org.deeplearning4j.datasets.base.IrisUtils Maven / Gradle / Ivy
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package org.deeplearning4j.datasets.base;
import org.apache.commons.io.IOUtils;
import org.deeplearning4j.common.resources.DL4JResources;
import org.deeplearning4j.common.resources.ResourceType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.common.resources.Downloader;
import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStream;
import java.net.URL;
import java.util.ArrayList;
import java.util.List;
public class IrisUtils {
private static final String IRIS_RELATIVE_URL = "datasets/iris.dat";
private static final String MD5 = "1c21400a78061197eac64c6748844216";
private IrisUtils() {}
public static List loadIris(int from, int to) throws IOException {
File rootDir = DL4JResources.getDirectory(ResourceType.DATASET, "iris");
File irisData = new File(rootDir, "iris.dat");
if(!irisData.exists()){
URL url = DL4JResources.getURL(IRIS_RELATIVE_URL);
Downloader.download("Iris", url, irisData, MD5, 3);
}
@SuppressWarnings("unchecked")
List lines;
try(InputStream is = new FileInputStream(irisData)){
lines = IOUtils.readLines(is);
}
List list = new ArrayList<>();
INDArray ret = Nd4j.ones(Math.abs(to - from), 4);
double[][] outcomes = new double[lines.size()][3];
int putCount = 0;
for (int i = from; i < to; i++) {
String line = lines.get(i);
String[] split = line.split(",");
addRow(ret, putCount++, split);
String outcome = split[split.length - 1];
double[] rowOutcome = new double[3];
rowOutcome[Integer.parseInt(outcome)] = 1;
outcomes[i] = rowOutcome;
}
for (int i = 0; i < ret.rows(); i++) {
DataSet add = new DataSet(ret.getRow(i, true), Nd4j.create(outcomes[from + i], new long[]{1,3}));
list.add(add);
}
return list;
}
private static void addRow(INDArray ret, int row, String[] line) {
double[] vector = new double[4];
for (int i = 0; i < 4; i++)
vector[i] = Double.parseDouble(line[i]);
ret.putRow(row, Nd4j.create(vector));
}
}
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