org.deeplearning4j.text.movingwindow.WindowConverter Maven / Gradle / Ivy
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* 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.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);
}
}