84 lines
3.5 KiB
Java
84 lines
3.5 KiB
Java
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/*******************************************************************************
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* Copyright (c) 2015-2018 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.plot;
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//
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//import lombok.extern.slf4j.Slf4j;
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//import org.deeplearning4j.models.embeddings.inmemory.InMemoryLookupTable;
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//import org.deeplearning4j.models.embeddings.loader.WordVectorSerializer;
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//import org.deeplearning4j.models.word2vec.wordstore.VocabCache;
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//import org.deeplearning4j.nn.conf.WorkspaceMode;
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//import org.junit.jupiter.api.Test;
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//import org.nd4j.linalg.api.buffer.DataBuffer;
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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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//import org.nd4j.linalg.io.ClassPathResource;
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//import org.nd4j.linalg.primitives.Pair;
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//
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//import java.io.File;
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//import java.util.ArrayList;
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//import java.util.List;
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//
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//@Slf4j
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//public class TsneTest {
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//
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// @Test
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// public void testSimple() throws Exception {
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// //Simple sanity check
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//
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// for(WorkspaceMode wsm : new WorkspaceMode[]{WorkspaceMode.NONE, WorkspaceMode.ENABLED}){
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//
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// //STEP 1: Initialization
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// int iterations = 100;
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// //create an n-dimensional array of doubles
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// Nd4j.setDataType(DataType.DOUBLE);
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// List<String> cacheList = new ArrayList<>(); //cacheList is a dynamic array of strings used to hold all words
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//
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// //STEP 2: Turn text input into a list of words
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// log.info("Load & Vectorize data....");
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// File wordFile = new ClassPathResource("deeplearning4j-tsne/words.txt").getFile(); //Open the file
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// //Get the data of all unique word vectors
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// Pair<InMemoryLookupTable,VocabCache> vectors = WordVectorSerializer.loadTxt(wordFile);
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// VocabCache cache = vectors.getSecond();
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// INDArray weights = vectors.getFirst().getSyn0(); //seperate weights of unique words into their own list
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//
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// for(int i = 0; i < cache.numWords(); i++) //seperate strings of words into their own list
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// cacheList.add(cache.wordAtIndex(i));
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//
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// //STEP 3: build a dual-tree tsne to use later
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// log.info("Build model....");
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// BarnesHutTsne tsne = new BarnesHutTsne.Builder()
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// .setMaxIter(iterations).theta(0.5)
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// .normalize(false)
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// .learningRate(500)
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// .useAdaGrad(false)
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// .workspaceMode(wsm)
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// .build();
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//
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// //STEP 4: establish the tsne values and save them to a file
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// log.info("Store TSNE Coordinates for Plotting....");
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// String outputFile = "target/archive-tmp/tsne-standard-coords.csv";
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// (new File(outputFile)).getParentFile().mkdirs();
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//
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// tsne.fit(weights);
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// tsne.saveAsFile(cacheList, outputFile);
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//
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//
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// }
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// }
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//
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//}
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