Machinelearning tests

import statistics as stats
import math

data = [1, 2, 2, float('nan'), 3, 4]

print("Mean:", stats.mean(data))
print("Median:", stats.median(data))
print("Mode:", stats.mode(data))

Median: 2.5
Mode: 2

Median: 2.5
Mode: 2

Median: nan
Mode: 2

((lambda x: (lambda y:  x ** y)) (3)) (2)

import sys
f = lambda x: list(map(sys.stdout.write, x)) 
t = f(['1', '2', '3'])

def f(a: int = '1', b: int = '2', c: int = '3') -> int:
    return a + b + c
print(f())

def f(arr):
    return arr[0] if len(arr) == 1 else arr[0] + f(arr[1:])
print(f(('a', 'b', 'c', 'd')))

class A:
    def __init__(self, x):
        self.x = x
    def __call__(self):
        return self.x
class B:
    def __init__(self, x):
        self.x = x
    def method(self):
        return self.x
class C:
    def __init__(self, x=3):
        self.x = x
    def __repr__(self):
        return str(self.x)

a = A(1)
b = B(2)
c = C(3)
callables = # ваш код
print([act() for act in callables])

#Вывод:     [1, 2, 3]

class A:
    def __init__(self, value): 
        self.value = value
    def __call__(self, other):
        return self.value ** other
x = A(2) 
print(x(3))

a = [1, 2, 3, 3]
b = {1, 2, 3, 3}
print(set(a) == b, list(b) == a)

import numpy as np
x = np.array([1, 2, 3, 4])
print(np.average(x, weights=[2, 1, 0, 5]))

import numpy as np
data = np.array([2, 7, 3, 1])
bins = [0, 5, 10]
hist, _ = np.histogram(data, bins)
print(*hist)
#data - набор данных
#bins - набор интервалов, задаваемых граничными значениями