/*
 *
 *  * Copyright 2015 Skymind,Inc.
 *  *
 *  *    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 org.deeplearning4j.optimize;

import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collection;
import java.util.List;

import org.deeplearning4j.nn.api.Model;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.optimize.api.ConvexOptimizer;
import org.deeplearning4j.optimize.api.IterationListener;
import org.deeplearning4j.optimize.api.StepFunction;
import org.deeplearning4j.optimize.solvers.ConjugateGradient;
import org.deeplearning4j.optimize.solvers.LineGradientAscent;
import org.deeplearning4j.optimize.solvers.StochasticGradientDescent;
import org.deeplearning4j.optimize.solvers.LBFGS;
import org.deeplearning4j.optimize.solvers.StochasticHessianFree;
import org.deeplearning4j.optimize.stepfunctions.StepFunctions;

/**
 * Generic purpose solver
 * @author Adam Gibson
 */
public class Solver {
    private NeuralNetConfiguration conf;
    private Collection<IterationListener> listeners;
    private Model model;
    private ConvexOptimizer optimizer;
    private StepFunction stepFunction;

    public void optimize() {
        if(optimizer == null)
            optimizer = getOptimizer();
        optimizer.optimize();

    }

    public ConvexOptimizer getOptimizer() {
        switch(conf.getOptimizationAlgo()) {
            case LBFGS:
                return new LBFGS(conf,stepFunction,listeners,model);
            case LINE_GRADIENT_DESCENT:
                return new LineGradientAscent(conf,stepFunction,listeners,model);
            case HESSIAN_FREE:
                return new StochasticHessianFree(conf,stepFunction,listeners,model);
            case CONJUGATE_GRADIENT:
                return new ConjugateGradient(conf,stepFunction,listeners,model);
            case STOCHASTIC_GRADIENT_DESCENT:
                return new StochasticGradientDescent(conf,stepFunction,listeners,model);
            default:
                throw new IllegalStateException("No optimizer found");
        }

    }

    public static class Builder {
        private NeuralNetConfiguration conf;
        private Model model;
        private List<IterationListener> listeners = new ArrayList<>();

        public Builder configure(NeuralNetConfiguration conf) {
            this.conf = conf;
            return this;
        }
        
        public Builder listener(IterationListener... listeners) {
            this.listeners.addAll(Arrays.asList(listeners));
            return this;
        }

        public Builder listeners(Collection<IterationListener> listeners) {
            this.listeners.addAll(listeners);
            return this;
        }
        
        public Builder model(Model model) {
            this.model = model;
            return this;
        }

        public Solver build() {
            Solver solver = new Solver();
            solver.conf = conf;
            solver.stepFunction = StepFunctions.createStepFunction(conf.getStepFunction());
            solver.model = model;
            solver.listeners = listeners;
            return solver;
        }
    }


}

@@ -1184,25 +1184,26 @@
 LineGradient
-A
+De
 scent;%0Aimpor
@@ -2123,17 +2123,18 @@
 Gradient
-A
+De
 scent(co

Change ascent to descent.