下面列出了org.bukkit.ChatColor#RED 实例代码，或者点击链接到github查看源代码，也可以在右侧发表评论。
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;
}
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;
}
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 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;
}
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 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;
}
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() {
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;
}
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 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() {
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 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() {
/**
* 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]);
}
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 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]);
}
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;
}