RL4J: Add TransformProcess, part 1 (#8711)
* Added TransformProcess, part 1 Signed-off-by: unknown <aboulang2002@yahoo.com> * Renamed TemporalMergeTransform to HistoryMergeTransform Signed-off-by: unknown <aboulang2002@yahoo.com> * changed INDArrayHelper to use Nd4j.expandDims Signed-off-by: Alexandre Boulanger <aboulang2002@yahoo.com> * Adjusted copyrights Signed-off-by: unknown <aboulang2002@yahoo.com>
This commit is contained in:
Samuel Audet
GitHub
parent
e4ddf109c3
commit
58aa5a3a9b
@ -26,10 +26,7 @@ import org.datavec.api.transform.schema.Schema;
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*
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* @author Adam Gibson
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*/
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public interface ColumnOp {
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/** Get the output schema for this transformation, given an input schema */
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Schema transform(Schema inputSchema);
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public interface ColumnOp extends Operation<Schema, Schema> {
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/** Set the input schema.
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*/
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@ -1,28 +1,20 @@
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/*******************************************************************************
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* Copyright (c) 2015-2019 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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package org.deeplearning4j.rl4j.observation.preprocessor;
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import org.nd4j.linalg.dataset.api.DataSetPreProcessor;
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/**
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* A base class for all DataSetPreProcessor that must be reset between each MDP sessions (games).
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*
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* @author Alexandre Boulanger
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*/
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public abstract class ResettableDataSetPreProcessor implements DataSetPreProcessor {
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public abstract void reset();
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}
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/*******************************************************************************
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* Copyright (c) 2020 Konduit K.K.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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package org.datavec.api.transform;
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public interface Operation<TIn, TOut> {
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TOut transform(TIn input);
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}
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@ -16,6 +16,7 @@
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package org.datavec.image.transform;
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import org.datavec.api.transform.Operation;
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import org.datavec.image.data.ImageWritable;
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import org.nd4j.shade.jackson.annotation.JsonInclude;
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import org.nd4j.shade.jackson.annotation.JsonTypeInfo;
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@ -29,15 +30,7 @@ import java.util.Random;
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*/
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@JsonInclude(JsonInclude.Include.NON_NULL)
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@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
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public interface ImageTransform {
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/**
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* Takes an image and returns a transformed image.
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*
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* @param image to transform, null == end of stream
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* @return transformed image
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*/
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ImageWritable transform(ImageWritable image);
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public interface ImageTransform extends Operation<ImageWritable, ImageWritable> {
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/**
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* Takes an image and returns a transformed image.
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@ -102,6 +102,13 @@
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<artifactId>gson</artifactId>
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<version>${gson.version}</version>
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</dependency>
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<dependency>
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<groupId>org.datavec</groupId>
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<artifactId>datavec-api</artifactId>
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<version>${datavec.version}</version>
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</dependency>
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</dependencies>
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<profiles>
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@ -1,30 +1,39 @@
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/*******************************************************************************
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* Copyright (c) 2015-2019 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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package org.deeplearning4j.rl4j.observation.preprocessor.pooling;
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import org.nd4j.linalg.api.ndarray.INDArray;
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/**
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* A PoolContentAssembler is used with the PoolingDataSetPreProcessor. This interface defines how the array of INDArray
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* returned by the ObservationPool is packaged into the single INDArray that will be set
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* in the DataSet of PoolingDataSetPreProcessor.preProcess
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*
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* @author Alexandre Boulanger
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*/
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public interface PoolContentAssembler {
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INDArray assemble(INDArray[] poolContent);
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}
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/*******************************************************************************
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* Copyright (c) 2020 Konduit K.K.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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package org.deeplearning4j.rl4j.helper;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.factory.Nd4j;
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/**
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* INDArray helper methods used by RL4J
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*
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* @author Alexandre Boulanger
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*/
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public class INDArrayHelper {
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/**
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* MultiLayerNetwork and ComputationGraph expect the first dimension to be the number of examples in the INDArray.
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* In the case of RL4J, it must be 1. This method will return a INDArray with the correct shape.
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*
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* @param source A INDArray
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* @return The source INDArray with the correct shape
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*/
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public static INDArray forceCorrectShape(INDArray source) {
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return source.shape()[0] == 1
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? source
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: Nd4j.expandDims(source, 0);
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}
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}
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@ -90,7 +90,7 @@ public abstract class AsyncThreadDiscrete<O, NN extends NeuralNet>
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accuReward += stepReply.getReward() * getConf().getRewardFactor();
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//if it's not a skipped frame, you can do a step of training
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if (!obs.isSkipped() || stepReply.isDone()) {
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if (!obs.isSkipped()) {
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INDArray[] output = current.outputAll(obs.getData());
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rewards.add(new MiniTrans(obs.getData(), action, output, accuReward));
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@ -99,7 +99,6 @@ public abstract class AsyncThreadDiscrete<O, NN extends NeuralNet>
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}
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obs = stepReply.getObservation();
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reward += stepReply.getReward();
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incrementStep();
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@ -158,7 +158,7 @@ public abstract class QLearningDiscrete<O extends Encodable> extends QLearning<O
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accuReward += stepReply.getReward() * configuration.getRewardFactor();
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//if it's not a skipped frame, you can do a step of training
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if (!obs.isSkipped() || stepReply.isDone()) {
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if (!obs.isSkipped()) {
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// Add experience
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if(pendingTransition != null) {
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@ -1,130 +0,0 @@
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/*******************************************************************************
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* Copyright (c) 2015-2019 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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package org.deeplearning4j.rl4j.observation.preprocessor;
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import org.deeplearning4j.rl4j.observation.preprocessor.pooling.ChannelStackPoolContentAssembler;
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import org.deeplearning4j.rl4j.observation.preprocessor.pooling.PoolContentAssembler;
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import org.deeplearning4j.rl4j.observation.preprocessor.pooling.CircularFifoObservationPool;
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import org.deeplearning4j.rl4j.observation.preprocessor.pooling.ObservationPool;
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import org.nd4j.base.Preconditions;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.dataset.api.DataSet;
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/**
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* The PoolingDataSetPreProcessor will accumulate features from incoming DataSets and will assemble its content
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* into a DataSet containing a single example.
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*
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* There are two special cases:
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* 1) preProcess will return without doing anything if the input DataSet is empty
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* 2) When the pool has not yet filled, the data from the incoming DataSet is stored in the pool but the DataSet is emptied
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* on exit.
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* <br>
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* The PoolingDataSetPreProcessor requires two sub components: <br>
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* 1) The ObservationPool that supervises what and how input observations are kept. (ex.: Circular FIFO, trailing min/max/avg, etc...)
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* The default is a Circular FIFO.
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* 2) The PoolContentAssembler that will assemble the pool content into a resulting single INDArray. (ex.: stacked along a dimention, squashed into a single observation, etc...)
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* The default is stacking along the dimension 0.
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*
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* @author Alexandre Boulanger
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*/
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public class PoolingDataSetPreProcessor extends ResettableDataSetPreProcessor {
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private final ObservationPool observationPool;
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private final PoolContentAssembler poolContentAssembler;
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protected PoolingDataSetPreProcessor(PoolingDataSetPreProcessor.Builder builder)
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{
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observationPool = builder.observationPool;
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poolContentAssembler = builder.poolContentAssembler;
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}
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/**
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* Note: preProcess will empty the processed dataset if the pool has not filled. Empty datasets should ignored by the
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* Policy/Learning class and other DataSetPreProcessors
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*
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* @param dataSet
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*/
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@Override
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public void preProcess(DataSet dataSet) {
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Preconditions.checkNotNull(dataSet, "Encountered null dataSet");
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if(dataSet.isEmpty()) {
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return;
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}
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Preconditions.checkArgument(dataSet.numExamples() == 1, "Pooling datasets conatining more than one example is not supported");
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// store a duplicate in the pool
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observationPool.add(dataSet.getFeatures().slice(0, 0).dup());
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if(!observationPool.isAtFullCapacity()) {
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dataSet.setFeatures(null);
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return;
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}
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INDArray result = poolContentAssembler.assemble(observationPool.get());
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// return a DataSet containing only 1 example (the result)
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long[] resultShape = result.shape();
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long[] newShape = new long[resultShape.length + 1];
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newShape[0] = 1;
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System.arraycopy(resultShape, 0, newShape, 1, resultShape.length);
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dataSet.setFeatures(result.reshape(newShape));
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}
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public static PoolingDataSetPreProcessor.Builder builder() {
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return new PoolingDataSetPreProcessor.Builder();
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}
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@Override
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public void reset() {
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observationPool.reset();
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}
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public static class Builder {
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private ObservationPool observationPool;
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private PoolContentAssembler poolContentAssembler;
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/**
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* Default is CircularFifoObservationPool
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*/
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public PoolingDataSetPreProcessor.Builder observablePool(ObservationPool observationPool) {
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this.observationPool = observationPool;
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return this;
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}
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/**
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* Default is ChannelStackPoolContentAssembler
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*/
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public PoolingDataSetPreProcessor.Builder poolContentAssembler(PoolContentAssembler poolContentAssembler) {
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this.poolContentAssembler = poolContentAssembler;
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return this;
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}
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public PoolingDataSetPreProcessor build() {
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if(observationPool == null) {
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observationPool = new CircularFifoObservationPool();
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}
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if(poolContentAssembler == null) {
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poolContentAssembler = new ChannelStackPoolContentAssembler();
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}
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return new PoolingDataSetPreProcessor(this);
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}
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}
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}
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@ -1,62 +0,0 @@
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/*******************************************************************************
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* Copyright (c) 2015-2019 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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package org.deeplearning4j.rl4j.observation.preprocessor;
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import lombok.Builder;
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import org.nd4j.base.Preconditions;
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import org.nd4j.linalg.dataset.api.DataSet;
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/**
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* The SkippingDataSetPreProcessor will either do nothing to the input (when not skipped) or will empty
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* the input DataSet when skipping.
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*
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* @author Alexandre Boulanger
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*/
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public class SkippingDataSetPreProcessor extends ResettableDataSetPreProcessor {
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private final int skipFrame;
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private int currentIdx = 0;
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/**
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* @param skipFrame For example, a skipFrame of 4 will skip 3 out of 4 observations.
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*/
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@Builder
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public SkippingDataSetPreProcessor(int skipFrame) {
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Preconditions.checkArgument(skipFrame > 0, "skipFrame must be greater than 0, got %s", skipFrame);
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this.skipFrame = skipFrame;
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}
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@Override
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public void preProcess(DataSet dataSet) {
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Preconditions.checkNotNull(dataSet, "Encountered null dataSet");
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if(dataSet.isEmpty()) {
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return;
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}
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if(currentIdx++ % skipFrame != 0) {
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dataSet.setFeatures(null);
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dataSet.setLabels(null);
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}
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}
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@Override
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public void reset() {
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currentIdx = 0;
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}
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}
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@ -0,0 +1,35 @@
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/*******************************************************************************
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* Copyright (c) 2020 Konduit K.K.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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package org.deeplearning4j.rl4j.observation.transform;
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import java.util.Map;
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/**
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* Used with {@link TransformProcess TransformProcess} to filter-out an observation.
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*
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* @author Alexandre Boulanger
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*/
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public interface FilterOperation {
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/**
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* The logic that determines if the observation should be skipped.
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*
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* @param channelsData the name of the channel
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* @param currentObservationStep The step number if the observation in the current episode.
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* @param isFinalObservation true if this is the last observation of the episode
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* @return true if the observation should be skipped
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*/
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boolean isSkipped(Map<String, Object> channelsData, int currentObservationStep, boolean isFinalObservation);
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}
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@ -1,32 +1,26 @@
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/*******************************************************************************
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* Copyright (c) 2015-2019 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
|
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
|
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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package org.deeplearning4j.rl4j.observation.preprocessor.pooling;
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import org.nd4j.linalg.api.ndarray.INDArray;
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/**
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* ObservationPool is used with the PoolingDataSetPreProcessor. Used to supervise how data from the
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* PoolingDataSetPreProcessor is stored.
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*
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* @author Alexandre Boulanger
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*/
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public interface ObservationPool {
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void add(INDArray observation);
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INDArray[] get();
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boolean isAtFullCapacity();
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void reset();
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}
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/*******************************************************************************
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* Copyright (c) 2020 Konduit K.K.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* 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.
|
||||
*
|
||||
* SPDX-License-Identifier: Apache-2.0
|
||||
******************************************************************************/
|
||||
package org.deeplearning4j.rl4j.observation.transform;
|
||||
|
||||
/**
|
||||
* The {@link TransformProcess TransformProcess} will call reset() (at the start of an episode) of any step that implement this interface.
|
||||
*/
|
||||
public interface ResettableOperation {
|
||||
/**
|
||||
* Called by TransformProcess when an episode starts. See {@link TransformProcess#reset() TransformProcess.reset()}
|
||||
*/
|
||||
void reset();
|
||||
}
|
||||
@ -0,0 +1,45 @@
|
||||
/*******************************************************************************
|
||||
* Copyright (c) 2020 Konduit K.K.
|
||||
*
|
||||
* This program and the accompanying materials are made available under the
|
||||
* terms of the Apache License, Version 2.0 which is available at
|
||||
* https://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.
|
||||
*
|
||||
* SPDX-License-Identifier: Apache-2.0
|
||||
******************************************************************************/
|
||||
package org.deeplearning4j.rl4j.observation.transform.filter;
|
||||
|
||||
import org.deeplearning4j.rl4j.observation.transform.FilterOperation;
|
||||
import org.nd4j.base.Preconditions;
|
||||
import java.util.Map;
|
||||
|
||||
/**
|
||||
* Used with {@link org.deeplearning4j.rl4j.observation.transform.TransformProcess TransformProcess}. Will cause the
|
||||
* transform process to skip a fixed number of frames between non skipped ones.
|
||||
*
|
||||
* @author Alexandre Boulanger
|
||||
*/
|
||||
public class UniformSkippingFilter implements FilterOperation {
|
||||
|
||||
private final int skipFrame;
|
||||
|
||||
/**
|
||||
* @param skipFrame Will cause the filter to keep (not skip) 1 frame every skipFrames.
|
||||
*/
|
||||
public UniformSkippingFilter(int skipFrame) {
|
||||
Preconditions.checkArgument(skipFrame > 0, "skipFrame should be greater than 0");
|
||||
|
||||
this.skipFrame = skipFrame;
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean isSkipped(Map<String, Object> channelsData, int currentObservationStep, boolean isFinalObservation) {
|
||||
return !isFinalObservation && (currentObservationStep % skipFrame != 0);
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,41 @@
|
||||
/*******************************************************************************
|
||||
* Copyright (c) 2020 Konduit K.K.
|
||||
*
|
||||
* This program and the accompanying materials are made available under the
|
||||
* terms of the Apache License, Version 2.0 which is available at
|
||||
* https://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.
|
||||
*
|
||||
* SPDX-License-Identifier: Apache-2.0
|
||||
******************************************************************************/
|
||||
package org.deeplearning4j.rl4j.observation.transform.legacy;
|
||||
|
||||
import org.bytedeco.javacv.OpenCVFrameConverter;
|
||||
import org.bytedeco.opencv.opencv_core.Mat;
|
||||
import org.datavec.api.transform.Operation;
|
||||
import org.datavec.image.data.ImageWritable;
|
||||
import org.deeplearning4j.rl4j.space.Encodable;
|
||||
import org.nd4j.linalg.api.ndarray.INDArray;
|
||||
import org.nd4j.linalg.factory.Nd4j;
|
||||
|
||||
import static org.bytedeco.opencv.global.opencv_core.CV_32FC;
|
||||
|
||||
public class EncodableToINDArrayTransform implements Operation<Encodable, INDArray> {
|
||||
|
||||
private final int[] shape;
|
||||
|
||||
public EncodableToINDArrayTransform(int[] shape) {
|
||||
this.shape = shape;
|
||||
}
|
||||
|
||||
@Override
|
||||
public INDArray transform(Encodable encodable) {
|
||||
return Nd4j.create(encodable.toArray()).reshape(shape);
|
||||
}
|
||||
|
||||
}
|
||||
@ -0,0 +1,48 @@
|
||||
/*******************************************************************************
|
||||
* Copyright (c) 2020 Konduit K.K.
|
||||
*
|
||||
* This program and the accompanying materials are made available under the
|
||||
* terms of the Apache License, Version 2.0 which is available at
|
||||
* https://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.
|
||||
*
|
||||
* SPDX-License-Identifier: Apache-2.0
|
||||
******************************************************************************/
|
||||
package org.deeplearning4j.rl4j.observation.transform.legacy;
|
||||
|
||||
import org.bytedeco.javacv.OpenCVFrameConverter;
|
||||
import org.bytedeco.opencv.opencv_core.Mat;
|
||||
import org.datavec.api.transform.Operation;
|
||||
import org.datavec.image.data.ImageWritable;
|
||||
import org.deeplearning4j.rl4j.space.Encodable;
|
||||
import org.nd4j.linalg.api.ndarray.INDArray;
|
||||
import org.nd4j.linalg.factory.Nd4j;
|
||||
|
||||
import static org.bytedeco.opencv.global.opencv_core.CV_32FC;
|
||||
|
||||
public class EncodableToImageWriteableTransform implements Operation<Encodable, ImageWritable> {
|
||||
|
||||
private final OpenCVFrameConverter.ToMat converter = new OpenCVFrameConverter.ToMat();
|
||||
private final int height;
|
||||
private final int width;
|
||||
private final int colorChannels;
|
||||
|
||||
public EncodableToImageWriteableTransform(int height, int width, int colorChannels) {
|
||||
this.height = height;
|
||||
this.width = width;
|
||||
this.colorChannels = colorChannels;
|
||||
}
|
||||
|
||||
@Override
|
||||
public ImageWritable transform(Encodable encodable) {
|
||||
INDArray indArray = Nd4j.create((encodable).toArray()).reshape(height, width, colorChannels);
|
||||
Mat mat = new Mat(height, width, CV_32FC(3), indArray.data().pointer());
|
||||
return new ImageWritable(converter.convert(mat));
|
||||
}
|
||||
|
||||
}
|
||||
@ -0,0 +1,37 @@
|
||||
package org.deeplearning4j.rl4j.observation.transform.legacy;
|
||||
|
||||
import org.datavec.api.transform.Operation;
|
||||
import org.datavec.image.data.ImageWritable;
|
||||
import org.datavec.image.loader.NativeImageLoader;
|
||||
import org.deeplearning4j.rl4j.space.Encodable;
|
||||
import org.nd4j.linalg.api.buffer.DataType;
|
||||
import org.nd4j.linalg.api.ndarray.INDArray;
|
||||
import org.nd4j.linalg.factory.Nd4j;
|
||||
|
||||
import java.io.IOException;
|
||||
|
||||
public class ImageWriteableToINDArrayTransform implements Operation<ImageWritable, INDArray> {
|
||||
|
||||
private final int height;
|
||||
private final int width;
|
||||
private final NativeImageLoader loader;
|
||||
|
||||
public ImageWriteableToINDArrayTransform(int height, int width) {
|
||||
this.height = height;
|
||||
this.width = width;
|
||||
this.loader = new NativeImageLoader(height, width);
|
||||
}
|
||||
|
||||
@Override
|
||||
public INDArray transform(ImageWritable imageWritable) {
|
||||
INDArray out = null;
|
||||
try {
|
||||
out = loader.asMatrix(imageWritable);
|
||||
} catch (IOException e) {
|
||||
e.printStackTrace();
|
||||
}
|
||||
out = out.reshape(1, height, width);
|
||||
INDArray compressed = out.castTo(DataType.UINT8);
|
||||
return compressed;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,147 @@
|
||||
/*******************************************************************************
|
||||
* Copyright (c) 2020 Konduit K.K.
|
||||
*
|
||||
* This program and the accompanying materials are made available under the
|
||||
* terms of the Apache License, Version 2.0 which is available at
|
||||
* https://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.
|
||||
*
|
||||
* SPDX-License-Identifier: Apache-2.0
|
||||
******************************************************************************/
|
||||
package org.deeplearning4j.rl4j.observation.transform.operation;
|
||||
|
||||
import org.datavec.api.transform.Operation;
|
||||
import org.deeplearning4j.rl4j.helper.INDArrayHelper;
|
||||
import org.deeplearning4j.rl4j.observation.transform.ResettableOperation;
|
||||
import org.deeplearning4j.rl4j.observation.transform.operation.historymerge.CircularFifoStore;
|
||||
import org.deeplearning4j.rl4j.observation.transform.operation.historymerge.HistoryMergeAssembler;
|
||||
import org.deeplearning4j.rl4j.observation.transform.operation.historymerge.HistoryMergeElementStore;
|
||||
import org.deeplearning4j.rl4j.observation.transform.operation.historymerge.HistoryStackAssembler;
|
||||
import org.nd4j.linalg.api.ndarray.INDArray;
|
||||
|
||||
/**
|
||||
* The HistoryMergeTransform will accumulate features from incoming INDArrays and will assemble its content
|
||||
* into a new INDArray containing a single example.
|
||||
*
|
||||
* This is used in scenarios where motion in an important element.
|
||||
*
|
||||
* There is a special case:
|
||||
* * When the store is not full (not ready), the data from the incoming INDArray is stored but null is returned (will be interpreted as a skipped observation)
|
||||
* <br>
|
||||
* The HistoryMergeTransform requires two sub components: <br>
|
||||
* 1) The {@link HistoryMergeElementStore HistoryMergeElementStore} that supervises what and how input INDArrays are kept. (ex.: Circular FIFO, trailing min/max/avg, etc...)
|
||||
* The default is a Circular FIFO.
|
||||
* 2) The {@link HistoryMergeAssembler HistoryMergeAssembler} that will assemble the store content into a resulting single INDArray. (ex.: stacked along a dimension, squashed into a single observation, etc...)
|
||||
* The default is stacking along the dimension 0.
|
||||
*
|
||||
* @author Alexandre Boulanger
|
||||
*/
|
||||
public class HistoryMergeTransform implements Operation<INDArray, INDArray>, ResettableOperation {
|
||||
|
||||
private final HistoryMergeElementStore historyMergeElementStore;
|
||||
private final HistoryMergeAssembler historyMergeAssembler;
|
||||
private final boolean shouldStoreCopy;
|
||||
private final boolean isFirstDimenstionBatch;
|
||||
|
||||
private HistoryMergeTransform(Builder builder) {
|
||||
this.historyMergeElementStore = builder.historyMergeElementStore;
|
||||
this.historyMergeAssembler = builder.historyMergeAssembler;
|
||||
this.shouldStoreCopy = builder.shouldStoreCopy;
|
||||
this.isFirstDimenstionBatch = builder.isFirstDimenstionBatch;
|
||||
}
|
||||
|
||||
@Override
|
||||
public INDArray transform(INDArray input) {
|
||||
INDArray element;
|
||||
if(isFirstDimenstionBatch) {
|
||||
element = input.slice(0, 0);
|
||||
}
|
||||
else {
|
||||
element = input;
|
||||
}
|
||||
|
||||
if(shouldStoreCopy) {
|
||||
element = element.dup();
|
||||
}
|
||||
|
||||
historyMergeElementStore.add(element);
|
||||
if(!historyMergeElementStore.isReady()) {
|
||||
return null;
|
||||
}
|
||||
|
||||
INDArray result = historyMergeAssembler.assemble(historyMergeElementStore.get());
|
||||
|
||||
return INDArrayHelper.forceCorrectShape(result);
|
||||
}
|
||||
|
||||
@Override
|
||||
public void reset() {
|
||||
historyMergeElementStore.reset();
|
||||
}
|
||||
|
||||
public static Builder builder() {
|
||||
return new Builder();
|
||||
}
|
||||
|
||||
public static class Builder {
|
||||
private HistoryMergeElementStore historyMergeElementStore;
|
||||
private HistoryMergeAssembler historyMergeAssembler;
|
||||
private boolean shouldStoreCopy = false;
|
||||
private boolean isFirstDimenstionBatch = false;
|
||||
|
||||
/**
|
||||
* Default is {@link CircularFifoStore CircularFifoStore}
|
||||
*/
|
||||
public Builder elementStore(HistoryMergeElementStore store) {
|
||||
this.historyMergeElementStore = store;
|
||||
return this;
|
||||
}
|
||||
|
||||
/**
|
||||
* Default is {@link HistoryStackAssembler HistoryStackAssembler}
|
||||
*/
|
||||
public Builder assembler(HistoryMergeAssembler assembler) {
|
||||
this.historyMergeAssembler = assembler;
|
||||
return this;
|
||||
}
|
||||
|
||||
/**
|
||||
* If true, tell the HistoryMergeTransform to store copies of incoming INDArrays.
|
||||
* (To prevent later in-place changes to a stored INDArray from changing what has been stored)
|
||||
*
|
||||
* Default is false
|
||||
*/
|
||||
public Builder shouldStoreCopy(boolean shouldStoreCopy) {
|
||||
this.shouldStoreCopy = shouldStoreCopy;
|
||||
return this;
|
||||
}
|
||||
|
||||
/**
|
||||
* If true, tell the HistoryMergeTransform that the first dimension of the input INDArray is the batch count.
|
||||
* When this is the case, the HistoryMergeTransform will slice the input like this [batch, height, width] -> [height, width]
|
||||
*
|
||||
* Default is false
|
||||
*/
|
||||
public Builder isFirstDimenstionBatch(boolean isFirstDimenstionBatch) {
|
||||
this.isFirstDimenstionBatch = isFirstDimenstionBatch;
|
||||
return this;
|
||||
}
|
||||
|
||||
public HistoryMergeTransform build() {
|
||||
if(historyMergeElementStore == null) {
|
||||
historyMergeElementStore = new CircularFifoStore();
|
||||
}
|
||||
|
||||
if(historyMergeAssembler == null) {
|
||||
historyMergeAssembler = new HistoryStackAssembler();
|
||||
}
|
||||
|
||||
return new HistoryMergeTransform(this);
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -1,95 +1,82 @@
|
||||
/*******************************************************************************
|
||||
* Copyright (c) 2015-2019 Skymind, Inc.
|
||||
*
|
||||
* This program and the accompanying materials are made available under the
|
||||
* terms of the Apache License, Version 2.0 which is available at
|
||||
* https://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.
|
||||
*
|
||||
* SPDX-License-Identifier: Apache-2.0
|
||||
******************************************************************************/
|
||||
|
||||
package org.deeplearning4j.rl4j.observation.preprocessor.pooling;
|
||||
|
||||
import org.apache.commons.collections4.queue.CircularFifoQueue;
|
||||
import org.nd4j.base.Preconditions;
|
||||
import org.nd4j.linalg.api.ndarray.INDArray;
|
||||
import org.nd4j.linalg.factory.Nd4j;
|
||||
|
||||
/**
|
||||
* CircularFifoObservationPool is used with the PoolingDataSetPreProcessor. This pool is a first-in first-out queue
|
||||
* with a fixed size that replaces its oldest element if full.
|
||||
*
|
||||
* @author Alexandre Boulanger
|
||||
*/
|
||||
public class CircularFifoObservationPool implements ObservationPool {
|
||||
private static final int DEFAULT_POOL_SIZE = 4;
|
||||
|
||||
private final CircularFifoQueue<INDArray> queue;
|
||||
|
||||
private CircularFifoObservationPool(Builder builder) {
|
||||
queue = new CircularFifoQueue<>(builder.poolSize);
|
||||
}
|
||||
|
||||
public CircularFifoObservationPool()
|
||||
{
|
||||
this(DEFAULT_POOL_SIZE);
|
||||
}
|
||||
|
||||
public CircularFifoObservationPool(int poolSize)
|
||||
{
|
||||
Preconditions.checkArgument(poolSize > 0, "The pool size must be at least 1, got %s", poolSize);
|
||||
queue = new CircularFifoQueue<>(poolSize);
|
||||
}
|
||||
|
||||
/**
|
||||
* Add an element to the pool, if this addition would make the pool to overflow, the added element replaces the oldest one.
|
||||
* @param elem
|
||||
*/
|
||||
public void add(INDArray elem) {
|
||||
queue.add(elem);
|
||||
}
|
||||
|
||||
/**
|
||||
* @return The content of the pool, returned in order from oldest to newest.
|
||||
*/
|
||||
public INDArray[] get() {
|
||||
int size = queue.size();
|
||||
INDArray[] array = new INDArray[size];
|
||||
for (int i = 0; i < size; ++i) {
|
||||
array[i] = queue.get(i).castTo(Nd4j.dataType());
|
||||
}
|
||||
return array;
|
||||
}
|
||||
|
||||
public boolean isAtFullCapacity() {
|
||||
return queue.isAtFullCapacity();
|
||||
}
|
||||
|
||||
@Override
|
||||
public void reset() {
|
||||
queue.clear();
|
||||
}
|
||||
|
||||
public static Builder builder() {
|
||||
return new Builder();
|
||||
}
|
||||
|
||||
public static class Builder {
|
||||
private int poolSize = DEFAULT_POOL_SIZE;
|
||||
|
||||
public Builder poolSize(int poolSize) {
|
||||
this.poolSize = poolSize;
|
||||
return this;
|
||||
}
|
||||
|
||||
public CircularFifoObservationPool build() {
|
||||
return new CircularFifoObservationPool(this);
|
||||
}
|
||||
}
|
||||
}
|
||||
/*******************************************************************************
|
||||
* Copyright (c) 2020 Konduit K.K.
|
||||
*
|
||||
* This program and the accompanying materials are made available under the
|
||||
* terms of the Apache License, Version 2.0 which is available at
|
||||
* https://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.
|
||||
*
|
||||
* SPDX-License-Identifier: Apache-2.0
|
||||
******************************************************************************/
|
||||
package org.deeplearning4j.rl4j.observation.transform.operation.historymerge;
|
||||
|
||||
import org.apache.commons.collections4.queue.CircularFifoQueue;
|
||||
import org.deeplearning4j.rl4j.observation.transform.operation.HistoryMergeTransform;
|
||||
import org.nd4j.base.Preconditions;
|
||||
import org.nd4j.linalg.api.ndarray.INDArray;
|
||||
import org.nd4j.linalg.factory.Nd4j;
|
||||
|
||||
/**
|
||||
* CircularFifoStore is used with the {@link HistoryMergeTransform HistoryMergeTransform}. This store is a first-in first-out queue
|
||||
* with a fixed size that replaces its oldest element if full.
|
||||
*
|
||||
* @author Alexandre Boulanger
|
||||
*/
|
||||
public class CircularFifoStore implements HistoryMergeElementStore {
|
||||
private static final int DEFAULT_STORE_SIZE = 4;
|
||||
|
||||
private final CircularFifoQueue<INDArray> queue;
|
||||
|
||||
public CircularFifoStore() {
|
||||
this(DEFAULT_STORE_SIZE);
|
||||
}
|
||||
|
||||
public CircularFifoStore(int size) {
|
||||
Preconditions.checkArgument(size > 0, "The size must be at least 1, got %s", size);
|
||||
queue = new CircularFifoQueue<>(size);
|
||||
}
|
||||
|
||||
/**
|
||||
* Add an element to the store, if this addition would make the store to overflow, the new element replaces the oldest.
|
||||
* @param elem
|
||||
*/
|
||||
@Override
|
||||
public void add(INDArray elem) {
|
||||
queue.add(elem);
|
||||
}
|
||||
|
||||
/**
|
||||
* @return The content of the store, returned in order from oldest to newest.
|
||||
*/
|
||||
@Override
|
||||
public INDArray[] get() {
|
||||
int size = queue.size();
|
||||
INDArray[] array = new INDArray[size];
|
||||
for (int i = 0; i < size; ++i) {
|
||||
array[i] = queue.get(i).castTo(Nd4j.dataType());
|
||||
}
|
||||
return array;
|
||||
}
|
||||
|
||||
/**
|
||||
* The CircularFifoStore needs to be completely filled before being ready.
|
||||
* @return false when the number of elements in the store is less than the store capacity (default is 4)
|
||||
*/
|
||||
@Override
|
||||
public boolean isReady() {
|
||||
return queue.isAtFullCapacity();
|
||||
}
|
||||
|
||||
/**
|
||||
* Clears the store.
|
||||
*/
|
||||
@Override
|
||||
public void reset() {
|
||||
queue.clear();
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,35 @@
|
||||
/*******************************************************************************
|
||||
* Copyright (c) 2020 Konduit K.K.
|
||||
*
|
||||
* This program and the accompanying materials are made available under the
|
||||
* terms of the Apache License, Version 2.0 which is available at
|
||||
* https://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.
|
||||
*
|
||||
* SPDX-License-Identifier: Apache-2.0
|
||||
******************************************************************************/
|
||||
package org.deeplearning4j.rl4j.observation.transform.operation.historymerge;
|
||||
|
||||
import org.deeplearning4j.rl4j.observation.transform.operation.HistoryMergeTransform;
|
||||
import org.nd4j.linalg.api.ndarray.INDArray;
|
||||
|
||||
/**
|
||||
* A HistoryMergeAssembler is used with the {@link HistoryMergeTransform HistoryMergeTransform}. This interface defines how the array of INDArray
|
||||
* given by the {@link HistoryMergeElementStore HistoryMergeElementStore} is packaged into the single INDArray that will be
|
||||
* returned by the HistoryMergeTransform
|
||||
*
|
||||
* @author Alexandre Boulanger
|
||||
*/
|
||||
public interface HistoryMergeAssembler {
|
||||
/**
|
||||
* Assemble an array of INDArray into a single INArray
|
||||
* @param elements The input INDArray[]
|
||||
* @return the assembled INDArray
|
||||
*/
|
||||
INDArray assemble(INDArray[] elements);
|
||||
}
|
||||
@ -0,0 +1,51 @@
|
||||
/*******************************************************************************
|
||||
* Copyright (c) 2020 Konduit K.K.
|
||||
*
|
||||
* This program and the accompanying materials are made available under the
|
||||
* terms of the Apache License, Version 2.0 which is available at
|
||||
* https://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.
|
||||
*
|
||||
* SPDX-License-Identifier: Apache-2.0
|
||||
******************************************************************************/
|
||||
package org.deeplearning4j.rl4j.observation.transform.operation.historymerge;
|
||||
|
||||
import org.deeplearning4j.rl4j.observation.transform.operation.HistoryMergeTransform;
|
||||
import org.nd4j.linalg.api.ndarray.INDArray;
|
||||
|
||||
/**
|
||||
* HistoryMergeElementStore is used with the {@link HistoryMergeTransform HistoryMergeTransform}. Used to supervise how data from the
|
||||
* HistoryMergeTransform is stored.
|
||||
*
|
||||
* @author Alexandre Boulanger
|
||||
*/
|
||||
public interface HistoryMergeElementStore {
|
||||
/**
|
||||
* Add an element into the store
|
||||
* @param observation
|
||||
*/
|
||||
void add(INDArray observation);
|
||||
|
||||
/**
|
||||
* Get the content of the store
|
||||
* @return the content of the store
|
||||
*/
|
||||
INDArray[] get();
|
||||
|
||||
/**
|
||||
* Used to tell the HistoryMergeTransform that the store is ready. The HistoryMergeTransform will tell the {@link org.deeplearning4j.rl4j.observation.transform.TransformProcess TransformProcess}
|
||||
* to skip the observation is the store is not ready.
|
||||
* @return true if the store is ready
|
||||
*/
|
||||
boolean isReady();
|
||||
|
||||
/**
|
||||
* Resets the store to an initial state.
|
||||
*/
|
||||
void reset();
|
||||
}
|
||||
@ -1,53 +1,52 @@
|
||||
/*******************************************************************************
|
||||
* Copyright (c) 2015-2019 Skymind, Inc.
|
||||
*
|
||||
* This program and the accompanying materials are made available under the
|
||||
* terms of the Apache License, Version 2.0 which is available at
|
||||
* https://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.
|
||||
*
|
||||
* SPDX-License-Identifier: Apache-2.0
|
||||
******************************************************************************/
|
||||
|
||||
package org.deeplearning4j.rl4j.observation.preprocessor.pooling;
|
||||
|
||||
import org.nd4j.linalg.api.ndarray.INDArray;
|
||||
import org.nd4j.linalg.factory.Nd4j;
|
||||
|
||||
/**
|
||||
* ChannelStackPoolContentAssembler is used with the PoolingDataSetPreProcessor. This assembler will
|
||||
* stack along the dimension 0. For example if the pool elements are of shape [ Height, Width ]
|
||||
* the output will be of shape [ Stacked, Height, Width ]
|
||||
*
|
||||
* @author Alexandre Boulanger
|
||||
*/
|
||||
public class ChannelStackPoolContentAssembler implements PoolContentAssembler {
|
||||
|
||||
/**
|
||||
* Will return a new INDArray with one more dimension and with poolContent stacked along dimension 0.
|
||||
*
|
||||
* @param poolContent Array of INDArray
|
||||
* @return A new INDArray with 1 more dimension than the input elements
|
||||
*/
|
||||
@Override
|
||||
public INDArray assemble(INDArray[] poolContent)
|
||||
{
|
||||
// build the new shape
|
||||
long[] elementShape = poolContent[0].shape();
|
||||
long[] newShape = new long[elementShape.length + 1];
|
||||
newShape[0] = poolContent.length;
|
||||
System.arraycopy(elementShape, 0, newShape, 1, elementShape.length);
|
||||
|
||||
// put pool elements in result
|
||||
INDArray result = Nd4j.create(newShape);
|
||||
for(int i = 0; i < poolContent.length; ++i) {
|
||||
result.putRow(i, poolContent[i]);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
}
|
||||
/*******************************************************************************
|
||||
* Copyright (c) 2020 Konduit K.K.
|
||||
*
|
||||
* This program and the accompanying materials are made available under the
|
||||
* terms of the Apache License, Version 2.0 which is available at
|
||||
* https://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.
|
||||
*
|
||||
* SPDX-License-Identifier: Apache-2.0
|
||||
******************************************************************************/
|
||||
|
||||
package org.deeplearning4j.rl4j.observation.transform.operation.historymerge;
|
||||
|
||||
import org.nd4j.linalg.api.ndarray.INDArray;
|
||||
import org.nd4j.linalg.factory.Nd4j;
|
||||
|
||||
/**
|
||||
* HistoryStackAssembler is used with the HistoryMergeTransform. This assembler will
|
||||
* stack along the dimension 0. For example if the store elements are of shape [ Height, Width ]
|
||||
* the output will be of shape [ Stacked, Height, Width ]
|
||||
*
|
||||
* @author Alexandre Boulanger
|
||||
*/
|
||||
public class HistoryStackAssembler implements HistoryMergeAssembler {
|
||||
|
||||
/**
|
||||
* Will return a new INDArray with one more dimension and with elements stacked along dimension 0.
|
||||
*
|
||||
* @param elements Array of INDArray
|
||||
* @return A new INDArray with 1 more dimension than the input elements
|
||||
*/
|
||||
@Override
|
||||
public INDArray assemble(INDArray[] elements) {
|
||||
// build the new shape
|
||||
long[] elementShape = elements[0].shape();
|
||||
long[] newShape = new long[elementShape.length + 1];
|
||||
newShape[0] = elements.length;
|
||||
System.arraycopy(elementShape, 0, newShape, 1, elementShape.length);
|
||||
|
||||
// stack the elements in result on the dimension 0
|
||||
INDArray result = Nd4j.create(newShape);
|
||||
for(int i = 0; i < elements.length; ++i) {
|
||||
result.putRow(i, elements[i]);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
}
|
||||
@ -89,7 +89,7 @@ public abstract class Policy<O, A> implements IPolicy<O, A> {
|
||||
getNeuralNet().reset();
|
||||
}
|
||||
|
||||
private <AS extends ActionSpace<A>> Learning.InitMdp<Observation> refacInitMdp(LegacyMDPWrapper<O, A, AS> mdpWrapper, IHistoryProcessor hp, RefacEpochStepCounter epochStepCounter) {
|
||||
protected <AS extends ActionSpace<A>> Learning.InitMdp<Observation> refacInitMdp(LegacyMDPWrapper<O, A, AS> mdpWrapper, IHistoryProcessor hp, RefacEpochStepCounter epochStepCounter) {
|
||||
epochStepCounter.setCurrentEpochStep(0);
|
||||
|
||||
double reward = 0;
|
||||
|
||||
@ -0,0 +1,38 @@
|
||||
package org.deeplearning4j.rl4j.helper;
|
||||
|
||||
import org.junit.Test;
|
||||
import org.nd4j.linalg.api.ndarray.INDArray;
|
||||
import org.nd4j.linalg.factory.Nd4j;
|
||||
|
||||
import static org.junit.Assert.*;
|
||||
|
||||
public class INDArrayHelperTest {
|
||||
@Test
|
||||
public void when_inputHasIncorrectShape_expect_outputWithCorrectShape() {
|
||||
// Arrange
|
||||
INDArray input = Nd4j.create(new double[] { 1.0, 2.0, 3.0});
|
||||
|
||||
// Act
|
||||
INDArray output = INDArrayHelper.forceCorrectShape(input);
|
||||
|
||||
// Assert
|
||||
assertEquals(2, output.shape().length);
|
||||
assertEquals(1, output.shape()[0]);
|
||||
assertEquals(3, output.shape()[1]);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_inputHasCorrectShape_expect_outputWithSameShape() {
|
||||
// Arrange
|
||||
INDArray input = Nd4j.create(new double[] { 1.0, 2.0, 3.0}).reshape(1, 3);
|
||||
|
||||
// Act
|
||||
INDArray output = INDArrayHelper.forceCorrectShape(input);
|
||||
|
||||
// Assert
|
||||
assertEquals(2, output.shape().length);
|
||||
assertEquals(1, output.shape()[0]);
|
||||
assertEquals(3, output.shape()[1]);
|
||||
}
|
||||
|
||||
}
|
||||
@ -1,164 +0,0 @@
|
||||
package org.deeplearning4j.rl4j.observation.preprocessor;
|
||||
|
||||
import org.deeplearning4j.rl4j.observation.preprocessor.pooling.ObservationPool;
|
||||
import org.deeplearning4j.rl4j.observation.preprocessor.pooling.PoolContentAssembler;
|
||||
import org.junit.Assert;
|
||||
import org.junit.Test;
|
||||
import org.nd4j.linalg.api.ndarray.INDArray;
|
||||
import org.nd4j.linalg.dataset.DataSet;
|
||||
import org.nd4j.linalg.factory.Nd4j;
|
||||
|
||||
import static junit.framework.TestCase.assertTrue;
|
||||
import static org.junit.Assert.assertEquals;
|
||||
|
||||
public class PoolingDataSetPreProcessorTest {
|
||||
|
||||
@Test(expected = NullPointerException.class)
|
||||
public void when_dataSetIsNull_expect_NullPointerException() {
|
||||
// Assemble
|
||||
PoolingDataSetPreProcessor sut = PoolingDataSetPreProcessor.builder().build();
|
||||
|
||||
// Act
|
||||
sut.preProcess(null);
|
||||
}
|
||||
|
||||
@Test(expected = IllegalArgumentException.class)
|
||||
public void when_dataSetHasMoreThanOneExample_expect_IllegalArgumentException() {
|
||||
// Assemble
|
||||
PoolingDataSetPreProcessor sut = PoolingDataSetPreProcessor.builder().build();
|
||||
|
||||
// Act
|
||||
sut.preProcess(new DataSet(Nd4j.rand(new long[] { 2, 2, 2 }), null));
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_dataSetIsEmpty_expect_EmptyDataSet() {
|
||||
// Assemble
|
||||
PoolingDataSetPreProcessor sut = PoolingDataSetPreProcessor.builder().build();
|
||||
DataSet ds = new DataSet(null, null);
|
||||
|
||||
// Act
|
||||
sut.preProcess(ds);
|
||||
|
||||
// Assert
|
||||
Assert.assertTrue(ds.isEmpty());
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_builderHasNoPoolOrAssembler_expect_defaultPoolBehavior() {
|
||||
// Arrange
|
||||
PoolingDataSetPreProcessor sut = PoolingDataSetPreProcessor.builder().build();
|
||||
DataSet[] observations = new DataSet[5];
|
||||
INDArray[] inputs = new INDArray[5];
|
||||
|
||||
|
||||
// Act
|
||||
for(int i = 0; i < 5; ++i) {
|
||||
inputs[i] = Nd4j.rand(new long[] { 1, 2, 2 });
|
||||
DataSet input = new DataSet(inputs[i], null);
|
||||
sut.preProcess(input);
|
||||
observations[i] = input;
|
||||
}
|
||||
|
||||
// Assert
|
||||
assertTrue(observations[0].isEmpty());
|
||||
assertTrue(observations[1].isEmpty());
|
||||
assertTrue(observations[2].isEmpty());
|
||||
|
||||
for(int i = 0; i < 4; ++i) {
|
||||
assertEquals(inputs[i].getDouble(new int[] { 0, 0, 0 }), observations[3].getFeatures().getDouble(new int[] { 0, i, 0, 0 }), 0.0001);
|
||||
assertEquals(inputs[i].getDouble(new int[] { 0, 0, 1 }), observations[3].getFeatures().getDouble(new int[] { 0, i, 0, 1 }), 0.0001);
|
||||
assertEquals(inputs[i].getDouble(new int[] { 0, 1, 0 }), observations[3].getFeatures().getDouble(new int[] { 0, i, 1, 0 }), 0.0001);
|
||||
assertEquals(inputs[i].getDouble(new int[] { 0, 1, 1 }), observations[3].getFeatures().getDouble(new int[] { 0, i, 1, 1 }), 0.0001);
|
||||
}
|
||||
|
||||
for(int i = 0; i < 4; ++i) {
|
||||
assertEquals(inputs[i+1].getDouble(new int[] { 0, 0, 0 }), observations[4].getFeatures().getDouble(new int[] { 0, i, 0, 0 }), 0.0001);
|
||||
assertEquals(inputs[i+1].getDouble(new int[] { 0, 0, 1 }), observations[4].getFeatures().getDouble(new int[] { 0, i, 0, 1 }), 0.0001);
|
||||
assertEquals(inputs[i+1].getDouble(new int[] { 0, 1, 0 }), observations[4].getFeatures().getDouble(new int[] { 0, i, 1, 0 }), 0.0001);
|
||||
assertEquals(inputs[i+1].getDouble(new int[] { 0, 1, 1 }), observations[4].getFeatures().getDouble(new int[] { 0, i, 1, 1 }), 0.0001);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_builderHasPoolAndAssembler_expect_paramPoolAndAssemblerAreUsed() {
|
||||
// Arrange
|
||||
INDArray input = Nd4j.rand(1, 1);
|
||||
TestObservationPool pool = new TestObservationPool();
|
||||
TestPoolContentAssembler assembler = new TestPoolContentAssembler();
|
||||
PoolingDataSetPreProcessor sut = PoolingDataSetPreProcessor.builder()
|
||||
.observablePool(pool)
|
||||
.poolContentAssembler(assembler)
|
||||
.build();
|
||||
|
||||
// Act
|
||||
sut.preProcess(new DataSet(input, null));
|
||||
|
||||
// Assert
|
||||
assertTrue(pool.isAtFullCapacityCalled);
|
||||
assertTrue(pool.isGetCalled);
|
||||
assertEquals(input.getDouble(0), pool.observation.getDouble(0), 0.0);
|
||||
assertTrue(assembler.assembleIsCalled);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_pastInputChanges_expect_outputNotChanged() {
|
||||
// Arrange
|
||||
INDArray input = Nd4j.zeros(1, 1);
|
||||
TestObservationPool pool = new TestObservationPool();
|
||||
TestPoolContentAssembler assembler = new TestPoolContentAssembler();
|
||||
PoolingDataSetPreProcessor sut = PoolingDataSetPreProcessor.builder()
|
||||
.observablePool(pool)
|
||||
.poolContentAssembler(assembler)
|
||||
.build();
|
||||
|
||||
// Act
|
||||
sut.preProcess(new DataSet(input, null));
|
||||
input.putScalar(0, 0, 1.0);
|
||||
|
||||
// Assert
|
||||
assertEquals(0.0, pool.observation.getDouble(0), 0.0);
|
||||
}
|
||||
|
||||
private static class TestObservationPool implements ObservationPool {
|
||||
|
||||
public INDArray observation;
|
||||
public boolean isGetCalled;
|
||||
public boolean isAtFullCapacityCalled;
|
||||
private boolean isResetCalled;
|
||||
|
||||
@Override
|
||||
public void add(INDArray observation) {
|
||||
this.observation = observation;
|
||||
}
|
||||
|
||||
@Override
|
||||
public INDArray[] get() {
|
||||
isGetCalled = true;
|
||||
return new INDArray[0];
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean isAtFullCapacity() {
|
||||
isAtFullCapacityCalled = true;
|
||||
return true;
|
||||
}
|
||||
|
||||
@Override
|
||||
public void reset() {
|
||||
isResetCalled = true;
|
||||
}
|
||||
}
|
||||
|
||||
private static class TestPoolContentAssembler implements PoolContentAssembler {
|
||||
|
||||
public boolean assembleIsCalled;
|
||||
|
||||
@Override
|
||||
public INDArray assemble(INDArray[] poolContent) {
|
||||
assembleIsCalled = true;
|
||||
return Nd4j.create(1, 1);
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -1,70 +0,0 @@
|
||||
package org.deeplearning4j.rl4j.observation.preprocessor;
|
||||
|
||||
import org.junit.Test;
|
||||
import org.nd4j.linalg.dataset.DataSet;
|
||||
import org.nd4j.linalg.factory.Nd4j;
|
||||
|
||||
import static org.junit.Assert.assertFalse;
|
||||
import static org.junit.Assert.assertTrue;
|
||||
|
||||
public class SkippingDataSetPreProcessorTest {
|
||||
@Test(expected = IllegalArgumentException.class)
|
||||
public void when_ctorSkipFrameIsZero_expect_IllegalArgumentException() {
|
||||
SkippingDataSetPreProcessor sut = new SkippingDataSetPreProcessor(0);
|
||||
}
|
||||
|
||||
@Test(expected = IllegalArgumentException.class)
|
||||
public void when_builderSkipFrameIsZero_expect_IllegalArgumentException() {
|
||||
SkippingDataSetPreProcessor sut = SkippingDataSetPreProcessor.builder()
|
||||
.skipFrame(0)
|
||||
.build();
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_skipFrameIs3_expect_Skip2OutOf3() {
|
||||
// Arrange
|
||||
SkippingDataSetPreProcessor sut = SkippingDataSetPreProcessor.builder()
|
||||
.skipFrame(3)
|
||||
.build();
|
||||
DataSet[] results = new DataSet[4];
|
||||
|
||||
// Act
|
||||
for(int i = 0; i < 4; ++i) {
|
||||
results[i] = new DataSet(Nd4j.create(new double[] { 123.0 }), null);
|
||||
sut.preProcess(results[i]);
|
||||
}
|
||||
|
||||
// Assert
|
||||
assertFalse(results[0].isEmpty());
|
||||
assertTrue(results[1].isEmpty());
|
||||
assertTrue(results[2].isEmpty());
|
||||
assertFalse(results[3].isEmpty());
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_resetIsCalled_expect_skippingIsReset() {
|
||||
// Arrange
|
||||
SkippingDataSetPreProcessor sut = SkippingDataSetPreProcessor.builder()
|
||||
.skipFrame(3)
|
||||
.build();
|
||||
DataSet[] results = new DataSet[4];
|
||||
|
||||
// Act
|
||||
results[0] = new DataSet(Nd4j.create(new double[] { 123.0 }), null);
|
||||
results[1] = new DataSet(Nd4j.create(new double[] { 123.0 }), null);
|
||||
results[2] = new DataSet(Nd4j.create(new double[] { 123.0 }), null);
|
||||
results[3] = new DataSet(Nd4j.create(new double[] { 123.0 }), null);
|
||||
|
||||
sut.preProcess(results[0]);
|
||||
sut.preProcess(results[1]);
|
||||
sut.reset();
|
||||
sut.preProcess(results[2]);
|
||||
sut.preProcess(results[3]);
|
||||
|
||||
// Assert
|
||||
assertFalse(results[0].isEmpty());
|
||||
assertTrue(results[1].isEmpty());
|
||||
assertFalse(results[2].isEmpty());
|
||||
assertTrue(results[3].isEmpty());
|
||||
}
|
||||
}
|
||||
@ -1,41 +0,0 @@
|
||||
package org.deeplearning4j.rl4j.observation.preprocessor.pooling;
|
||||
|
||||
import org.junit.Test;
|
||||
import org.nd4j.linalg.api.ndarray.INDArray;
|
||||
import org.nd4j.linalg.factory.Nd4j;
|
||||
|
||||
import static org.junit.Assert.assertEquals;
|
||||
|
||||
public class ChannelStackPoolContentAssemblerTest {
|
||||
|
||||
@Test
|
||||
public void when_assemble_expect_poolContentStackedOnChannel() {
|
||||
// Assemble
|
||||
ChannelStackPoolContentAssembler sut = new ChannelStackPoolContentAssembler();
|
||||
INDArray[] poolContent = new INDArray[] {
|
||||
Nd4j.rand(2, 2),
|
||||
Nd4j.rand(2, 2),
|
||||
};
|
||||
|
||||
// Act
|
||||
INDArray result = sut.assemble(poolContent);
|
||||
|
||||
// Assert
|
||||
assertEquals(3, result.shape().length);
|
||||
assertEquals(2, result.shape()[0]);
|
||||
assertEquals(2, result.shape()[1]);
|
||||
assertEquals(2, result.shape()[2]);
|
||||
|
||||
assertEquals(poolContent[0].getDouble(0, 0), result.getDouble(0, 0, 0), 0.0001);
|
||||
assertEquals(poolContent[0].getDouble(0, 1), result.getDouble(0, 0, 1), 0.0001);
|
||||
assertEquals(poolContent[0].getDouble(1, 0), result.getDouble(0, 1, 0), 0.0001);
|
||||
assertEquals(poolContent[0].getDouble(1, 1), result.getDouble(0, 1, 1), 0.0001);
|
||||
|
||||
assertEquals(poolContent[1].getDouble(0, 0), result.getDouble(1, 0, 0), 0.0001);
|
||||
assertEquals(poolContent[1].getDouble(0, 1), result.getDouble(1, 0, 1), 0.0001);
|
||||
assertEquals(poolContent[1].getDouble(1, 0), result.getDouble(1, 1, 0), 0.0001);
|
||||
assertEquals(poolContent[1].getDouble(1, 1), result.getDouble(1, 1, 1), 0.0001);
|
||||
|
||||
}
|
||||
|
||||
}
|
||||
@ -1,100 +0,0 @@
|
||||
package org.deeplearning4j.rl4j.observation.preprocessor.pooling;
|
||||
|
||||
import org.junit.Test;
|
||||
import org.nd4j.linalg.api.ndarray.INDArray;
|
||||
import org.nd4j.linalg.factory.Nd4j;
|
||||
|
||||
import static org.junit.Assert.assertEquals;
|
||||
import static org.junit.Assert.assertFalse;
|
||||
import static org.junit.Assert.assertTrue;
|
||||
|
||||
public class CircularFifoObservationPoolTest {
|
||||
|
||||
@Test(expected = IllegalArgumentException.class)
|
||||
public void when_poolSizeZeroOrLess_expect_IllegalArgumentException() {
|
||||
CircularFifoObservationPool sut = new CircularFifoObservationPool(0);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_poolIsEmpty_expect_NotReady() {
|
||||
// Assemble
|
||||
CircularFifoObservationPool sut = new CircularFifoObservationPool();
|
||||
|
||||
// Act
|
||||
boolean isReady = sut.isAtFullCapacity();
|
||||
|
||||
// Assert
|
||||
assertFalse(isReady);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_notEnoughElementsInPool_expect_notReady() {
|
||||
// Assemble
|
||||
CircularFifoObservationPool sut = new CircularFifoObservationPool();
|
||||
sut.add(Nd4j.create(new double[] { 123.0 }));
|
||||
|
||||
// Act
|
||||
boolean isReady = sut.isAtFullCapacity();
|
||||
|
||||
// Assert
|
||||
assertFalse(isReady);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_enoughElementsInPool_expect_ready() {
|
||||
// Assemble
|
||||
CircularFifoObservationPool sut = CircularFifoObservationPool.builder()
|
||||
.poolSize(2)
|
||||
.build();
|
||||
sut.add(Nd4j.createFromArray(123.0));
|
||||
sut.add(Nd4j.createFromArray(123.0));
|
||||
|
||||
// Act
|
||||
boolean isReady = sut.isAtFullCapacity();
|
||||
|
||||
// Assert
|
||||
assertTrue(isReady);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_addMoreThanSize_expect_getReturnOnlyLastElements() {
|
||||
// Assemble
|
||||
CircularFifoObservationPool sut = CircularFifoObservationPool.builder().build();
|
||||
sut.add(Nd4j.createFromArray(0.0));
|
||||
sut.add(Nd4j.createFromArray(1.0));
|
||||
sut.add(Nd4j.createFromArray(2.0));
|
||||
sut.add(Nd4j.createFromArray(3.0));
|
||||
sut.add(Nd4j.createFromArray(4.0));
|
||||
sut.add(Nd4j.createFromArray(5.0));
|
||||
sut.add(Nd4j.createFromArray(6.0));
|
||||
|
||||
// Act
|
||||
INDArray[] result = sut.get();
|
||||
|
||||
// Assert
|
||||
assertEquals(3.0, result[0].getDouble(0), 0.0);
|
||||
assertEquals(4.0, result[1].getDouble(0), 0.0);
|
||||
assertEquals(5.0, result[2].getDouble(0), 0.0);
|
||||
assertEquals(6.0, result[3].getDouble(0), 0.0);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_resetIsCalled_expect_poolContentFlushed() {
|
||||
// Assemble
|
||||
CircularFifoObservationPool sut = CircularFifoObservationPool.builder().build();
|
||||
sut.add(Nd4j.createFromArray(0.0));
|
||||
sut.add(Nd4j.createFromArray(1.0));
|
||||
sut.add(Nd4j.createFromArray(2.0));
|
||||
sut.add(Nd4j.createFromArray(3.0));
|
||||
sut.add(Nd4j.createFromArray(4.0));
|
||||
sut.add(Nd4j.createFromArray(5.0));
|
||||
sut.add(Nd4j.createFromArray(6.0));
|
||||
sut.reset();
|
||||
|
||||
// Act
|
||||
INDArray[] result = sut.get();
|
||||
|
||||
// Assert
|
||||
assertEquals(0, result.length);
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,166 @@
|
||||
package org.deeplearning4j.rl4j.observation.transform.operation;
|
||||
|
||||
import org.deeplearning4j.rl4j.observation.transform.operation.historymerge.HistoryMergeAssembler;
|
||||
import org.deeplearning4j.rl4j.observation.transform.operation.historymerge.HistoryMergeElementStore;
|
||||
import org.junit.Test;
|
||||
import org.nd4j.linalg.api.ndarray.INDArray;
|
||||
import org.nd4j.linalg.factory.Nd4j;
|
||||
|
||||
import static org.junit.Assert.*;
|
||||
|
||||
public class HistoryMergeTransformTest {
|
||||
|
||||
@Test
|
||||
public void when_firstDimensionIsNotBatch_expect_observationAddedAsIs() {
|
||||
// Arrange
|
||||
MockStore store = new MockStore(false);
|
||||
HistoryMergeTransform sut = HistoryMergeTransform.builder()
|
||||
.isFirstDimenstionBatch(false)
|
||||
.elementStore(store)
|
||||
.build();
|
||||
INDArray input = Nd4j.create(new double[] { 1.0, 2.0, 3.0 });
|
||||
|
||||
// Act
|
||||
sut.transform(input);
|
||||
|
||||
// Assert
|
||||
assertEquals(1, store.addedObservation.shape().length);
|
||||
assertEquals(3, store.addedObservation.shape()[0]);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_firstDimensionIsBatch_expect_observationAddedAsSliced() {
|
||||
// Arrange
|
||||
MockStore store = new MockStore(false);
|
||||
HistoryMergeTransform sut = HistoryMergeTransform.builder()
|
||||
.isFirstDimenstionBatch(true)
|
||||
.elementStore(store)
|
||||
.build();
|
||||
INDArray input = Nd4j.create(new double[] { 1.0, 2.0, 3.0 }).reshape(1, 3);
|
||||
|
||||
// Act
|
||||
sut.transform(input);
|
||||
|
||||
// Assert
|
||||
assertEquals(1, store.addedObservation.shape().length);
|
||||
assertEquals(3, store.addedObservation.shape()[0]);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_notReady_expect_resultIsNull() {
|
||||
// Arrange
|
||||
MockStore store = new MockStore(false);
|
||||
HistoryMergeTransform sut = HistoryMergeTransform.builder()
|
||||
.isFirstDimenstionBatch(true)
|
||||
.elementStore(store)
|
||||
.build();
|
||||
INDArray input = Nd4j.create(new double[] { 1.0, 2.0, 3.0 });
|
||||
|
||||
// Act
|
||||
INDArray result = sut.transform(input);
|
||||
|
||||
// Assert
|
||||
assertNull(result);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_notShouldStoreCopy_expect_sameIsStored() {
|
||||
// Arrange
|
||||
MockStore store = new MockStore(false);
|
||||
HistoryMergeTransform sut = HistoryMergeTransform.builder()
|
||||
.shouldStoreCopy(false)
|
||||
.elementStore(store)
|
||||
.build();
|
||||
INDArray input = Nd4j.create(new double[] { 1.0, 2.0, 3.0 });
|
||||
|
||||
// Act
|
||||
INDArray result = sut.transform(input);
|
||||
|
||||
// Assert
|
||||
assertSame(input, store.addedObservation);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_shouldStoreCopy_expect_copyIsStored() {
|
||||
// Arrange
|
||||
MockStore store = new MockStore(true);
|
||||
HistoryMergeTransform sut = HistoryMergeTransform.builder()
|
||||
.shouldStoreCopy(true)
|
||||
.elementStore(store)
|
||||
.build();
|
||||
INDArray input = Nd4j.create(new double[] { 1.0, 2.0, 3.0 });
|
||||
|
||||
// Act
|
||||
INDArray result = sut.transform(input);
|
||||
|
||||
// Assert
|
||||
assertNotSame(input, store.addedObservation);
|
||||
assertEquals(1, store.addedObservation.shape().length);
|
||||
assertEquals(3, store.addedObservation.shape()[0]);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_transformCalled_expect_storeContentAssembledAndOutputHasCorrectShape() {
|
||||
// Arrange
|
||||
MockStore store = new MockStore(true);
|
||||
MockAssemble assemble = new MockAssemble();
|
||||
HistoryMergeTransform sut = HistoryMergeTransform.builder()
|
||||
.elementStore(store)
|
||||
.assembler(assemble)
|
||||
.build();
|
||||
INDArray input = Nd4j.create(new double[] { 1.0, 2.0, 3.0 });
|
||||
|
||||
// Act
|
||||
INDArray result = sut.transform(input);
|
||||
|
||||
// Assert
|
||||
assertEquals(1, assemble.assembleElements.length);
|
||||
assertSame(store.addedObservation, assemble.assembleElements[0]);
|
||||
|
||||
assertEquals(2, result.shape().length);
|
||||
assertEquals(1, result.shape()[0]);
|
||||
assertEquals(3, result.shape()[1]);
|
||||
}
|
||||
|
||||
public static class MockStore implements HistoryMergeElementStore {
|
||||
|
||||
private final boolean isReady;
|
||||
private INDArray addedObservation;
|
||||
|
||||
public MockStore(boolean isReady) {
|
||||
|
||||
this.isReady = isReady;
|
||||
}
|
||||
|
||||
@Override
|
||||
public void add(INDArray observation) {
|
||||
addedObservation = observation;
|
||||
}
|
||||
|
||||
@Override
|
||||
public INDArray[] get() {
|
||||
return new INDArray[] { addedObservation };
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean isReady() {
|
||||
return isReady;
|
||||
}
|
||||
|
||||
@Override
|
||||
public void reset() {
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
public static class MockAssemble implements HistoryMergeAssembler {
|
||||
|
||||
private INDArray[] assembleElements;
|
||||
|
||||
@Override
|
||||
public INDArray assemble(INDArray[] elements) {
|
||||
assembleElements = elements;
|
||||
return elements[0];
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,77 @@
|
||||
package org.deeplearning4j.rl4j.observation.transform.operation.historymerge;
|
||||
|
||||
import org.junit.Test;
|
||||
import org.nd4j.linalg.api.ndarray.INDArray;
|
||||
import org.nd4j.linalg.factory.Nd4j;
|
||||
|
||||
import static org.junit.Assert.*;
|
||||
|
||||
public class CircularFifoStoreTest {
|
||||
|
||||
@Test(expected = IllegalArgumentException.class)
|
||||
public void when_fifoSizeIsLessThan1_expect_exception() {
|
||||
// Arrange
|
||||
CircularFifoStore sut = new CircularFifoStore(0);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_adding2elementsWithSize2_expect_notReadyAfter1stReadyAfter2nd() {
|
||||
// Arrange
|
||||
CircularFifoStore sut = new CircularFifoStore(2);
|
||||
INDArray firstElement = Nd4j.create(new double[] { 1.0, 2.0, 3.0 });
|
||||
INDArray secondElement = Nd4j.create(new double[] { 10.0, 20.0, 30.0 });
|
||||
|
||||
// Act
|
||||
sut.add(firstElement);
|
||||
boolean isReadyAfter1st = sut.isReady();
|
||||
sut.add(secondElement);
|
||||
boolean isReadyAfter2nd = sut.isReady();
|
||||
|
||||
// Assert
|
||||
assertFalse(isReadyAfter1st);
|
||||
assertTrue(isReadyAfter2nd);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_adding2elementsWithSize2_expect_getReturnThese2() {
|
||||
// Arrange
|
||||
CircularFifoStore sut = new CircularFifoStore(2);
|
||||
INDArray firstElement = Nd4j.create(new double[] { 1.0, 2.0, 3.0 });
|
||||
INDArray secondElement = Nd4j.create(new double[] { 10.0, 20.0, 30.0 });
|
||||
|
||||
// Act
|
||||
sut.add(firstElement);
|
||||
sut.add(secondElement);
|
||||
INDArray[] results = sut.get();
|
||||
|
||||
// Assert
|
||||
assertEquals(2, results.length);
|
||||
|
||||
assertEquals(1.0, results[0].getDouble(0), 0.00001);
|
||||
assertEquals(2.0, results[0].getDouble(1), 0.00001);
|
||||
assertEquals(3.0, results[0].getDouble(2), 0.00001);
|
||||
|
||||
assertEquals(10.0, results[1].getDouble(0), 0.00001);
|
||||
assertEquals(20.0, results[1].getDouble(1), 0.00001);
|
||||
assertEquals(30.0, results[1].getDouble(2), 0.00001);
|
||||
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_adding2elementsThenCallingReset_expect_getReturnEmpty() {
|
||||
// Arrange
|
||||
CircularFifoStore sut = new CircularFifoStore(2);
|
||||
INDArray firstElement = Nd4j.create(new double[] { 1.0, 2.0, 3.0 });
|
||||
INDArray secondElement = Nd4j.create(new double[] { 10.0, 20.0, 30.0 });
|
||||
|
||||
// Act
|
||||
sut.add(firstElement);
|
||||
sut.add(secondElement);
|
||||
sut.reset();
|
||||
INDArray[] results = sut.get();
|
||||
|
||||
// Assert
|
||||
assertEquals(0, results.length);
|
||||
}
|
||||
|
||||
}
|
||||
@ -0,0 +1,37 @@
|
||||
package org.deeplearning4j.rl4j.observation.transform.operation.historymerge;
|
||||
|
||||
import org.junit.Test;
|
||||
import org.nd4j.linalg.api.ndarray.INDArray;
|
||||
import org.nd4j.linalg.factory.Nd4j;
|
||||
|
||||
import static org.junit.Assert.*;
|
||||
|
||||
public class HistoryStackAssemblerTest {
|
||||
|
||||
@Test
|
||||
public void when_assembling2INDArrays_expect_stackedAsResult() {
|
||||
// Arrange
|
||||
INDArray[] input = new INDArray[] {
|
||||
Nd4j.create(new double[] { 1.0, 2.0, 3.0 }),
|
||||
Nd4j.create(new double[] { 10.0, 20.0, 30.0 }),
|
||||
};
|
||||
HistoryStackAssembler sut = new HistoryStackAssembler();
|
||||
|
||||
// Act
|
||||
INDArray result = sut.assemble(input);
|
||||
|
||||
// Assert
|
||||
assertEquals(2, result.shape().length);
|
||||
assertEquals(2, result.shape()[0]);
|
||||
assertEquals(3, result.shape()[1]);
|
||||
|
||||
assertEquals(1.0, result.getDouble(0, 0), 0.00001);
|
||||
assertEquals(2.0, result.getDouble(0, 1), 0.00001);
|
||||
assertEquals(3.0, result.getDouble(0, 2), 0.00001);
|
||||
|
||||
assertEquals(10.0, result.getDouble(1, 0), 0.00001);
|
||||
assertEquals(20.0, result.getDouble(1, 1), 0.00001);
|
||||
assertEquals(30.0, result.getDouble(1, 2), 0.00001);
|
||||
|
||||
}
|
||||
}
|
||||
Loading…
x
Reference in New Issue
Block a user