人工智能学习笔记——科学计算库Numpy
import numpy
创建向量：vector = numpy.array([1,2,3,4])
创建矩阵：matrix = numpy.array([[1,2,3],[4,5,6]])
打印维度（行列数）:print(vector.shape)
print(matrix.shape)：(2, 3)
打印类型：print(marix.dtype)：int64
读取文本：world_alchohol = numpy.genfromtxt("world_alcohol.txt",delimiter=",",dtype =str,skip_header = 1 )
delimiter:分隔符
dtype：传入的类型
skip_header = 1：跳过第一行
索引取值：value = world_alchohol[1,4]#第二行第五列
allValue = world_alchohol[:,4]#所有行的第五列
print(vector[0:3])
print(marix[:,0:3])
print(marix[0:2,0:3])
vector = numpy.array([1,2,3,4,5])
vector == 5
array([False, False, False, False, True])
equalFive = (vector == 5)
print(vector[equalFive])
[5]
marix = numpy.array([
[1,2,3],
[4,5,6],
[7,8,9]
])
marix == 5
array([[False, False, False],
[False, True, False],
[False, False, False]])
&与 |或
类型转换：vector = numpy.array(["1","2","3"])
print(vector.dtype)
vector = vector.astype(float)
print(vector.dtype)
最值：vector = numpy.array([1,2,3,4,5])
minValue = vector.min()
maxValue = vector.max()
print(minValue)
print(maxValue)1 5行列求和：marix = numpy.array([
[1,2,3],
[4,5,6],
])
[7,8,9]
columnSum = marix.sum(axis=0)#列求和
rawSum = marix.sum(axis=1)#行求和
print(columnSum)
print(rawSum)
[12 15 18] [ 6 15 24]import numpy as np
构造数列：print(np.arange(15))
[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14]构造切片数列：np.arange(2,10,2)
array([2, 4, 6, 8])构造矩阵：marix = np.arange(15).reshape(3,5)
print(marix)
[[ 0 1 2 3 4] [ 5 6 7 8 9] [10 11 12 13 14]]矩阵的维度（二维、三维）：marix.ndim
矩阵元素的个数：marix.size
构造零矩阵：np.zeros((3,4))
array([[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]])构造单位矩阵：np.ones((2,3,4),dtype=np.int16)
array([[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]], dtype=int16)构造随机数矩阵：np.random.random((2,3))
array([[0.36243571, 0.10834852, 0.50913561], [0.19129293, 0.01765437, 0.18629496]])平均切片：from numpy import pi
np.linspace(0,2*pi,10)
array([[0.36243571, 0.10834852, 0.50913561], [0.19129293, 0.01765437, 0.18629496]])矩阵内积（对应相乘）：A = np.array([[1,2],[3,4]])
B = np.array([[5,6],[7,8]])
print(A*B)
[[ 5 12] [21 32]]矩阵外积：A = np.array([[1,2],[3,4]])
B = np.array([[5,6],[7,8]])
print(A.dot(B))
print(np.dot(A,B))
[[19 22] [43 50]] [[19 22] [43 50]]数学运算：np.exp()
np.sqrt()
向下取整：print(np.floor(1.01))
向上取整：print(np.ceil(1.01))
向零取整：print(np.fix(1.8))
矩阵转向量：print(marix)
print(marix.ravel())[[ 0 1 2 3 4] [ 5 6 7 8 9] [10 11 12 13 14]] [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14]垂直、水平拼接矩阵：print(np.vstack((A,B)))
print(np.hstack((A,B)))
[[1 2] [3 4] [5 6] [7 8]] [[1 2 5 6] [3 4 7 8]]矩阵水平平均切割：marix = np.arange(24).reshape(2,12)
print(marix)
print(np.hsplit(marix,2))
[[ 0 1 2 3 4 5 6 7 8 9 10 11] [12 13 14 15 16 17 18 19 20 21 22 23]] [array([[ 0, 1, 2, 3, 4, 5], [12, 13, 14, 15, 16, 17]]),array([[ 6, 7, 8, 9, 10, 11], [18, 19, 20, 21, 22, 23]])]矩阵水平任意切割：print(np.hsplit(marix,(3,5)))
[array([[ 0, 1, 2], [12, 13, 14]]),array([[ 3, 4], [15, 16]]),array([[ 5, 6, 7, 8, 9, 10, 11], [17, 18, 19, 20, 21, 22, 23]])]矩阵垂直平均切割：print(np.vsplit(marix,2))
[array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]]), array([[12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]])]复制：
a = np.array([1,2,3,4])
b = a.copy()#复制初始化值
print(b is a)
b[0] = 10
print(a)
print(b)False [1 2 3 4] [10 2 3 4]a = np.array([1,2,3,4])
b = a
print(b is a)
b[0] = 10
print(a)
print(b)
True [10 2 3 4] [10 2 3 4]矩阵行（列）的最大值索引：
data = np.sin(np.arange(20)).reshape(5,4)
print(data)
ind = data.argmax(axis = 0)#axis = 0列axis = 1行
print(ind)
data_max = data[ind,range(data.shape[1])]#data.shape[1]列数
print(data_max)[[ 0. 0.84147098 0.90929743 0.14112001] [-0.7568025 -0.95892427 -0.2794155 0.6569866 ] [ 0.98935825 0.41211849 -0.54402111 -0.99999021] [-0.53657292 0.42016704 0.99060736 0.65028784] [-0.28790332 -0.96139749 -0.75098725 0.14987721]] [2 0 3 1] [0.98935825 0.84147098 0.99060736 0.6569866 ]矩阵复制扩展：
a = np.arange(0,40,10)
print(a)
b = np.tile(a,(3,2))
print(b)[ 0 10 20 30] [[ 0 10 20 30 0 10 20 30] [ 0 10 20 30 0 10 20 30] [ 0 10 20 30 0 10 20 30]]排序：
marix = np.array([[1,4,3],[4,2,9],[7,6,8]])
print(marix)
print(np.sort(marix,axis=1))#行排序[[1 4 3] [4 2 9] [7 6 8]] [[1 3 4] [2 4 9] [6 7 8]]