hristo-vrigazov

import math
import statistics
import copy

centroids = [(2, 2), (-2, -2)]
data_points = [ \
(-1.88, 2.05), \
(-0.71, 0.42), \
(2.41, -0.67), \
(1.85, -3.80), \

hristo-vrigazov

import asyncio, evdev

from mouse_handlers import left_mouse_down, left_mouse_up
from mouse_handlers import right_mouse_down, right_mouse_up
from evdev import ecodes

mouse_file = '/dev/input/event6'
keyboard_file = '/dev/input/event9'

down = 1

hristo-vrigazov

% Hidden Markov Model example

% The weather can be in state "sun" or "rain" initially
% with 0.5 probabilities for each state
0.5::start(weather, sun); 
0.5::start(weather, rain).

% In a given moment in time, if today is sunny - "sun",
% tommorrow will be 0.6 sun or 0.4 rain
0.6::trans(weather, Moment_in_time, sun, sun); 

hristo-vrigazov

import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np

# Read in the image and print out some stats
# Note: in the previous example we were reading a .jpg 
# Here we read a .png and convert to 0,255 bytescale
image = (mpimg.imread('test.png')*255).astype('uint8')
print('This image is: ',type(image), 
         'with dimensions:', image.shape)

hristo-vrigazov

def longestPalindrome(self, s):
    """
    :type s: str
    :rtype: str
    """
    best_palindrome_length = 1
    best_palindrome = s[0]

    n = len(s)
    for i in range(2 * n - 1):

hristo-vrigazov

# Load pickled data
import pickle
import os.path
import os
import wget
import cv2
import zipfile
import numpy as np

hristo-vrigazov

package config;


import java.io.File;
import java.io.IOException;

import org.junit.Test;
import org.openqa.selenium.WebDriver;
import org.openqa.selenium.chrome.ChromeDriver;
import org.openqa.selenium.chrome.ChromeDriverService;

hristo-vrigazov

0.9::attribute_value(brown, brand).
0.9::attribute_value(brown, color).
0.9::attribute_value(tv, noun).
0.9::attribute_value(dress, clothing).

member(X, [Y|T]) :- X = Y; member(X, T).

item_prop(1, brown, brand).
item_prop(1, tv, noun).

hristo-vrigazov

import sys

signs_map = {
    '>': 1,
    '<': -1,
    '_': 0
}

reverse_signs_map = {
    0: '_',

hristo-vrigazov

import tensorflow as tf


def tf_repeat(tensor, repeats, axis):
    shape = tf.shape(tensor)
    axis_range = tf.range(shape.shape[0].value)
    ones = tf.ones_like(axis_range)
    repeats_tiled = tf.fill(tf.shape(axis_range), repeats)
    axis_tiled = tf.fill(tf.shape(axis_range), axis)
    mask = tf.equal(axis_range, axis_tiled)