吴恩达机器学习笔记---Octave教程(Python实现)

前言

 本节主要将吴恩达机器学习中的Octave教程操作用Python实现,主要内容包括:
 1.基本操作(Basic Operations)
 2.移动数据(Moving Data Around)
 3.计算数据(Computing on Data)
 4.绘图数据(Plotting Data)
 5.控制语句:for,while,if语句

基本操作(Basic Operations)

(一)算术运算和逻辑运算

# 算术运算
print("5 + 6 = %d" % (5+6))
print("3 - 2 = %d" % (3-2))
print("5 * 8 = %d" % (5*8))
print("1/2 = %f" % (1/2))
print("2^6 = %d" % (pow(2, 6)))

# 逻辑运算
print("1 & 0 = %d" % int(1&0))
print("1 | 0 = %d" % int(1|0))

       运行结果

5 + 6 = 11
3 - 2 = 1
5 * 8 = 40
1/2 = 0.500000
2^6 = 64
1 & 0 = 0
1 | 0 = 1

(二)变量赋值

import math
import numpy as np

# 变量赋值
a = 3
print("a = %d" % a)
b = 'hi'
print("b = %s" % b)
c = (3 >= 1)
print("c = %d" % int(c))

a = math.pi
print("pi = %f" % a)
print("保留六位小数的pi= %.6f" % a)

# 建立矩阵
A = np.mat([[1, 2], [3, 4], [5, 6]])
print("A = ", A)
V = np.mat([[1], [2], [3]])
print("V = ",  V)

# 建立特殊矩阵
A = np.ones((2, 3))
print("A = ", A)
B = np.zeros((2, 3))
print("B = ", B)
I = np.eye(6)
print("I = ", I)

C = np.random.rand(3, 3)
print("C = ", C)
D = np.random.randn(1, 3)
print("D = ", D)

       运行结果

a = 3
b = hi
c = 1
pi = 3.141593
保留六位小数的pi= 3.141593
A =  [[1 2]
 [3 4]
 [5 6]]
V =  [[1]
 [2]
 [3]]
A =  [[1. 1. 1.]
 [1. 1. 1.]]
B =  [[0. 0. 0.]
 [0. 0. 0.]]
I =  [[1. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 1.]]
C =  [[0.32699556 0.12524207 0.58057881]
 [0.64533286 0.84355689 0.77487524]
 [0.48110471 0.38639891 0.80057691]]
D =  [[-0.91338325  0.72946901 -1.07624459]]

移动数据(Moving Data Around)

import numpy as np
import pandas as pd

# 矩阵大小
A = np.mat([[1, 2], [3, 4], [5, 6]])
print("矩阵A的大小为: ", A.shape)
print("矩阵A的行数的大小为: ", A.shape[0])
print("矩阵A的列数的大小为: ", A.shape[1])

V = np.mat([1, 2, 3, 4])
print("矩阵V的最大维度为: ", max(V.shape))

# 加载数据
path = r'C:\Users\Administrator\Desktop\ML\machine-learning-ex1\ex1\ex1data1.txt'           # 文件路径,内容为两列
data = pd.read_csv(path, header = None, names = ['data1', 'data2'])
print("加载的数据的大小为: ", data.shape)

# 从加载的数据中裁剪一部分存储到V中
V = data.iloc[:10, :]
print(V)

# 操作数据
A = np.mat([[1, 2], [3, 4], [5, 6]])
print(A[2, 1])          # 索引为(2,1)的值
print(A[1, :])          # 第二行所有元素
print(A[:, 1])          # 第二列所有元素

A[:, 1] = [[1], [2], [3]]      # 把第二列替换掉
B = [[10], [11], [12]]
C = [[10, 11]]
print(np.c_[A, B])             # 为矩阵加上列
print(np.r_[A, C])             # 为矩阵加上行

       运行结果

矩阵A的大小为:  (3, 2)
矩阵A的行数的大小为:  3
矩阵A的列数的大小为:  2
矩阵V的最大维度为:  4
加载的数据的大小为:  (97, 2)
    data1    data2
0  6.1101  17.5920
1  5.5277   9.1302
2  8.5186  13.6620
3  7.0032  11.8540
4  5.8598   6.8233
5  8.3829  11.8860
6  7.4764   4.3483
7  8.5781  12.0000
8  6.4862   6.5987
9  5.0546   3.8166
6
[[3 4]]
[[2]
 [4]
 [6]]
[[ 1  1 10]
 [ 3  2 11]
 [ 5  3 12]]
[[ 1  1]
 [ 3  2]
 [ 5  3]
 [10 11]]

计算数据(Computing on Data)

import numpy as np
import math

A = np.mat([[1, 2], [3, 4], [5, 6]])
B = np.ones((3, 2))
C = np.mat([[1, 1], [2, 2]])

print("A*C = ", np.dot(A, C))             # 直接A*B也可以
print("A.*B = ", np.multiply(A, B))
print(A+1)

# 转置和逆
print("矩阵A的转置为: ", A.T)
print("矩阵A的逆为: ", A.I)

print("矩阵A的最大值: ", np.max(A))
print("矩阵A各元素之和为: ", np.sum(A))

       运行结果

A*C =  [[ 5  5]
 [11 11]
 [17 17]]
A.*B =  [[1. 2.]
 [3. 4.]
 [5. 6.]]
[[2 3]
 [4 5]
 [6 7]]
矩阵A的转置为:  [[1 3 5]
 [2 4 6]]
矩阵A的逆为:  [[-1.33333333 -0.33333333  0.66666667]
 [ 1.08333333  0.33333333 -0.41666667]]
矩阵A的最大值:  6
矩阵A各元素之和为:  21

绘图数据(Plotting Data)

import numpy as np
import math
import matplotlib.pyplot as plt

t = np.arange(0, 0.98, 0.01)

y1 = np.sin(2*math.pi*4*t)
y2 = np.cos(2*math.pi*4*t)

plt.plot(t, y1)
plt.plot(t, y2)

plt.xlabel('time')                  # 横坐标
plt.ylabel('value')                 # 纵坐标
plt.legend(['sin', 'cos'])          # 标注名称
plt.title('myplot')                 # 标题

plt.show()

       运行结果

吴恩达机器学习笔记---Octave教程(Python实现)_第1张图片

import numpy as np
import math
import matplotlib.pyplot as plt

t = np.arange(0, 0.98, 0.01)

y1 = np.sin(2*math.pi*4*t)
y2 = np.cos(2*math.pi*4*t)

plt.subplot(1, 2, 1)
plt.plot(t, y1, 'r')
plt.title('sin')

plt.subplot(1, 2, 2)
plt.plot(t, y2, 'b')
plt.title('cos')

plt.show()

       运行结果
吴恩达机器学习笔记---Octave教程(Python实现)_第2张图片

控制语句:for,while,if语句

  • for,while,if语句以最简单的例子展示用法
# if语句判断成绩
score = 79
if 90 < score <= 100:
    grade = '优秀'
elif 80 < score <= 90:
    grade = '良好'
elif 60 < score <= 80:
    grade = '一般'
else:
    grade = '不及格'

print(grade)

# for语句求1到100的和
sum = 0
for i in range(101):
    sum += i
print(sum)

# while语句求1到100的和
sum = 0
i = 1
while(i<101):
    sum += i
    i += 1
print(sum)

       运行结果

一般
5050
5050

       最后补充一下Python的函数定义,以代价函数为例:

import numpy as np

def CostFunction(X, y, theta):
    m = X.shape[0]
    predictions = X*theta
    cost = predictions - y
    sqrErrors = [[cost[i][j] ** 2 for j in range(len(cost[i]))] for i in range(len(cost))]

    J = 1/(2*m)*np.sum(sqrErrors)
    return J


# 给出例子
X = np.mat([[1, 1], [1, 2], [1, 3]])
y = np.mat([[1], [2], [3]])
theta = np.mat([[0], [0]])

print("代价函数的结果的为 ", CostFunction(X, y, theta))

       运行结果

代价函数的结果的为  2.333333333333333

你可能感兴趣的:(机器学习,机器学习,python,numpy)