wmalarski

from __future__ import division
import random

l = [0, 1, 2]


def generate_choice():
    choice = random.choice(l)
    cross = random.choice(l)
    show = random.choice(list(set(l) - {choice, cross}))

wmalarski

from __future__ import division
import math


def func(x):
    return 50 * math.sin(x)

x_args = [(2 * x * math.pi)/50 for x in range(51)]
y_args = [func(x) for x in x_args]
y_min, y_max = min(y_args), max(y_args)

wmalarski

clc; clear;
%% train
data_train = load('Lab2_training.txt');
x_train = data_train(:, 1:4)';
y_train = data_train(:, 5:7)';
%% test
data_test = load('Lab2_testing.txt');
x_test = data_test(:, 1:4)';
y_test = data_test(:, 5:7)';

wmalarski

from __future__ import division
import numpy as np
import math

n = 100000


def probability(dim):
    sums = (np.random.uniform(-1, 1, (n, dim)) ** 2).sum(axis=1)
    return ((sums < 1).real.sum() / n) * (2.0 ** dim)

wmalarski

import numpy as np
import matplotlib.pyplot as plt

t = np.loadtxt('f2.txt')


def random_result(dim):
    res = np.zeros(t.size, dtype=np.int8)
    y = np.random.randint(0, dim[0], dim[1])
    x = np.arange(0, dim[1], dtype=np.int8)

wmalarski

from __future__ import division
import numpy as np
import matplotlib.pyplot as plt


if __name__ == "__main__":
    n = 10**6
    x_pos = np.zeros(n)
    y_pos = np.zeros(n)

wmalarski

import numpy as np
import matplotlib.pyplot as plt

_gamma1d25 = 1.1330030963193471
_gamma2d5 = 3.323350970447843
_beta = 1.5
_l = (_gamma2d5 * np.sin(np.pi * _beta / 2.0))
_m = _gamma1d25 * _beta * pow(2.0, (_beta - 1.0) / 2.0)
_omega = np.power(_l / _m, 1 / _beta)

wmalarski

from __future__ import division
from visual import *
import numpy as np

radS = 0.4
radH = 0.4
length = 100.
k = 0.5
dt = 0.1
n_balls = 52

wmalarski

from __future__ import division
import tweepy
import configparser

config = configparser.ConfigParser()
config.read('config.ini')

access_token_secret = config['TWEEPY']['access_token_secret']
consumer_secret = config['TWEEPY']['consumer_secret']
consumer_key = config['TWEEPY']['consumer_key']

wmalarski

from collections import defaultdict


class Context(object):

    def __init__(self):
        self._classes = {}
        self._instances = {}

    def feature(self, component_cls):