|
|
|
|
@ -14,6 +14,7 @@
|
|
|
|
|
|
|
|
|
|
## 1.
|
|
|
|
|
|
|
|
|
|
```py
|
|
|
|
|
>>> def delay_signal (signal, T):
|
|
|
|
|
... """"Расчёт выхода y(t) для устройства задержки"""
|
|
|
|
|
... output=[]
|
|
|
|
|
@ -28,9 +29,10 @@
|
|
|
|
|
>>> y= delay_signal (x, 2)
|
|
|
|
|
>>> y
|
|
|
|
|
[0, 0, 1, 0.5, 3.6, 4.5, 1]
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
## 2.
|
|
|
|
|
|
|
|
|
|
```py
|
|
|
|
|
>>> import random
|
|
|
|
|
>>> import matplotlib.pyplot as plt
|
|
|
|
|
>>> def histogram (sample, number):
|
|
|
|
|
@ -67,15 +69,15 @@
|
|
|
|
|
-11.561019750784087 - 0.4122951289244803 | 2
|
|
|
|
|
0.4122951289244803 - 12.385610008633048 | 5
|
|
|
|
|
[3, 2, 5]
|
|
|
|
|
|
|
|
|
|
```
|
|
|
|
|

|
|
|
|
|
|
|
|
|
|
## 3.
|
|
|
|
|
|
|
|
|
|
```py
|
|
|
|
|
>>> linear_regression = lambda b1, b2, x: b1+b2 * x
|
|
|
|
|
>>> result = linear_regression (2,3,5)
|
|
|
|
|
>>> result
|
|
|
|
|
17
|
|
|
|
|
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|