org.deeplearning4j.text.movingwindow.WindowConverter Maven / Gradle / Ivy

/*******************************************************************************
 * Copyright (c) 2015-2018 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.text.movingwindow;

import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.models.word2vec.Word2Vec;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.INDArrayIndex;
import org.nd4j.linalg.indexing.NDArrayIndex;

import java.util.List;


/**
 * Util methods for converting windows to 
 * training examples
 * @author Adam Gibson    
 *
 */
@Slf4j
public class WindowConverter {


    private WindowConverter() {}

    /**
     * Converts a window (each word in the window)
     *
     * in to a vector.
     *
     * Keep in mind each window is a multi word context.
     *
     * From there, each word uses the passed in model
     * as a lookup table to get what vectors are relevant
     * to the passed in windows
     * @param window the window to take in.
     * @param vec the model to use as a lookup table
     * @return a concacneated 1 row array
     * containing all of the numbers for each word in the window
     */
    public static INDArray asExampleArray(Window window, Word2Vec vec, boolean normalize) {
        int length = vec.lookupTable().layerSize();
        List words = window.getWords();
        int windowSize = vec.getWindow();
        Preconditions.checkState(words.size() == vec.getWindow());
        INDArray ret = Nd4j.create(1, length * windowSize);



        for (int i = 0; i < words.size(); i++) {
            String word = words.get(i);
            INDArray n = normalize ? vec.getWordVectorMatrixNormalized(word) : vec.getWordVectorMatrix(word);
            ret.put(new INDArrayIndex[] {NDArrayIndex.interval(i * vec.lookupTable().layerSize(),
                            i * vec.lookupTable().layerSize() + vec.lookupTable().layerSize())}, n);
        }

        return ret;
    }



    /**
     * Converts a window (each word in the window)
     *
     * in to a vector.
     *
     * Keep in mind each window is a multi word context.
     *
     * From there, each word uses the passed in model
     * as a lookup table to get what vectors are relevant
     * to the passed in windows
     * @param window the window to take in.
     * @param vec the model to use as a lookup table
     * @return a concatneated 1 row array
     * containing all of the numbers for each word in the window
     */
    public static INDArray asExampleMatrix(Window window, Word2Vec vec) {
        INDArray[] data = new INDArray[window.getWords().size()];
        for (int i = 0; i < data.length; i++) {
            data[i] = vec.getWordVectorMatrix(window.getWord(i));

            // if there's null elements
            if (data[i] == null)
                data[i] = Nd4j.zeros(1, vec.getLayerSize());
        }
        return Nd4j.hstack(data);
    }

}