test

[TOC]

创建矩阵（采用ndarray对象）

# 创建一维的narray对象
a = np.array([1,2,3])
print(a)  # [1 2 3]

# 创建二维的narray对象
a = np.array([
    [1,2,3],
    [4,5,6]
])
print(a)  # [[1 2 3] [4 5 6]]

# 创建多维对象以其类推

a = np.array([1,2,3,4,5,6])
print(a)  # [1 2 3 4 5 6]
b = a.reshape((2,3))
print(b)
'''
[[1 2 3]
 [4 5 6]]
'''

获取矩阵行数列数（二维情况）

a = np.array([
    [1,2,3],
    [4,5,6]
])
print(a.shape)  # (2, 3)
print(a.shape[0])  # 2
print(a.shape[1])  # 3

矩阵的截取

按行列截取

a = np.array([
    [1,2,3,4,5],
    [6,7,8,9,10]
])
print(a[0:1])  # [[1 2 3 4 5]]
print(a[1,2:4])  # [8 9]
print(a[1,:])  # [ 6  7  8  9 10]
print(a[1:,2:])  # [[ 8  9 10]]

按条件截取

a = np.array([
    [1,2,3,4,5],
    [6,7,8,9,10]
])
b = a[a%2==0]
print(b)  # [ 2  4  6  8 10]

a[a>6] = 0
print(a)  # [[1 2 3 4 5] [6 0 0 0 0]]

矩阵的合并

a1 = np.array([
    [1,2],
    [3,4]
])
a2 = np.array([
    [5,6],
    [7,8]
])

a = np.hstack([a1, a2])
print(a)
'''
[[1 2 5 6]
 [3 4 7 8]]
'''

a = np.vstack([a1, a2])
print(a)
'''
[[1 2]
 [3 4]
 [5 6]
 [7 8]]
'''

通过函数创建矩阵

arange

a = np.arange(5)
print(a)  # [0 1 2 3 4]

a = np.arange(0, 5)
print(a)  # [0 1 2 3 4]

a = np.arange(0, 10, 2)
print(a)  # [0 2 4 6 8]

linspace等差数列

a = np.linspace(0, 1, 7)
print(a)  # [0.         0.16666667 0.33333333 0.5        0.66666667 0.83333333 1.        ]

logspace等比数列

a = np.logspace(0, 4, 5)
print(a)  # [1.e+00 1.e+01 1.e+02 1.e+03 1.e+04]

ones、zeros、eye、empty

a_ones = np.ones((3,4))
print(a_ones)
'''
[[1. 1. 1. 1.]
 [1. 1. 1. 1.]
 [1. 1. 1. 1.]]
'''

a_zeros = np.zeros((3,4))
print(a_zeros)
'''
[[0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]
'''

a_eye = np.eye(3)
print(a_eye)
'''
[[1. 0. 0.]
 [0. 1. 0.]
 [0. 0. 1.]]
'''

a_empty = np.empty((3,4))
print(a_empty)

矩阵的运算

矩阵的点乘

a1 = np.array([
    [1,2,3,4,5],
    [6,7,8,9,10],
])
a2 = np.array([
    [1,2,3],
    [4,5,6],
    [7,8,9],
    [10,11,12],
    [13,14,15]
])
a = a1.dot(a2)
print(a)
'''
[[135 150 165]
 [310 350 390]]
'''

矩阵的转置

a = np.array([
    [1,2,3],
    [4,5,6]
])

b = a.transpose()
print(b)
'''
[[1 4]
 [2 5]
 [3 6]]
'''

矩阵的逆

import numpy.linalg as lg

a = np.array([
    [1,2,3],
    [4,5,6],
    [7,8,9]
])

b = lg.inv(a)
print(b)
'''
[[-4.50359963e+15  9.00719925e+15 -4.50359963e+15]
 [ 9.00719925e+15 -1.80143985e+16  9.00719925e+15]
 [-4.50359963e+15  9.00719925e+15 -4.50359963e+15]]
'''

矩阵信息获取

最大最小值

a = np.array([
    [1,2,3],
    [4,5,6]
])
print(a.max())  # 6
print(a.max(axis=0))  # [4 5 6]
print(a.max(axis=1))  # [3 6]
print(a.min())  # 1
print(a.min(axis=0))  # [1 2 3]
print(a.min(axis=1))  # [1 4]

平均值mean()

a = np.array([
    [1,2,3],
    [4,5,6]
])
print(a.mean())  # 3.5
print(a.mean(axis=0))  # [2.5 3.5 4.5]
print(a.mean(axis=1))  # [2. 5.]

方差var()

a = np.array([
    [1,2,3],
    [4,5,6]
])
print(a.var())  # 2.9166666666666665
print(a.var(axis=0))  # [2.25 2.25 2.25]
print(a.var(axis=1))  # [0.66666667 0.66666667]

标准差std()

a = np.array([
    [1,2,3],
    [4,5,6]
])
print(a.std())  # 1.707825127659933
print(a.std(axis=0))  # [1.5 1.5 1.5]
print(a.std(axis=1))  # [0.81649658 0.81649658]

中值median()

a = np.array([
    [1,2,3],
    [4,5,6]
])
print(np.median(a))  # 3.5
print(np.median(a, axis=0))  # [2.5 3.5 4.5]
print(np.median(a, axis=1))  # [2. 5.]

求和sum()

a = np.array([
    [1,2,3],
    [4,5,6]
])
print(a.sum())  # 21
print(a.sum(axis=0))  # [5 7 9]
print(a.sum(axis=1))  # [ 6 15]

累积和cumsum()

a = np.array([
    [1,2,3],
    [4,5,6]
])
print(a.cumsum())  # [ 1  3  6 10 15 21]
print(a.cumsum(axis=0))  # [[1 2 3] [5 7 9]]
print(a.cumsum(axis=1))  # [[ 1  3  6] [ 4  9 15]]