org.deeplearning4j.base.IrisUtils Maven / Gradle / Ivy
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*
* * 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
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* * 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.
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package org.deeplearning4j.base;
import org.apache.commons.io.IOUtils;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.io.ClassPathResource;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
public class IrisUtils {
private IrisUtils() {}
public static List loadIris(int from, int to) throws IOException {
ClassPathResource resource = new ClassPathResource("/iris.dat");
@SuppressWarnings("unchecked")
List lines = IOUtils.readLines(resource.getInputStream());
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), Nd4j.create(outcomes[from + i]));
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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