public void testClassification() throws Exception {
File rawFile = TEMP_DIR.createFile("simple.csv");
File egaFile = TEMP_DIR.createFile("simple.ega");
File outputFile = TEMP_DIR.createFile("simple_output.csv");
FileUtil.copyResource("org/encog/data/simple.csv", rawFile);
FileUtil.copyResource("org/encog/data/simple-r.ega", egaFile);
EncogAnalyst analyst = new EncogAnalyst();
analyst.addAnalystListener(new ConsoleAnalystListener());
analyst.load(egaFile);
analyst.executeTask("task-full");
ReadCSV csv = new ReadCSV(outputFile.toString(), true, CSVFormat.ENGLISH);
while (csv.next()) {
double diff = Math.abs(csv.getDouble(2) - csv.getDouble(4));
Assert.assertTrue(diff < 1.5);
}
Assert.assertEquals(4, analyst.getScript().getFields().length);
Assert.assertEquals(3, analyst.getScript().getFields()[3].getClassMembers().size());
csv.close();
}
/**
* 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 void convertCSV2Binary(
File csvFile, CSVFormat format, File binFile, int[] input, int[] ideal, boolean headers) {
binFile.delete();
ReadCSV csv = new ReadCSV(csvFile.toString(), headers, format);
BufferedMLDataSet buffer = new BufferedMLDataSet(binFile);
buffer.beginLoad(input.length, ideal.length);
while (csv.next()) {
BasicMLData inputData = new BasicMLData(input.length);
BasicMLData idealData = new BasicMLData(ideal.length);
// handle input data
for (int i = 0; i < input.length; i++) {
inputData.setData(i, csv.getDouble(input[i]));
}
// handle input data
for (int i = 0; i < ideal.length; i++) {
idealData.setData(i, csv.getDouble(ideal[i]));
}
// add to dataset
buffer.add(inputData, idealData);
}
buffer.endLoad();
}
