[PYTHON] Numpy basic calculation memo

A memo of the one often used in Python's numerical calculation library, Numpy. It is often imported under the name np. Will be updated from time to time.

import numpy as np

array Make a line.

>>> np.array([1, 2, 3])
array([1, 2, 3])
>>> np.array([[1, 2], [3, 4]])
array([[1, 2],
       [3, 4]])

T Make a transposed matrix.

>>> a = np.array([[1, 3], [2, 1]])
>>> a
array([[1, 3],
       [2, 1]])
>>> a.T
array([[1, 2],
       [3, 1]])

zeros, ones zeros is 0 and ones is filled with 1 to produce a matrix of the specified form.

>>> np.zeros(3)
array([ 0.,  0.,  0.])
>>> np.zeros([3, 3])
array([[ 0.,  0.,  0.],
       [ 0.,  0.,  0.],
       [ 0.,  0.,  0.]])
>>> np.ones(3)
array([ 1.,  1.,  1.])
>>> np.ones([3, 3])
array([[ 1.,  1.,  1.],
       [ 1.,  1.,  1.],
       [ 1.,  1.,  1.]])

eye Make an identity matrix.

>>> np.eye(3)
array([[ 1.,  0.,  0.],
       [ 0.,  1.,  0.],
       [ 0.,  0.,  1.]])

diag Make a diagonal matrix.

>>> np.diag([1, 2, 3])
array([[1, 0, 0],
       [0, 2, 0],
       [0, 0, 3]])

vstack, hstack Used for matrix composition. The usage is as follows.

>>> a = np.array([1, 1, 1])
>>> b = np.array([2, 2, 2])
>>> np.vstack([a, b])
array([[1, 1, 1],
       [2, 2, 2]])
>>> np.hstack([a, b])
array([1, 1, 1, 2, 2, 2])

dot Calculate the inner product.

>>> np.dot([1, 2, 3], [1, 2, 3])
14

cross Calculate the cross product.

>>> np.cross([0, 1], [1, 0])
array(-1)

flatten Change to a one-dimensional array

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

random.rand Generate a matrix of random numbers.

>>> np.random.rand(3)
array([ 0.37043199,  0.67058649,  0.53891633])
>>> np.random.rand(2, 3)
array([[ 0.50614319,  0.04483549,  0.39542568],
       [ 0.04853891,  0.55439793,  0.81737454]])