catharsis96

\documentclass{article}

\begin{document}



\centerline{\sc \large A Simple Sample \LaTeX\ File}
\vspace{.5pc}
\centerline{\sc Stupid Stuff I Wish Someone Had Told Me Four Years Ago}
\centerline{\it (Read the .tex file along with this or it won't 

catharsis96

using UnityEngine;
using System.Collections;
using System.Collections.Generic;

[System.Serializable]
public class PathContainerScript : MonoBehavior {
		[SerializeField]
		public List<Waypoint> Waypoints;
		[SerializeField]
		public float Speed;

catharsis96

\documentclass{article}
\usepackage{listings}             % Include the listings-package
\begin{document}
\lstset{language=Pascal}          % Set your language (you can change the language for each code-block optionally)

\begin{lstlisting}[frame=single]  % Start your code-block
for i:=maxint to 0 do
begin
{ do nothing }
end;

catharsis96

\documentclass{article}
\usepackage{listings}             % Include the listings-package
\begin{document}
\lstset{language=Pascal}          % Set your language (you can change the language for each code-block optionally)

\begin{lstlisting}[frame=single]  % Start your code-block
for i:=maxint to 0 do
begin
{ do nothing }
end;

catharsis96

'''
This script perfoms the basic process for applying a machine learning
algorithm to a dataset using Python libraries.

The four steps are:
   1. Download a dataset (using pandas)
   2. Process the numeric data (using numpy)
   3. Train and evaluate learners (using scikit-learn)
   4. Plot and compare results (using matplotlib)

catharsis96

'''
This script perfoms the basic process for applying a machine learning
algorithm to a dataset using Python libraries.

The four steps are:
   1. Download a dataset (using pandas)
   2. Process the numeric data (using numpy)
   3. Train and evaluate learners (using scikit-learn)
   4. Plot and compare results (using matplotlib)

catharsis96

'''
This script perfoms the basic process for applying a machine learning
algorithm to a dataset using Python libraries.

The four steps are:
   1. Download a dataset (using pandas)
   2. Process the numeric data (using numpy)
   3. Train and evaluate learners (using scikit-learn)
   4. Plot and compare results (using matplotlib)

catharsis96

import java.sql.*;
import java.util.logging.*;

public class JDBCtest {

    public static void main(String[] args) {

        Connection connection = null;
        //URL к базе состоит из протокола:подпротокола://[хоста]:[порта_СУБД]/[БД] и других_сведений
        String url = "jdbc:postgresql://127.0.0.1:5432/test";

catharsis96

{
  "projects": [ "src", "test" ],
  "sdk": {
    "version": "1.0.0-preview2-003131"
  }
}

catharsis96

#include <stdio.h>

void main()
{
	char *temp = "Paras";

	int i;
	i=0;

	temp[3]='F';