YuriyGuts

// This is an example of a C# application that uploads a CSV dataset to DataRobot,
// runs a classification project in autopilot mode and prints the leaderboard
// using the V2 REST API. Here you can also see an example of interacting with the
// asynchronous API routes (upload project, set target).

// Since the WebClient class has limited HTTP functionality, we use the newer
// Microsoft.Net.Http.HttpClient class. Its methods are async; as a result,
// the C# code uses asynchronous operations too.

// You may need to install the following packages for this code to work:

YuriyGuts

"""
Converts subtitles in a video from any format to plain text using ffmpeg.

Reads all files that match a glob pattern.
Writes output to current directory unless specified otherwise.

Usage: convert-subtitles.py <input_pattern> [output_folder]
Example: convert-subtitles.py ~/Videos/*.mkv
"""
import glob

YuriyGuts

#!/usr/bin/env bash

# Install or upgrade any JetBrains product on Linux, assuming it is distributed as a tarball
# and has the entry point at $INSTALL_DIR/bin/$PRODUCT.sh
# Tested on IDEA, PyCharm, CLion, DataGrip 2016-2018.*

# Usage:
#    install-jetbrains-product.sh [ProductName] [DownloadURL]
#
# Example:

YuriyGuts

#!/usr/bin/bash
# ----------------------
# 00-common-vars.sh
# ----------------------

# =========== CONFIG =============
# Assuming we'll set the Dropbox folder to ~/Dropbox
DROPBOX_FOLDER=$HOME/Dropbox
# ================================

YuriyGuts

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import pandas as pd


input_filename = 'https://raw.githubusercontent.com/devua/csv/master/salaries/2018_june_final.csv'
output_filename = '2018_june_final_eng.csv'

df = pd.read_csv(input_filename)

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

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 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

# 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

<#

.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 ("|").