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3D rendering engine. Plus modelling. Expected glsl textures 3d and 2d rendering3D primitives, and a lot of scenes' samples to test.+ Game Jogl reworked, Calculator (numbers and vectors). Java code parser implementation starts (<=1.2)
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/*
*
* * Copyright (c) 2024. Manuel Daniel Dahmen
* *
* *
* * Copyright 2024 Manuel Daniel Dahmen
* *
* * Licensed under the Apache License, Version 2.0 (the "License");
* * you may not use this file except in compliance with the License.
* * You may obtain a copy of the License at
* *
* * http://www.apache.org/licenses/LICENSE-2.0
* *
* * Unless required by applicable law or agreed to in writing, software
* * distributed under the License is distributed on an "AS IS" BASIS,
* * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* * See the License for the specific language governing permissions and
* * limitations under the License.
*
*
*/
package one.empty3.neuralnetwork;
public class Loss {
/***
* Fonction de perte Cross Entropy
* Permet de calculer l'erreur (la différence entre la sortie du réseau et la sortie que serait
* celle d'un item identifié (train je pense).
* @param sumX Taille en X de la donnée
* @param sumY Taille en Y de la donnée (0= vecteur)
* @param actual Sortie du reseau constatée
* @param expected Sortie prédite
* @return Mesure 'CrossEntropy' de l'erreur
*/
public static double[] crossentropy(int sumX, int sumY, double [] actual, double [] expected) {
double[] result = new double[expected.length];
for (int i = 0; i < expected.length; i++) {
result[i] =-expected[i]*Math.log(actual[i]);
}
double[] res2 = new double[result.length / sumX];
for(int j=0; j