人工智能简介

1、Python基础

环境搭建

Anaconda
使用Anaconda Prompt:
conda list 列表
conda install pandas 安装库
conda update pandas 更新库

代码编辑器 Jupyter Notebook
打印命令：

days = '365'
print(days)
print(type(days))

转换
int_days = int(days)

指数乘法
print(4**3) # 64

List

months = []
months.append("January")
months.append("February")
print(months)
print(len(months))
print(months[-1])
# 结果 ['January', 'February']
print(tex[2:6])  # 包含索引2，但是不包含第6个数值
print(tex[2:])    #从索引2到最后

循环语句

cities = ["Austin","Dallas","Houston"]
for city in cities:
    print(city)

i=0
while i<3:
    i += 1
    print(i)

for i in range(10):
    print(i)

布尔类型

cat = True
dog = False
print(type(cat))

查询

animals = ["cat","dog","rabbit"]
for animal in animals:
    if animal == "cat":
        print("Cat found")

animals = ["cat","dog","rabbit"]
if "cat" in animals:
    print("Cat found")

animals = ["cat","dog","rabbit"]
cat_found = "cat" in animals
print(cat_found)

字典

scores = {}
scores["Jim"] = 80
scores["Sue"] = 85
scores["Ann"] = 75

print(type(scores))
print(scores.keys())
print(scores["Sue"])
print("Jim" in scores) # key 是否在字典中
#  <class 'dict'>
#  dict_keys(['Jim', 'Sue', 'Ann'])
#  85

使用字典计数

pantry = ["apple","orange","grape","apple","orange","apple","tomato","potato","grape"]
pantry_counts = {}

for item in pantry:
    if item in pantry_counts:
        pantry_counts[item] = pantry_counts[item]+1
    else:
        pantry_counts[item] = 1
print(pantry_counts)
#  {'apple': 3, 'orange': 2, 'grape': 2, 'tomato': 1, 'potato': 1}

Numpy

import numpy
vector = numpy.array([5,10,15,20])
matrix = numpy.array([[5,10,15],[20,25,30],[35,40,45]])
print(vector)
print(matrix)
# 行向量,矩阵操作定义

vector = numpy.array([5,10,15,20])
matrix = numpy.array([[5,10,15],[20,25,30],[35,40,45]])
print(vector.shape)
print(matrix.shape)
# 对象属性几行几列

vector = numpy.array([5,10,15,20.3])
print(vector)
print(vector.dtype)
#  [ 5.  10.  15.  20.3]
#  float64
# 数据类型

vector = numpy.array([5,10,15,20.3])
print(vector[0:3])
# 取三个值，从索引0开始

import numpy
matrix = numpy.array([[5,10,15],[20,25,30],[35,40,45]])
print(matrix[:,1])
# 取所有矩阵中的第二列  [10 25 40]
print(matrix[:,0:2])
"""
所有样板中前两列
[[ 5 10]
 [20 25]
 [35 40]]
"""

判断

import numpy
vector = numpy.array([5,10,15,20])
vector == 10
"""
array([False,  True, False, False])
"""

matrix = numpy.array([
    [5,10,15],
    [20,25,30],
    [35,40,45]
])
matrix == 25
"""
array([[False, False, False],
       [False,  True, False],
       [False, False, False]])
"""

vector = numpy.array([5,10,10  ,15,20])
equal_to_ten = (vector == 10)
print(equal_to_ten)
print(vector[equal_to_ten])
"""
[False  True False False]
[10 10]
"""

matrix = numpy.array([
    [5,10,15],
    [20,25,30],
    [35,40,45]
])
second_column_25 = (matrix[:,1] == 25)
print(second_column_25)
print(matrix[second_column_25,:])
"""
[False  True False]
[[20 25 30]]
"""

vector = numpy.array([5,10,15,20])
equal_to_ten_and_five = (vector==10)&(vector==5)
print(equal_to_ten_and_five)
"""
[False False False False]
"""

判断并赋值

vector = numpy.array([5,10,15,20])
equal_to_ten_or_five = (vector==10)|(vector==5)
print(equal_to_ten_or_five)
vector[equal_to_ten_or_five] = 50
print(vector)
"""
[ True  True False False]
[50 50 15 20]
"""

matrix = numpy.array([
    [5,10,15],
    [20,25,30],
    [35,40,45]
])
second_column_25 = matrix[:,1]==25
print(second_column_25)
matrix[second_column_25,1]=10
print(matrix)
"""
[False  True False]
[[ 5 10 15]
 [20 10 30]
 [35 40 45]]
"""

类型转化

vector = numpy.array(["1","2","3"])
print(vector.dtype)
print(vector)
vector=vector.astype(float)
print(vector.dtype)
print(vector)
"""
<U1
['1' '2' '3']
float64
[1. 2. 3.]
"""

最小值

vector = numpy.array([5,10,15,20])
vector.min()
# 5

按行求值

matrix = numpy.array([
    [5,10,15],
    [20,25,30],
    [35,40,45]
])
matrix.sum(axis=1)
# array([ 30,  75, 120])

按列求和

matrix = numpy.array([
    [5,10,15],
    [20,25,30],
    [35,40,45]
])
matrix.sum(axis=0)
"""
array([60, 75, 90])
"""