com.simiacryptus.mindseye.layers.java.CrossDifferenceLayer Maven / Gradle / Ivy
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Pure Java Neural Networks Components
/*
* 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.layers.java;
import com.google.gson.JsonObject;
import com.simiacryptus.mindseye.lang.*;
import javax.annotation.Nonnull;
import javax.annotation.Nullable;
import java.util.Arrays;
import java.util.List;
import java.util.Map;
import java.util.UUID;
import java.util.stream.IntStream;
/**
* The type Cross difference key.
*/
@SuppressWarnings("serial")
public class CrossDifferenceLayer extends LayerBase {
/**
* Instantiates a new Cross difference key.
*/
public CrossDifferenceLayer() {
}
/**
* Instantiates a new Cross difference key.
*
* @param id the id
*/
protected CrossDifferenceLayer(@Nonnull final JsonObject id) {
super(id);
}
/**
* From json cross difference key.
*
* @param json the json
* @param rs the rs
* @return the cross difference key
*/
public static CrossDifferenceLayer fromJson(@Nonnull final JsonObject json, Map rs) {
return new CrossDifferenceLayer(json);
}
/**
* Index int.
*
* @param x the x
* @param y the y
* @param max the max
* @return the int
*/
public static int index(final int x, final int y, final int max) {
return max * (max - 1) / 2 - (max - x) * (max - x - 1) / 2 + y - x - 1;
}
@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
assert 1 == inObj.length;
Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
return new Result(TensorArray.wrap(inObj[0].getData().stream().parallel().map(tensor -> {
final int inputDim = tensor.length();
final int outputDim = (inputDim * inputDim - inputDim) / 2;
@Nonnull final Tensor result = new Tensor(outputDim);
@Nullable final double[] inputData = tensor.getData();
@Nullable final double[] resultData = result.getData();
IntStream.range(0, inputDim).forEach(x -> {
IntStream.range(x + 1, inputDim).forEach(y -> {
resultData[CrossDifferenceLayer.index(x, y, inputDim)] = inputData[x] - inputData[y];
});
});
tensor.freeRef();
return result;
}).toArray(i -> new Tensor[i])), (@Nonnull final DeltaSet buffer, @Nonnull final TensorList data) -> {
final Result input = inObj[0];
if (input.isAlive()) {
@Nonnull TensorArray tensorArray = TensorArray.wrap(data.stream().parallel().map(tensor -> {
final int outputDim = tensor.length();
final int inputDim = (1 + (int) Math.sqrt(1 + 8 * outputDim)) / 2;
@Nonnull final Tensor passback = new Tensor(inputDim);
@Nullable final double[] passbackData = passback.getData();
@Nullable final double[] tensorData = tensor.getData();
IntStream.range(0, inputDim).forEach(x -> {
IntStream.range(x + 1, inputDim).forEach(y -> {
passbackData[x] += tensorData[CrossDifferenceLayer.index(x, y, inputDim)];
passbackData[y] += -tensorData[CrossDifferenceLayer.index(x, y, inputDim)];
});
});
tensor.freeRef();
return passback;
}).toArray(i -> new Tensor[i]));
input.accumulate(buffer, tensorArray);
}
data.freeRef();
}) {
@Override
protected void _free() {
Arrays.stream(inObj).forEach(nnResult -> nnResult.freeRef());
}
@Override
public boolean isAlive() {
for (@Nonnull final Result element : inObj)
if (element.isAlive()) {
return true;
}
return false;
}
};
}
@Nonnull
@Override
public JsonObject getJson(Map resources, DataSerializer dataSerializer) {
return super.getJsonStub();
}
@Nonnull
@Override
public List state() {
return Arrays.asList();
}
}