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range The() function generates a starting value of start, The final value does not exceed stop, In steps of step Equal difference sequence of .range The basic calling syntax of the function is as follows ：
range(start, stop[, step])
start： The starting value of the array , Omission , The default value is 0.
stop： The upper limit of the array , Generate an arithmetic sequence that does not exceed this value .
step： step , Omission , The default value is 1, That is, the difference between the two numbers in the array .
for i in range(6): print(i)
Get the results ：
0
1
2
3
4
5
You can find range Function can omit the initial value start（ The default value is 0） And step length step（ The default value is 1）, And take the default value to generate the arithmetic sequence .
for i in range(5, 16, 2): print(i)
Get the results ：
5
7
9
11
13
15
You can find range The function generates an initial value of 5, The final value does not exceed 16（ Maximize ）, In steps of 2 Equal difference sequence of .
random.randint The function is numpy In the library , Usually you need to load first numpy library , Call this function again . The basic calling syntax of the function is as follows ：
import numpy as npnp.random.randint(low, high=None, size=None, dtype=int)
low： The number generated randomly must be greater than or equal to this value .
high： The number generated randomly should be less than this value .
size： Controls the size of random numbers , If omitted, a single integer is output by default .
random.randint The integer array() function returns a random integer number, array, or data frame .
Range from low( contain ) To high( Not included ), namely [low, high). If no parameters are written high Value , Then the data range is [0, low).
for i in range(5): print(np.random.randint(6))
Get the results ：
0
1
5
1
4
You can find random.randint If there is only one number in the function , Then a data range is generated [0, This number ) The integer of .
np.random.randint(-2, 9, (1,6))
Get the results ：
array([[ 6, 0, 6, -1, -2, 2]])
You can find random.randint Function size Values can control the dimension of data . The first number refers to the number of rows of data , The second number refers to the number of columns of data . example 2 Generate a 1 That's ok 6 Array of columns .
np.random.randint(5, 10, (3, 5))
Get the results ：
array([[6, 8, 8, 5, 8],
[6, 9, 9, 7, 9],
[9, 7, 7, 7, 8]])
You can find random.randint Function size Values can control the dimension of data . The first number refers to the number of rows of data , The second number refers to the number of columns of data . example 3 Generate a 3 That's ok 6 Column data box .
clip The function is numpy In the library , Usually you need to load first numpy library , Call this function again .clip The basic calling syntax of the function is as follows ：
import numpy as npnp.clip(a, a_min, a_max, out=None, **kwargs)
a： Array or data frame .
a_min： Lower bound , The minimum value of the interval ,a Middle ratio a_min Small numbers are forced to become a_min.
a_max： upper bound , The maximum value of the interval ,a Middle ratio a_max Large numbers are forced to become a_max.
out： You can specify the object of the output matrix ,shape And a identical .
The purpose of this function is to a All numbers in are limited to a_min and a_max In this interval , Values beyond this range are truncated and set to the limit value .
a = np.array(range(1, 10))a_min = 3a_max = 8print(a)print('======compare======')print(np.clip(a, a_min, a_max))Get the results ：
[1 2 3 4 5 6 7 8 9]
======compare======
[3 3 3 4 5 6 7 8 8]
compare The previous is the original value , The next is to use clip Function intercepts the value after . You can find clip The < span class = span class = span class = span class = span class = span class = span class = span class = span class = span class = span class = span class = span class = span class = span class = span class = span class = span class = span class a_min And greater than a_max The values of are forced to be bound values .
a = np.random.randint(20, 50, (4, 4))a_min = 30a_max = 40print(a)print('====compare====')print(np.clip(a, a_min, a_max))Get the results ：
[[40 39 35 21]
[29 44 36 46]
[47 40 40 26]
[24 24 26 44]]
====compare====
[[40 39 35 30]
[30 40 36 40]
[40 40 40 30]
[30 30 30 40]]
You can find clip The < span > function sets the data frame to less than a_min And greater than a_max The values of are forced to be bound values .
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