com.screenslicer.core.scrape.neural.NeuralNetManager.java Source code

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Here is the source code for com.screenslicer.core.scrape.neural.NeuralNetManager.java

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/* 
 * ScreenSlicer (TM) -- automatic, zero-config web scraping (TM)
 * Copyright (C) 2013-2014 Machine Publishers, LLC
 * ops@machinepublishers.com | screenslicer.com | machinepublishers.com
 * 717 Martin Luther King Dr W Ste I, Cincinnati, Ohio 45220
 *
 * You can redistribute this program and/or modify it under the terms of the
 * GNU Affero General Public License version 3 as published by the Free
 * Software Foundation. Additional permissions or commercial licensing may be
 * available--see LICENSE file or contact Machine Publishers, LLC for details.
 * 
 * This program is distributed in the hope that it will be useful, but WITHOUT
 * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 * FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License version 3
 * for more details.
 * 
 * You should have received a copy of the GNU Affero General Public License
 * version 3 along with this program. If not, see <http://www.gnu.org/licenses/>.
 * 
 * For general details about how to investigate and report license violations,
 * please see: https://www.gnu.org/licenses/gpl-violation.html
 * and email the author: ops@machinepublishers.com
 * Keep in mind that paying customers have more rights than the AGPL alone offers.
 */
package com.screenslicer.core.scrape.neural;

import java.io.File;

import org.apache.commons.io.FileUtils;

import com.screenslicer.common.Log;

public class NeuralNetManager {
    private static NeuralNetVoters net = new NeuralNetVoters();

    private NeuralNetManager() {

    }

    //TODO make thread safe
    public static NeuralNet instance() {
        return net;
    }

    public static String asString() {
        return net.toString();
    }

    public static NeuralNet randomInstance(int numInputs, int numNets, int numLayers, int numNodesPerLayer) {
        for (int i = 0; i < numNets; i++) {
            if (i == 0) {
                reset(new NeuralNetVote(
                        NeuralNetProperties.randomInstance(numInputs, numLayers, numNodesPerLayer)));
            } else {
                add(new NeuralNetVote(NeuralNetProperties.randomInstance(numInputs, numLayers, numNodesPerLayer)));
            }
        }
        return net;
    }

    public static void add(String config) {
        if (config != null) {
            net.add(NeuralNetProperties.load(config));
        }
    }

    public static void add(File config) {
        if (config != null) {
            try {
                add(FileUtils.readFileToString(config, "utf-8"));
            } catch (Exception e) {
                Log.exception(e);
                throw new RuntimeException(e);
            }
        }
    }

    public static void add(NeuralNet nn) {
        if (nn != null) {
            net.add(((NeuralNetProperties.Configurable) nn).properties());
        }
    }

    public static void reset(String config) {
        net = new NeuralNetVoters();
        if (config != null) {
            net.add(NeuralNetProperties.load(config));
        } else {
            throw new IllegalArgumentException();
        }
    }

    public static void reset(File config) {
        try {
            reset(FileUtils.readFileToString(config, "utf-8"));
        } catch (Exception e) {
            Log.exception(e);
            throw new RuntimeException(e);
        }
    }

    public static void reset(NeuralNet nn) {
        net = new NeuralNetVoters();
        if (nn != null) {
            net.add(((NeuralNetProperties.Configurable) nn).properties());
        } else {
            throw new IllegalArgumentException();
        }
    }

    public static NeuralNet copy() {
        NeuralNetVoters copy = new NeuralNetVoters();
        copy.add(NeuralNetProperties.load(net.toString()));
        return copy;
    }
}