Introduction to Machine learning NumPy

From numpy import * #导入numpy所有内容到当然命名空间下jj =[] #创建一个线性表jj. Append (1) #在线性表尾部添加变量jj. Append (' hat ') #线性表的数据类型可以是不同的kk ={}  #创建一个字典kk [' Dog ']= ' you ' #字典相当于cpp中的mapkk [1]=42 #数字和字符串可以作为keya =set ([1,2,2,2,3]) #创建一个集合b =set ([2,3,4])   A-B #得到集合a有 While collection B doesn't have something a|b  # AB's a&b  # AB intersection Aa=mat ([[[],        [3,4]]) #创建一个矩阵, similar to Matlabbb=array (() # To create a vector, note that the vector group is a different print shape (AA) than the matrix. T) #得到矩阵aa的行和列的个数,  . T is transpose Matrix eye (3)            #单位矩阵print bb**3<span style= "White-space:pre" ></span>  #向量对应位相乘print aa**2       # Aa*aaprint Multiply (AA,AA) #aa对应位相乘

I'll talk about that today.

Introduction to Machine learning NumPy