Package org.encog.util.simple

Source Code of org.encog.util.simple.TrainingSetUtil

/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 Heaton Research, 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.
*  
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.util.simple;

import org.encog.ml.data.MLData;
import org.encog.ml.data.MLDataPair;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLData;
import org.encog.ml.data.basic.BasicMLDataPair;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.util.EngineArray;
import org.encog.util.ObjectPair;
import org.encog.util.csv.CSVFormat;
import org.encog.util.csv.ReadCSV;

public class TrainingSetUtil {

  /**
   * Load a CSV file into a memory dataset. 
   * @param format The CSV format to use.
   * @param filename The filename to load.
   * @param headers True if there is a header line.
   * @param inputSize The input size.  Input always comes first in a file.
   * @param idealSize The ideal size, 0 for unsupervised.
   * @return A NeuralDataSet that holds the contents of the CSV file.
   */
  public static MLDataSet loadCSVTOMemory(CSVFormat format,
      String filename, boolean headers, int inputSize, int idealSize) {
    MLDataSet result = new BasicMLDataSet();
    ReadCSV csv = new ReadCSV(filename, headers, format);
    while (csv.next()) {
      MLData input = null;
      MLData ideal = null;
      int index = 0;

      input = new BasicMLData(inputSize);
      for (int i = 0; i < inputSize; i++) {
        double d = csv.getDouble(index++);
        input.setData(i, d);
      }

      if (idealSize > 0) {
        ideal = new BasicMLData(idealSize);
        for (int i = 0; i < idealSize; i++) {
          double d = csv.getDouble(index++);
          ideal.setData(i, d);
        }
      }

      MLDataPair pair = new BasicMLDataPair(input, ideal);
      result.add(pair);
    }

    return result;
  }

  public static ObjectPair<double[][], double[][]> trainingToArray(
      MLDataSet training) {
    int length = (int)training.getRecordCount();
    double[][] a = new double[length][training.getInputSize()];
    double[][] b = new double[length][training.getIdealSize()];

    int index = 0;
    for (MLDataPair pair : training) {
      EngineArray.arrayCopy(pair.getInputArray(), a[index]);
      EngineArray.arrayCopy(pair.getIdealArray(), b[index]);
      index++;
    }

    return new ObjectPair<double[][], double[][]>(a, b);
  }
}
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