Machine Learning Basic Library Learning

foreword

I have worked as a python crawler for nearly a year before, and I still have some basic python language. At present, machine learning is in full swing. In the project, machine learning is used to classify the collected content. I will learn the relevant libraries in my spare time, and look forward to money++

Introduction

Numpy
python scientific computing base library
matplotlib
Matplotlib is a Python 2D plotting library that produces publication-quality graphics in a variety of hardcopy formats and a cross-platform interactive environment
pandas
python data analysis library

NumPy basic knowledge points

Array output settings

set_printoptions(precision=None, threshold=None, edgeitems=None, linewidth=None, suppress=None, nanstr=None, infstr=None, formatter=None)

precisionOutput floating point precision setting, default is 8
thresholdThe threshold for triggering the output summary, which can be set to np.inf to print out all

slice source

The python sequence slice address can be written as [start:end:step], where start, end and step can be omitted

range(10) =>[0,1,2,3,4,5,6,7,8,9]
When start is omitted, the default starts from item 0range(10)[:10:2] => [0,2,4,6,8]
When the end is omitted, it defaults to the end of the arrayrange(10)[1::2] => [1,3,5,7,9]
When the start and end are not omitted, step defaults to 1range(10)[2:6:] => [2,3,4,5]
Step size step=n; represents starting from start (start is also counted) every step interval, take a number until the end endrange(20)[::3] => [0,3,6,9,12,15,18]
When step is equal to a negative number, take the number from right to leftrange(10)[::-1] => [9,8,7,6,5,4,3,2,1,0]; range(10)[::-2] => [9,7,5,3,1]
a[start:end]: the range is [start,end)
a[:end]: the range is [0,end)
a[start:]: The range is from start to end (including the end)

multidimensional slice

In [1]: import numpy as np

In [2]: a = np.arange(25).reshape((5,5))

In [3]: a
Out[3]: 
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24]])

# 各个维度分别取，冒号表示取这个维度的所有

In [4]: a[:,2:5]
Out[4]: 
array([[ 2,  3,  4],
       [ 7,  8,  9],
       [12, 13, 14],
       [17, 18, 19],
       [22, 23, 24]])

In [5]: a[:, None].shape
Out[5]: (5, 1, 5)

# None代表新增加一个维度，它有一个别称叫newaxis， None放在哪一维，就会在哪一维上出现新的维度

In [6]: a[:, None]
Out[6]: 
array([[[ 0,  1,  2,  3,  4]],      
       [[ 5,  6,  7,  8,  9]],      
       [[10, 11, 12, 13, 14]],      
       [[15, 16, 17, 18, 19]],      
       [[20, 21, 22, 23, 24]]])
In [7]: a[:,:, None].shape
Out[7]: (5, 5, 1)

In [8]: a[..., None].shape
Out[8]: (5, 5, 1)

The dots (…) represent as many colons as needed to produce a complete indexing tuple. For example, if x is a rank 5 array (i.e., it has 5 axes), then

x[1,2,…] is equivalent to x[1,2,:,:,:],
x[…,3] to x[:,:,:,:,3]
x[4,…,5,:] to x[4,:,:,5,:].

>>> c = np.array( [[[  0,  1,  2],               # a 3D array (two stacked 2D arrays)
...                 [ 10, 12, 13]],
...                [[100,101,102],
...                 [110,112,113]]])
>>> c.shape
(2, 2, 3)
>>> c[1,...]                                   # same as c[1,:,:] or c[1]
array([[100, 101, 102],
       [110, 112, 113]])
>>> c[...,2]                                   # same as c[:,:,2]
array([[  2,  13],
       [102, 113]])

test installation

import matplotlib.pyplot as plt
import numpy as np

X = np.linspace(-np.pi,np.pi,256,endpoint=True)
(C,S)=np.cos(X),np.sin(X)

#这里用到了Matplotlib和numpy模块,linspace在(−π,π)之间分成共256个小段，
#并把这256个值赋予X。C,S分别是cosine和sine值（X,C,S都是numpy数组）
plt.plot(X,C)
plt.plot(X,S)

#进行显示
plt.show()