diff --git a/src/me/zombie_striker/neuralnetwork/util/DeepReinformentUtil.java b/src/me/zombie_striker/neuralnetwork/util/DeepReinformentUtil.java
deleted file mode 100644
index 8fc4530..0000000
--- a/src/me/zombie_striker/neuralnetwork/util/DeepReinformentUtil.java
+++ /dev/null
@@ -1,207 +0,0 @@
-package me.zombie_striker.neuralnetwork.util;
-
-import java.util.*;
-import java.util.Map.Entry;
-
-import me.zombie_striker.neuralnetwork.NNBaseEntity;
-import me.zombie_striker.neuralnetwork.neurons.*;
-import me.zombie_striker.neuralnetwork.neurons.input.InputNeuron;
-
-public class DeepReinformentUtil {
-
-	/**
-	 * Teaches the NN for an instantaneous, constant answer regarding a single
-	 * response. Useful for true-false situations
-	 * 
-	 * @param Base
-	 *            The NeuralEntity
-	 * @param neuronsThatShouldBeTrue
-	 *            Array of all neuons that should be equal to 1. The rest will
-	 *            try to be decreased to -1;
-	 * @param repetitions
-	 *            the amount of repetitions for the training.
-	 */
-	public static void instantaneousReinforce(NNBaseEntity base,
-			Neuron[] neuronsThatShouldBeTrue, int repetitions) {
-		HashMap<Neuron, Double> h = new HashMap<>();
-		for (Neuron n : base.ai.getOutputNeurons()) {
-			boolean isRightNeuron = false;
-			for (Neuron rn : neuronsThatShouldBeTrue) {
-				if (n == rn) {
-					isRightNeuron = true;
-					break;
-				}
-			}
-			h.put(n, isRightNeuron ? 1.0 : -1.0);
-		}
-		instantaneousReinforce(base, h, repetitions);
-	}
-
-	/**
-	 * Teaches the NN for an instantaneous, constant answer regarding a single
-	 * response. Useful for true-false situations
-	 * 
-	 * @param Base
-	 *            the NeuralEntity
-	 * @param correctValues
-	 *            a Hashmap of all neurons and the values they should be equal
-	 *            to
-	 * @param repetitions
-	 *            the amount of repetitions for the training.
-	 */
-	public static void instantaneousReinforce(NNBaseEntity base,
-			HashMap<Neuron, Double> correctValues, int repetitions) {
-		final double step = 0.01;
-
-		for (int loops = 0; loops < repetitions; loops++) {
-			HashMap<Neuron, Double> suggestedValueForNeuron = correctValues;
-			// Subtract -1 a second time to not get the outputs
-			for (int layer = base.ai.MAX_LAYERS - 1 - 1; layer >= 0; layer--) {
-				for (Neuron n : base.ai.getNeuronsInLayer(layer)) {
-					HashMap<Neuron, Double> currentValsThre = new HashMap<>();
-					for (Neuron outputs : suggestedValueForNeuron.keySet()) {
-						currentValsThre
-								.put(outputs, outputs.getTriggeredStength());
-					}
-					double orgThr = n.getThreshold();
-					if (!n.isTriggered()) {
-						if (!(n instanceof InputNeuron))
-							if(n.getTriggeredStength() <= n.getThreshold()){
-								n.setThreshold(-2);
-								n.forceTriggerUpdateTree();
-								double shouldLowerVal =0;
-								for (Entry<Neuron, Double> c : currentValsThre.entrySet()) {
-									double sug = suggestedValueForNeuron.get(c.getKey());
-									shouldLowerVal += (Math.abs(c.getValue()-sug)-Math.abs(c.getKey().getTriggeredStength()-sug));
-								}
-								n.setThreshold(orgThr);
-								if(shouldLowerVal>0){
-									n.setThreshold(n.getThreshold()-step);
-								}
-							}
-							continue;
-					}else{
-						if (!(n instanceof InputNeuron))
-						if(n.getTriggeredStength() > n.getThreshold()){
-							n.setThreshold(2);
-							n.forceTriggerUpdateTree();
-							double shouldIncreaseVal =0;
-							for (Entry<Neuron, Double> c : currentValsThre.entrySet()) {
-								double sug = suggestedValueForNeuron.get(c.getKey());
-								shouldIncreaseVal += (Math.abs(c.getValue()-sug)-Math.abs(c.getKey().getTriggeredStength()-sug));
-							}
-							n.setThreshold(orgThr);
-							if(shouldIncreaseVal>0){
-								n.setThreshold(n.getThreshold()+step);
-							}
-						}
-					}
-
-					HashMap<Neuron, Double> currentVals2 = new HashMap<>();
-					for (Neuron outputs : suggestedValueForNeuron.keySet()) {
-						currentVals2
-								.put(outputs, outputs.getTriggeredStength());
-					}
-					n.setWeight(n.getWeight() - (step));
-					n.forceTriggerUpdateTree();
-					double change2 = 0.0;
-					for (Entry<Neuron, Double> c : currentVals2.entrySet()) {
-						// TODO: Multipled by 100 so the change is not a small
-						// number.
-						int wasRightDirection = (Math
-								.abs((suggestedValueForNeuron.get(c.getKey()) - c
-										.getKey().getTriggeredStength())) <= Math
-								.abs(suggestedValueForNeuron.get(c.getKey())
-										- c.getValue()) ? 1 : -1);
-
-						change2 += wasRightDirection
-								* (Math.abs((c.getKey().getTriggeredStength() * 100)
-										- (c.getValue() * 100)));
-						/*
-						 * change2 += ((Math
-						 * .abs((c.getKey().getTriggeredStength() * 100) -
-						 * (c.getValue() * 100))) * (Math
-						 * .abs((suggestedValueForNeuron.get(c.getKey()) - c
-						 * .getKey().getTriggeredStength())) <= Math
-						 * .abs(suggestedValueForNeuron.get(c.getKey()) -
-						 * c.getValue()) ? 1.0 : -1.0));
-						 */
-
-					}
-
-					if (change2 == 0.0) {
-						n.setWeight(n.getWeight() + (step));
-					} else if (change2 < 0.0) {
-						n.setWeight(n.getWeight() + (step * 2));
-					}
-					n.setWeight(removeExtremes(n.getWeight()));
-					n.forceTriggerUpdateTree();
-
-					for (int outputNeuronId : n.getStrengthIDs()) {
-						Neuron outputNeuronInstance = n.getAI()
-								.getNeuronFromId(outputNeuronId);
-
-						HashMap<Neuron, Double> currentVals = new HashMap<>();
-						for (Neuron outputs : suggestedValueForNeuron.keySet()) {
-							currentVals.put(outputs,
-									outputs.getTriggeredStength());
-						}
-						n.setStrengthForNeuron(outputNeuronInstance,
-								n.getOutputForNeuron(outputNeuronInstance)
-										- (step));
-						n.forceTriggerStengthUpdate();
-						outputNeuronInstance.forceTriggerUpdateTree();
-						double change = 0.0;
-						for (Entry<Neuron, Double> c : currentVals.entrySet()) {
-							int wasRightDirection = (Math
-									.abs((suggestedValueForNeuron.get(c
-											.getKey()) - c.getKey()
-											.getTriggeredStength())) <= Math
-									.abs(suggestedValueForNeuron.get(c.getKey())
-											- c.getValue()) ? 1 : -1);
-
-							change += wasRightDirection
-									* (Math.abs((c.getKey()
-											.getTriggeredStength() * 100)
-											- (c.getValue() * 100)));
-							/*
-							 * change += ((Math.abs((c.getKey()
-							 * .getTriggeredStength() * 100) - (c.getValue() *
-							 * 100))) * (Math
-							 * .abs((suggestedValueForNeuron.get(c .getKey()) -
-							 * c.getKey() .getTriggeredStength())) <= Math
-							 * .abs(suggestedValueForNeuron.get(c.getKey()) -
-							 * c.getValue()) ? 1.0 : -1.0));
-							 */
-						}
-
-						if (change == 0.0) {
-							n.setStrengthForNeuron(outputNeuronInstance,
-									n.getOutputForNeuron(outputNeuronInstance)
-											+ (step));
-						} else if (change < 0.0) {
-							n.setStrengthForNeuron(outputNeuronInstance,
-									n.getOutputForNeuron(outputNeuronInstance)
-											+ (step * 2));
-						}
-						n.setStrengthForNeuron(
-								outputNeuronInstance,
-								removeExtremes(n
-										.getStrengthForNeuron(outputNeuronInstance)));
-						n.forceTriggerStengthUpdate();
-						outputNeuronInstance.forceTriggerUpdateTree();
-					}
-				}
-			}
-		}
-	}
-
-
-	private static double removeExtremes(double d) {
-		if (d > 1)
-			return 1;
-		if (d < -1)
-			return -1;
-		return d;
-	}
-}