93940.00
|
||||
6.8,91738.00
|
||||
7.1,98273.00
|
||||
7.9,101302.00
|
||||
8.2,113812.00
|
||||
8.7,109431.00
|
||||
9.0,105582.00
|
||||
9.5,116969.00
|
||||
9.6,112635.00
|
||||
10.3,122391.00
|
||||
10.5,121872.00
|
||||
|
|
|
@ -0,0 +1,70 @@
|
|||
# https://www.cnblogs.com/judejie/p/8999832.html
|
||||
|
||||
# 工作年限与收入之间的散点图
|
||||
# 导入第三方模块
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
import numpy as np
|
||||
# 导入数据集
|
||||
# income = pd.read_csv(r'Salary_Data.csv')
|
||||
# 绘制散点图
|
||||
# sns.lmplot(x = 'YearsExperience', y = 'Salary', data = income, ci = None)
|
||||
# 显示图形
|
||||
|
||||
|
||||
class GD():
|
||||
def __init__(self,data,w,b, lr, nstep):
|
||||
self.data = data
|
||||
self.w = w
|
||||
self.b = b
|
||||
self.lr = lr
|
||||
self.nstep = nstep
|
||||
|
||||
def loss(self,w,b):
|
||||
x,y = self.data[:, 0],self.data[:, 1]
|
||||
loss = 0
|
||||
for i in range(data.shape[0]):
|
||||
loss += (w*x[i]+b-y[i])**2
|
||||
print(f"loss {loss}")
|
||||
return loss/float(data.shape[0])
|
||||
|
||||
def step_grad(self,w,b):
|
||||
x, y = self.data[:, 0], self.data[:, 1]
|
||||
w_gradient = 0
|
||||
b_gradient = 0
|
||||
for i in range(data.shape[0]):
|
||||
w_gradient += 2*(w*x[i]+b-y[i])*x[i]
|
||||
b_gradient += 2 * (w * x[i] + b - y[i])
|
||||
|
||||
w_new = w - w_gradient/data.shape[0]*self.lr
|
||||
b_new = b - b_gradient /data.shape[0] * self.lr
|
||||
return w_new,b_new
|
||||
|
||||
def gradientDescent(self):
|
||||
# history = np.empty( (self.nstep+1, 2) )
|
||||
error = np.zeros(self.nstep)
|
||||
w,b = self.w,self.b
|
||||
for i in range(self.nstep):
|
||||
w,b = self.step_grad(w, b)
|
||||
error[i]=self.loss(w,b)
|
||||
return w,b,error
|
||||
w = 12
|
||||
b =12
|
||||
income = pd.read_csv(r'Salary_Data.csv')
|
||||
data = np.array(income.values)
|
||||
nstep = 1000
|
||||
lr = 0.001
|
||||
gd = GD(data,w,b,lr,nstep)
|
||||
w,b,error = gd.gradientDescent()
|
||||
print("w,b is :",w,b)
|
||||
x = data[:,0]
|
||||
y = data[:,1]
|
||||
plt.scatter(x,y)
|
||||
# plt.legend()
|
||||
plt.plot(x, w*x+b, 'r')
|
||||
plt.show()
|
||||
|
||||
plt.plot(np.arange(nstep), error, 'r')
|
||||
plt.show()
|
||||
# 回归参数w,b的值：w,b is : 11768.548124439758 10167.865862562581
|
||||
Loading…
Reference in New Issue