Machinelearning tests

import numpy as np
Polynomial = np.polynomial.Polynomial
p = Polynomial([1, -1, 1])
q = Polynomial([2, -3])
print(int((p + q)(1)))

import re
s = "<span>1</span><b>2</b>"
p1 = re.compile(r"(<\w+>.*</\w+>)")
p2 = re.compile(r"(<\w+>.*?</\w+>)")
x1 = p1.findall(s)
x2 = p2.findall(s)
print(len(x1), len(x2))

from itertools import repeat, chain
class A:
    def __init__(self, x):
        self.x = x
    def __reversed__(self):
        return chain(reversed(self.x), repeat(0, 2))
a = A([1, 2])
print(*reversed(a))

class X:
    def __init__(self, l):
        self.l = l
    def __iter__(self):
        return (x for x in self.l)
a = X([1, 2])
b = X([3, 4])
print(*chain(a, a, b, b))

from itertools import islice
g = (i**2 for i in range(1, 4))
print(*g[1:3])  # 1
print(*islice(g, 1, 3))  # 2
print(*slice(g, 1, 3))  # 3
print(*g.slice(1, 3))  # 4

from itertools import zip_longest
print(*[*zip_longest([1], [3, 4])])

import numpy as np
basket = np.array([[1, 1, 1, 1], [1, 1, 1, 0]])
x = np.all(basket[:,2:], axis = 1)
print(*x)

from fractions import Fraction
a = Fraction(2, 3)
b = Fraction(1, 2)
print(a - b)

x= -7 // 3 == -(7 // 3)
y = -7 // -3 == 7 // 3
print(x, y)

from sklearn.cluster import KMeans
import numpy as np
x = np.array([[35, 7000], [45, 6900], [70, 7100], [20, 2000], [25, 2200], [15, 1800]])
kmeans = KMeans(n_clusters=2).fit(x)
y = kmeans.cluster_centers_
print(y)