YuriyGuts

#!/usr/bin/env python

import os
import subprocess
import sys


confirmation = raw_input("This script may DISCARD YOUR UNCOMMITTED CHANGES. Are you sure (Y/N)? ")
if confirmation.lower() != "y":
    sys.exit(1)

YuriyGuts

#!/usr/bin/env bash

GATEWAY=gateway.company.com
DOMAIN=company.local
RESOLUTION=1920x1080

if [ -z "$1" ]; then
    read -p "Computer name (e.g. CP1234): " COMPUTERNAME
else
    COMPUTERNAME=$1

YuriyGuts

// Precalculate levels according to separator characters.
var nodes = flattenedTree.Select(flatNode => new {
        Value = flatNode.Value,
        Level = flatNode.Value.Count(c => c == '.'),
        IsLeaf = false
    }).ToArray();

// Find leaves by comparing the levels of each two adjacent items.
for (int i = 1; i < nodes.Length; i++)
{

YuriyGuts

private static void PopulateNodeWithRandomSubtree(TreeNode nodeToPopulate, int currentLevel)
{
    // These can be the arguments of the function if necessary.
    const int maxTreeDepth = 5;
    const int maxSiblingCount = 4;

    // Generate children for this node if a fair coin toss yields 0 at least QUOTA times,
    // where QUOTA increases after each level. That way the deeper it gets, the less will
    // be the probability of generating more children.

YuriyGuts

static class Program
{
    static void Main(string[] args)
    {
        var indices = Enumerable.Range(0, 26);
        var alphabet = indices.Select(i => (char)('A' + i));

        WriteHeader("WhereMonadic");
        foreach (var item in indices.WhereMonadic(item => item % 2 == 0))
        {

YuriyGuts

<#

.SYNOPSIS
Dumps the entire file tree of the specified folder(s) and saves the results to a ZIP archive.

.PARAMETER SourceFolderList
Path to the folder(s) to retrieve the file tree for. Multiple folders should be separated by the pipe character ("|").

.PARAMETER BackupStorageList
Path to the folder(s) where the ZIP file will be stored. Multiple folders should be separated by the pipe character ("|").

YuriyGuts

# Predict the survival of RMS Titanic passengers using logistic regression.
# Based on Kaggle Titanic dataset: https://www.kaggle.com/c/titanic/data
#
# You might need to install Amelia and ROCR packages.


cleanData <- function(rawData) {
    # Uncomment these two lines to visualize the missing data.
    # library(Amelia)
    # missmap(trainingData, main="Missing vs. observed values")

YuriyGuts

#!/usr/bin/env bash

fetch() {
    echo "Removing brew cache"
    local cache=$(brew --cache) 
    rm -rf "$(brew --cache)"
    mkdir "$cache"
    mkdir "$cache/Casks"
    echo "Running brew update" 
    brew update 

YuriyGuts

CartPole Q-Learning Agent

Solves the OpenAI CartPole-v0 environment using a tabular version of Q-Learning with discretized feature space and epsilon-decreasing exploration.

View Repository

YuriyGuts

#!/usr/bin/env python
"""
Update all Git repos in the current directory. Fast-forward only, no merge commits.

Copyright (c) 2018 Yuriy Guts

usage: git-update-all.py
"""

from __future__ import division, print_function