Java Code Examples for org.bukkit.ChatColor#RED

private String getCurrentPlayersString() {
  int maxPlayers = this.game.getMaxPlayers();
  int currentPlayers = 0;
  if (this.game.getState() == GameState.RUNNING) {
    currentPlayers = this.game.getTeamPlayers().size();
  } else if (this.game.getState() == GameState.WAITING) {
    currentPlayers = this.game.getPlayers().size();
  } else {
    currentPlayers = 0;
  }

  String current = "0";
  if (currentPlayers >= maxPlayers) {
    current = ChatColor.RED + String.valueOf(currentPlayers) + ChatColor.WHITE;
  } else {
    current = String.valueOf(currentPlayers);
  }

  return current;
}
 

@Override
public boolean execute(CommandSender sender, String currentAlias, String[] args) {
    if (!testPermission(sender)) return true;

    double tps = Math.min(20,  Math.round(net.minecraft.server.MinecraftServer.currentTps * 10) / 10.0);
    ChatColor color;
    if (tps > 19.2D) {
        color = ChatColor.GREEN;
    } else if (tps > 17.4D) {
        color = ChatColor.YELLOW;
    } else {
        color = ChatColor.RED;
    }

    sender.sendMessage(ChatColor.GOLD + "[TPS] " + color + tps);

    return true;
}
 

public FireBomb() {
	super("fire-bomb", ChatColor.RED + "Fire Bomb", Material.FIRE_CHARGE);
	setLore("§bLeft-click to throw the bomb.",
			"§bIt will explode after a few seconds.");
	setDefaultConfig("fuse", 40);
	setDefaultConfig("radius", 9);
}
 

public String learn() {

		/**
		 * Simple explanation of these steps:
		 * 
		 * 1) If it is currently learning, change the inputs to either true or false.
		 * 
		 * 2) Let the NN tick and think. This will return the outputs from the
		 * OutpuitNeurons
		 * 
		 * 3) If it is not learning, just return the answer.
		 * 
		 * 4) Else, do the logic and see if the answer it gave (thought[0]) was correct.
		 * 
		 * 5) If it was not correct, use the DeepReinforcementUtil to improve it.
		 * 
		 * 6) After inprovement, return a message with if it was correct, the accuracy,
		 * the inputs, and what it thought was the output,
		 */
		binary.changeValueAt(0, 0, ThreadLocalRandom.current().nextBoolean());
		binary.changeValueAt(0, 1, ThreadLocalRandom.current().nextBoolean());
		boolean[] thought = tickAndThink();
		boolean logic = (binary.getBooleanAt(0, 0) == binary.getBooleanAt(0, 1));
		boolean result = logic == thought[0];
		this.getAccuracy().addEntry(result);

		// IMPROVE IT
		HashMap<Neuron, Double> map = new HashMap<>();
		for (int i = 0; i < thought.length; i++)
			map.put(ai.getNeuronFromId(i), logic ? 1 : -1.0);
		if (!result)
			DeepReinforcementUtil.instantaneousReinforce(this, map, 1);
		return ((result ? ChatColor.GREEN : ChatColor.RED) + "acc " + getAccuracy().getAccuracyAsInt() + "|"
				+ binary.getBooleanAt(0, 0) + " + " + binary.getBooleanAt(0, 1) + " ~~ " + thought[0]);

	}
 

public String learn() {
	/**
	 * Simple explanation of these steps:
	 * 
	 * 1) If it is currently learning, change the inputs to either true or false.
	 * 
	 * 2) Let the NN tick and think. This will return the outputs from the OutpuitNeurons
	 * 
	 * 3) If it is not learning, just return the answer.
	 * 
	 * 4) Else, do the logic and see if the answer it gave (thought[0]) was correct.
	 * 
	 * 5) If it was not correct, use the DeepReinforcementUtil to improve it.
	 * 
	 * 6) After inprovement, return a message with if it was correct, the accuracy, the inputs, and what it thought was the output,
	 */
	binary.changeValueAt(0, 0,
			ThreadLocalRandom.current().nextBoolean());
	binary.changeValueAt(0, 1,
			ThreadLocalRandom.current().nextBoolean());
	boolean[] thought = tickAndThink();
	boolean logic = !(binary.getBooleanAt(0, 0) || binary.getBooleanAt(0, 1));
	boolean wasCorrect = (logic == thought[0]);
	this.getAccuracy().addEntry(wasCorrect);

	// IMPROVE IT
	HashMap<Neuron, Double> map = new HashMap<>();
	for (int i = 0; i < thought.length; i++)
		map.put(ai.getNeuronFromId(i), logic ? 1.0 : -1.0);
	if (!wasCorrect)
		DeepReinforcementUtil.instantaneousReinforce(this, map,1);

	return (wasCorrect ? ChatColor.GREEN : ChatColor.RED) + "acc "
			+ getAccuracy().getAccuracyAsInt() + "|"
			+ binary.getBooleanAt(0, 0) + " + " + binary.getBooleanAt(0, 1)
			+ " ~~ " + thought[0];
	
}
 

public String learn() {
	/**
	 * Simple explanation of these steps:
	 * 
	 * 1) If it is currently learning, change the inputs to either true or false.
	 * 
	 * 2) Let the NN tick and think. This will return the outputs from the OutpuitNeurons
	 * 
	 * 3) If it is not learning, just return the answer.
	 * 
	 * 4) Else, do the logic and see if the answer it gave (thought[0]) was correct.
	 * 
	 * 5) If it was not correct, use the DeepReinforcementUtil to improve it.
	 * 
	 * 6) After inprovement, return a message with if it was correct, the accuracy, the inputs, and what it thought was the output,
	 */
	binary.changeValueAt(0, 0,
			ThreadLocalRandom.current().nextBoolean());
	binary.changeValueAt(0, 1,
			ThreadLocalRandom.current().nextBoolean());
	boolean[] thought = tickAndThink();
	boolean logic = (binary.getBooleanAt(0, 0) && binary.getBooleanAt(0, 1));
	boolean wasCorrect = (logic == thought[0]);
	this.getAccuracy().addEntry(wasCorrect);

	// IMPROVE IT
	HashMap<Neuron, Double> map = new HashMap<>();
	for (int i = 0; i < thought.length; i++)
		map.put(ai.getNeuronFromId(i), logic ? 1.0 : -1.0);
	if (!wasCorrect)
		DeepReinforcementUtil.instantaneousReinforce(this, map,1);

	return (wasCorrect ? ChatColor.GREEN : ChatColor.RED) + "acc "
			+ getAccuracy().getAccuracyAsInt() + "|"
			+ binary.getBooleanAt(0, 0) + " + " + binary.getBooleanAt(0, 1)
			+ " ~~ " + thought[0];
	
}
 

public String learn() {
	/**
	 * Simple explanation of these steps:
	 * 
	 * 1) If it is currently learning, change the input to either true or false.
	 * 
	 * 2) Let the NN tick and think. This will return the outputs from the
	 * OutpuitNeurons
	 * 
	 * 3) If it is not learning, just return the answer.
	 * 
	 * 4) Else, do the logic and see if the answer it gave (thought[0]) was correct.
	 * 
	 * 5) If it was not correct, use the DeepReinforcementUtil to improve it.
	 * 
	 * 6) After inprovement, return a message with if it was correct, the accuracy,
	 * the inputs, and what it thought was the output,
	 */
	binary.changeValueAt(0, 0, ThreadLocalRandom.current().nextBoolean());
	boolean[] thought = tickAndThink();
	boolean logic = !(binary.getBooleanAt(0, 0));
	boolean result = (logic == thought[0]);
	this.getAccuracy().addEntry(result);

	// IMPROVE IT
	HashMap<Neuron, Double> map = new HashMap<>();
	for (int i = 0; i < thought.length; i++)
		map.put(ai.getNeuronFromId(i), logic ? 1 : -1.0);
	if (!result)
		DeepReinforcementUtil.instantaneousReinforce(this, map, 1);
	return ((result ? ChatColor.GREEN : ChatColor.RED) + "acc " + getAccuracy().getAccuracyAsInt() + "|"
			+ binary.getBooleanAt(0, 0) + " ~~ " + thought[0]);
}
 

private boolean getToggle(CommandSender sender, String[] args)
{
    try
    {
        Setting toggle = MinecraftServer.getServer().sushchestvoConfig.getSettings().get(args[1]);
        // check config directly
        if (toggle == null && MinecraftServer.getServer().sushchestvoConfig.isSet(args[1]))
        {
            if (MinecraftServer.getServer().sushchestvoConfig.isBoolean(args[1]))
            {
                toggle = new BoolSetting(MinecraftServer.getServer().sushchestvoConfig, args[1], MinecraftServer.getServer().sushchestvoConfig.getBoolean(args[1], false), "");
            }
            else if (MinecraftServer.getServer().sushchestvoConfig.isInt(args[1]))
            {
                toggle = new IntSetting(MinecraftServer.getServer().sushchestvoConfig, args[1], MinecraftServer.getServer().sushchestvoConfig.getInt(args[1], 1), "");
            }
            if (toggle != null)
            {
                MinecraftServer.getServer().sushchestvoConfig.getSettings().put(toggle.path, toggle);
                MinecraftServer.getServer().sushchestvoConfig.load();
            }
        }
        if (toggle == null)
        {
            sender.sendMessage(ChatColor.RED + "Could not find option: " + args[1]);
            return false;
        }
        Object value = toggle.getValue();
        String option = (Boolean.TRUE.equals(value) ? ChatColor.GREEN : ChatColor.RED) + " " + value;
        sender.sendMessage(ChatColor.GOLD + args[1] + " " + option);
    }
    catch (Exception ex)
    {
        sender.sendMessage(ChatColor.RED + "Usage: " + usageMessage);
        ex.printStackTrace();
    }
    return true;
}
 

public String learn() {
	boolean[] bbb = numberToBinaryBooleans((lastNumber++ % 1023)/* (int) (Math.random() * 1023) */);
	for (int i = 0; i < bbb.length; i++) {
		binary.changeNumberAt(0, i, (bbb[i]) ? 1 : 0);
		binary.changeNumberAt(1, i, (bbb[i]) ? 0 : 1);
	}

	boolean[] thought = tickAndThink();
	float accuracy = 0;

	// If it isprime:

	boolean[] booleanBase = new boolean[10];
	for (int i = 0; i < 10; i++) {
		booleanBase[i] = binary.getNumberAt(0, i) != 0;
	}
	int number = binaryBooleansToNumber(booleanBase);
	boolean result = ifNumberIsPrime.get(number);

	wasCorrect = (result == thought[0]);

	this.getAccuracy().addEntry(wasCorrect);
	accuracy = (float) this.getAccuracy().getAccuracy();

	// IMPROVE IT
	HashMap<Neuron, Double> map = new HashMap<>();
	map.put(ai.getNeuronFromId(0), result ? 1 : -1.0);
	if (!wasCorrect)
		DeepReinforcementUtil.instantaneousReinforce(this, map, 1);
	return ((wasCorrect ? ChatColor.GREEN : ChatColor.RED) + "acc " + ((int) (100 * accuracy)) + "|=" + number
			+ "|correctResp=" + result + "|WasPrime-Score "
			+ ((int) (100 * (ai.getNeuronFromId(0).getTriggeredStength()))));
}
 

private boolean onSafe(Player sender, String label, String[] args) {
    String usage = ChatColor.RED + "/" + label + " safe <radius>";
    if (args.length == 1) {
        sender.sendMessage(ChatColor.RED + "Provide safe-from-radiation zone radius in the first argument. Radius will be relative to your current position.");
        sender.sendMessage(usage);
        return true;
    }

    int radius;
    try {
        radius = Integer.parseInt(args[1]);
    } catch (NumberFormatException e) {
        sender.sendMessage(ChatColor.RED + "Number was expected, but " + args[1] + " was provided.");
        sender.sendMessage(ChatColor.RED + usage);
        return true;
    }

    if (radius <= 0) {
        sender.sendMessage(ChatColor.RED + "Radius must be positive.");
        sender.sendMessage(ChatColor.RED + usage);
        return true;
    }

    RegionContainer container = this.worldGuardMatcher.getRegionContainer();
    if (container == null) {
        sender.sendMessage(ChatColor.RED + "Sorry, region container is not currently accessible.");
        return true;
    }

    if (this.define(sender, container, REGION_ID, radius)) {
        BlockVector2 origin = BukkitAdapter.asBlockVector(sender.getLocation()).toBlockVector2();
        sender.sendMessage(ChatColor.GREEN + "A new safe-from-radiation zone has been created in radius " +
                radius + " at the origin at " + origin + " in world " + sender.getWorld().getName() + ".");
    }
    return true;
}
 

public String learn() {
	/**
	 * Simple explanation of these steps:
	 * 
	 * 1) If it is currently learning, change the inputs to either true or false.
	 * 
	 * 2) Let the NN tick and think. This will return the outputs from the OutpuitNeurons
	 * 
	 * 3) If it is not learning, just return the answer.
	 * 
	 * 4) Else, do the logic and see if the answer it gave (thought[0]) was correct.
	 * 
	 * 5) If it was not correct, use the DeepReinforcementUtil to improve it.
	 * 
	 * 6) After inprovement, return a message with if it was correct, the accuracy, the inputs, and what it thought was the output,
	 */
	binary.changeValueAt(0, 0,
			ThreadLocalRandom.current().nextBoolean());
	binary.changeValueAt(0, 1,
			ThreadLocalRandom.current().nextBoolean());
	boolean[] thought = tickAndThink();
	boolean logic = !(binary.getBooleanAt(0, 0)&& binary.getBooleanAt(0, 1));
	boolean wasCorrect = (logic == thought[0]);
	this.getAccuracy().addEntry(wasCorrect);

	// IMPROVE IT
	HashMap<Neuron, Double> map = new HashMap<>();
	for (int i = 0; i < thought.length; i++)
		map.put(ai.getNeuronFromId(i), logic ? 1.0 : -1.0);
	if (!wasCorrect)
		DeepReinforcementUtil.instantaneousReinforce(this, map,1);

	return (wasCorrect ? ChatColor.GREEN : ChatColor.RED) + "acc "
			+ getAccuracy().getAccuracyAsInt() + "|"
			+ binary.getBooleanAt(0, 0) + " + " + binary.getBooleanAt(0, 1)
			+ " ~~ " + thought[0];
	
}
 

private String getStatus() {
  String status = null;
  if (this.game.getState() == GameState.WAITING && this.game.isFull()) {
    status = ChatColor.RED + BedwarsRel._l("sign.gamestate.full");
  } else {
    status = BedwarsRel._l("sign.gamestate." + this.game.getState().toString().toLowerCase());
  }

  return status;
}
 

private String getComparisonString(Integer value) {
  if (value > 0) {
    return ChatColor.GREEN + "+" + value;
  } else if (value < 0) {
    return ChatColor.RED + String.valueOf(value);
  } else {
    return String.valueOf(value);
  }
}
 

private String getStatus(Game game) {
  String status = null;
  if (game.getState() == GameState.WAITING && game.isFull()) {
    status = ChatColor.RED + BedwarsRel._l("sign.gamestate.full");
  } else {
    status = BedwarsRel._l("sign.gamestate." + game.getState().toString().toLowerCase());
  }

  return status;
}
 

public static String getFormattedName(Plugin plugin, boolean includeVersions) {
    ChatColor color = plugin.isEnabled() ? ChatColor.GREEN : ChatColor.RED;
    String pluginName = color + plugin.getName() + ChatColor.RESET;
    if (includeVersions) {
        pluginName = pluginName + " (" + plugin.getDescription().getVersion() + ")";
    }
    return pluginName;
}
 

private ChatColor pingColor(double ping) {
    if (ping <= 30) {
        return ChatColor.GREEN;
    } else if (ping <= 60) {
        return ChatColor.DARK_GREEN;
    } else if (ping <= 100) {
        return ChatColor.YELLOW;
    } else if (ping <= 150) {
        return ChatColor.GOLD;
    }
    return ChatColor.RED;
}
 

private ChatMessage waitingPlayerMessage(int players) {
    List<TeamModule> teams = Teams.getTeams().stream()
            .filter((team) -> !team.isObserver() && team.size() < team.getMin()).limit(2).collect(Collectors.toList());
    return new UnlocalizedChatMessage(ChatColor.RED + "{0}",
            new LocalizedChatMessage(players == 1 ? ChatConstant.UI_WAITING_PLAYER : ChatConstant.UI_WAITING_PLAYERS,
                    ChatColor.AQUA + "" + players + ChatColor.RED,
                    teams.size() == 1 ? teams.get(0).getCompleteName() : ""));
}
 

private String command() {
    return ChatColor.RED + "\"" + ChatColor.GOLD + command + ChatColor.RED + "\"";
}
 

public String learn() {
	boolean[] bbb = numberToBinaryBooleans((int) (Math.random() * (Math.pow(2, max_bytes))));
	boolean[] bbb2 = numberToBinaryBooleans((int) (Math.random() * (Math.pow(2, max_bytes))));
	for (int i = 0; i < bbb.length; i++) {
		this.binary.changeValueAt(0, i, (bbb[i]));
		((NumberAdder) base).binary.changeValueAt(1, i, (!bbb[i]));
	}
	for (int i = 0; i < bbb2.length; i++) {
		((NumberAdder) base).binary.changeValueAt(2, i, (bbb2[i]));
		((NumberAdder) base).binary.changeValueAt(3, i, (!bbb2[i]));
	}
	boolean[] thought = tickAndThink();

	boolean[] booleanBase = new boolean[10];
	for (int i = 0; i < 10; i++) {
		booleanBase[i] = base.binary.getBooleanAt(0, i);
	}

	boolean[] booleanBase2 = new boolean[10];
	for (int i = 0; i < 10; i++) {
		booleanBase2[i] = base.binary.getBooleanAt(2, i);
	}
	int number = binaryBooleansToNumber(booleanBase);
	int number2 = binaryBooleansToNumber(booleanBase2);
	int number3 = binaryBooleansToNumber(thought);

	boolean result = number + number2 == number3;
	boolean[] correctvalues = numberToBinaryBooleans(number + number2);

	this.getAccuracy().addEntry(result);

	StringBuilder sb = new StringBuilder();
	int amountOfMistakes = 0;
	for (int i = 0; i < Math.max(correctvalues.length, thought.length); i++) {
		if (i < thought.length && thought[i]) {
			sb.append((correctvalues.length > i && correctvalues[i] ? ChatColor.DARK_GREEN : ChatColor.DARK_RED)
					+ "+" + ((int) Math.pow(2, i)));
			if (!(correctvalues.length > i && correctvalues[i]))
				amountOfMistakes++;
		} else if (i < correctvalues.length && correctvalues[i]) {
			sb.append(ChatColor.GRAY + "+" + ((int) Math.pow(2, i)));
			amountOfMistakes++;
		}
	}

	// IMPROVE IT
	HashMap<Neuron, Double> map = new HashMap<>();
	for (int i = 0; i < thought.length; i++) {
		map.put(base.ai.getNeuronFromId(i), correctvalues.length > i && correctvalues[i] ? 1 : -1.0);
	}

	// amountOfMistakes = (int) Math.pow(2,amountOfMistakes);
	if (!result)
		DeepReinforcementUtil.instantaneousReinforce(base, map, amountOfMistakes);
	return ((result ? ChatColor.GREEN : ChatColor.RED) + "acc " + getAccuracy().getAccuracyAsInt() + "|" + number
			+ " + " + number2 + " = " + number3 + "|  " + sb.toString());
}
 

private static ChatColor setHealthColor(int currentHealth, int maxHealth) {

        double healthPercentage = currentHealth * 100 / maxHealth;

        if (healthPercentage > 75) {

            return ChatColor.DARK_GREEN;

        }

        if (healthPercentage > 50) {

            return ChatColor.GREEN;

        }

        if (healthPercentage > 25) {

            return ChatColor.RED;

        }

        if (healthPercentage > 0) {

            return ChatColor.DARK_RED;

        }

        return ChatColor.DARK_RED;

    }