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/*
* Copyright (c) 2019 by Andrew Charneski.
*
* The author licenses this file to you 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.
*/
package com.simiacryptus.mindseye.test.integration;
import com.simiacryptus.mindseye.lang.Coordinate;
import com.simiacryptus.mindseye.lang.Tensor;
import com.simiacryptus.notebook.NotebookOutput;
import com.simiacryptus.ref.lang.RefUtil;
import com.simiacryptus.ref.wrappers.*;
import com.simiacryptus.util.test.LabeledObject;
import javax.annotation.Nonnull;
import javax.annotation.Nullable;
import java.io.IOException;
import java.util.Random;
import java.util.function.IntFunction;
import java.util.function.ToDoubleFunction;
/**
* The type Supplemented problem data.
*/
public class SupplementedProblemData implements ImageProblemData {
private final int expansion = 10;
private final ImageProblemData inner;
private final Random random = new Random();
/**
* Instantiates a new Supplemented problem data.
*
* @param inner the inner
*/
public SupplementedProblemData(final ImageProblemData inner) {
this.inner = inner;
}
/**
* Print sample.
*
* @param log the log
* @param expanded the expanded
* @param size the size
*/
public static void printSample(@Nonnull final NotebookOutput log, @Nullable final Tensor[][] expanded, final int size) {
@Nonnull final RefArrayList list = new RefArrayList<>(RefArrays.asList(expanded));
RefCollections.shuffle(list.addRef());
log.p("Expanded Training Data Sample: " + RefUtil.get(list.stream().limit(size).map(x -> {
String temp_16_0001 = log.png(x[0].toGrayImage(), "");
RefUtil.freeRef(x);
return temp_16_0001;
}).reduce((a, b) -> a + b)));
list.freeRef();
}
/**
* Add noise tensor.
*
* @param tensor the tensor
* @return the tensor
*/
@Nonnull
protected static Tensor addNoise(@Nonnull final Tensor tensor) {
Tensor temp_16_0003 = tensor.mapParallel(v -> Math.random() < 0.9 ? v : v + Math.random() * 100);
tensor.freeRef();
return temp_16_0003;
}
/**
* Translate tensor.
*
* @param dx the dx
* @param dy the dy
* @param tensor the tensor
* @return the tensor
*/
@Nonnull
protected static Tensor translate(final int dx, final int dy, @Nonnull final Tensor tensor) {
final int sx = tensor.getDimensions()[0];
final int sy = tensor.getDimensions()[1];
return new Tensor(
tensor.coordStream(true).mapToDouble(RefUtil.wrapInterface((ToDoubleFunction super Coordinate>) c -> {
final int x = c.getCoords()[0] + dx;
final int y = c.getCoords()[1] + dy;
if (x < 0 || x >= sx) {
return 0.0;
} else if (y < 0 || y >= sy) {
return 0.0;
} else {
return tensor.get(x, y);
}
}, tensor)).toArray(), tensor.getDimensions());
}
@Nonnull
@Override
public RefStream> trainingData() throws IOException {
return inner.trainingData().flatMap(labeledObject -> {
return RefIntStream.range(0, expansion)
.mapToObj(RefUtil.wrapInterface((IntFunction) i -> {
final int dx = random.nextInt(10) - 5;
final int dy = random.nextInt(10) - 5;
return SupplementedProblemData.addNoise(SupplementedProblemData.translate(dx, dy, labeledObject.data.addRef()));
}, labeledObject.addRef()))
.map(RefUtil.wrapInterface(t -> {
LabeledObject temp_16_0002 = new LabeledObject<>(t.addRef(), labeledObject.label);
t.freeRef();
return temp_16_0002;
}, labeledObject));
});
}
@Override
public RefStream> validationData() throws IOException {
return inner.validationData();
}
}
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