本文整理了Java中org.nd4j.linalg.factory.Nd4j.hstack()方法的一些代码示例，展示了Nd4j.hstack()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Nd4j.hstack()方法的具体详情如下：
包路径：org.nd4j.linalg.factory.Nd4j
类名称：Nd4j
方法名：hstack

Nd4j.hstack介绍

[英]Concatenates two matrices horizontally. Matrices must have identical numbers of rows.
[中]水平连接两个矩阵。矩阵的行数必须相同。

代码示例

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Adds a feature for each example on to the current feature vector
 *
 * @param toAdd the feature vector to add
 */
@Override
public void addFeatureVector(INDArray toAdd) {
  setFeatures(Nd4j.hstack(getFeatureMatrix(), toAdd));
}

代码示例来源：origin: deeplearning4j/dl4j-examples

INDArray hstack = Nd4j.hstack(ones,zeros);
System.out.println("### HSTACK ####");
System.out.println(hstack);

代码示例来源：origin: deeplearning4j/dl4j-examples

INDArray hStack = Nd4j.hstack(rowVector1, rowVector2);      //Horizontal stack: [1,3]+[1,3] to [1,6]
System.out.println("\n\n\nCreating INDArrays from other INDArrays, using hstack and vstack:");
System.out.println("vStack:\n" + vStack);

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

private INDArray constructParams() {
  //some params will be null for subsampling etc
  INDArray keepView = null;
  for (INDArray aParam : editedParams) {
    if (aParam != null) {
      if (keepView == null) {
        keepView = aParam;
      } else {
        keepView = Nd4j.hstack(keepView, aParam);
      }
    }
  }
  if (!appendParams.isEmpty()) {
    INDArray appendView = Nd4j.hstack(appendParams);
    return Nd4j.hstack(keepView, appendView);
  } else {
    return keepView;
  }
}

代码示例来源：origin: org.nd4j/nd4j-parameter-server-node

@Override
public INDArray getAccumulatedResult() {
  if (aggregationWidth == 1) {
    return chunks.get((short) 0);
  } else
    return Nd4j.hstack(chunks.values());
}

代码示例来源：origin: org.nd4j/nd4j-parameter-server-node_2.11

@Override
public INDArray getAccumulatedResult() {
  if (aggregationWidth == 1) {
    return chunks.get((short) 0);
  } else
    return Nd4j.hstack(chunks.values());
}

代码示例来源：origin: org.nd4j/nd4j-api

/**
 * Adds a feature for each example on to the current feature vector
 *
 * @param toAdd the feature vector to add
 */
@Override
public void addFeatureVector(INDArray toAdd) {
  setFeatures(Nd4j.hstack(getFeatureMatrix(), toAdd));
}

代码示例来源：origin: mccorby/FederatedAndroidTrainer

@Override
public FederatedDataSet getTestData() {
  Random rand = new Random(seed);
  int numSamples = N_SAMPLES/10;
  double[] sum = new double[numSamples];
  double[] input1 = new double[numSamples];
  double[] input2 = new double[numSamples];
  for (int i = 0; i < numSamples; i++) {
    input1[i] = MIN_RANGE + (MAX_RANGE - MIN_RANGE) * rand.nextDouble();
    input2[i] = MIN_RANGE + (MAX_RANGE - MIN_RANGE) * rand.nextDouble();
    sum[i] = input1[i] + input2[i];
  }
  INDArray inputNDArray1 = Nd4j.create(input1, new int[]{numSamples, 1});
  INDArray inputNDArray2 = Nd4j.create(input2, new int[]{numSamples, 1});
  INDArray inputNDArray = Nd4j.hstack(inputNDArray1, inputNDArray2);
  INDArray outPut = Nd4j.create(sum, new int[]{numSamples, 1});
  return new FederatedDataSetImpl(new DataSet(inputNDArray, outPut));
}

代码示例来源：origin: mccorby/FederatedAndroidTrainer

@Override
public FederatedDataSet getTrainingData() {
  Random rand = new Random(seed);
  double[] sum = new double[N_SAMPLES];
  double[] input1 = new double[N_SAMPLES];
  double[] input2 = new double[N_SAMPLES];
  for (int i = 0; i < N_SAMPLES; i++) {
    input1[i] = MIN_RANGE + (MAX_RANGE - MIN_RANGE) * rand.nextDouble();
    input2[i] = MIN_RANGE + (MAX_RANGE - MIN_RANGE) * rand.nextDouble();
    sum[i] = input1[i] + input2[i];
  }
  INDArray inputNDArray1 = Nd4j.create(input1, new int[]{N_SAMPLES, 1});
  INDArray inputNDArray2 = Nd4j.create(input2, new int[]{N_SAMPLES, 1});
  INDArray inputNDArray = Nd4j.hstack(inputNDArray1, inputNDArray2);
  INDArray outPut = Nd4j.create(sum, new int[]{N_SAMPLES, 1});
  DataSet dataSet = new DataSet(inputNDArray, outPut);
  dataSet.shuffle();
  return new FederatedDataSetImpl(dataSet);
}

代码示例来源：origin: neo4j-graph-analytics/ml-models

final INDArray nodeFeatures = Nd4j.hstack(arrays);
embedding.putRow(nodeId, nodeFeatures);

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

out = Nd4j.hstack(inputs);
  break;
case 3:
  out = Nd4j.hstack(inputs);
  out = Nd4j.hstack(inputs);

代码示例来源：origin: sjsdfg/dl4j-tutorials

private static DataSetIterator getTrainingData(int batchSize, Random rand) {
    double [] sum = new double[nSamples];
    double [] input1 = new double[nSamples];
    double [] input2 = new double[nSamples];
    for (int i= 0; i< nSamples; i++) {
      input1[i] = MIN_RANGE + (MAX_RANGE - MIN_RANGE) * rand.nextDouble();
      input2[i] =  MIN_RANGE + (MAX_RANGE - MIN_RANGE) * rand.nextDouble();
      sum[i] = input1[i] + input2[i];
    }
    INDArray inputNDArray1 = Nd4j.create(input1, new int[]{nSamples,1});
    INDArray inputNDArray2 = Nd4j.create(input2, new int[]{nSamples,1});
    INDArray inputNDArray = Nd4j.hstack(inputNDArray1,inputNDArray2);
    INDArray outPut = Nd4j.create(sum, new int[]{nSamples, 1});
    DataSet dataSet = new DataSet(inputNDArray, outPut);
    List<DataSet> listDs = dataSet.asList();

    return new ListDataSetIterator(listDs,batchSize);
  }
}

代码示例来源：origin: neo4j-graph-analytics/ml-models

public Embedding prune(Embedding prevEmbedding, Embedding embedding) {
  INDArray embeddingToPrune = Nd4j.hstack(prevEmbedding.getNDEmbedding(), embedding.getNDEmbedding());
  Feature[] featuresToPrune = ArrayUtils.addAll(prevEmbedding.getFeatures(), embedding.getFeatures());
  progressLogger.log("Feature Pruning: Creating features graph");
  final Graph graph = loadFeaturesGraph(embeddingToPrune, prevEmbedding.features.length);
  progressLogger.log("Feature Pruning: Created features graph");
  progressLogger.log("Feature Pruning: Finding features to keep");
  int[] featureIdsToKeep = findConnectedComponents(graph)
      .collect(Collectors.groupingBy(item -> item.setId))
      .values()
      .stream()
      .mapToInt(results -> results.stream().mapToInt(value -> (int) value.nodeId).min().getAsInt())
      .toArray();
  progressLogger.log("Feature Pruning: Found features to keep");
  progressLogger.log("Feature Pruning: Pruning embeddings");
  INDArray prunedNDEmbedding = pruneEmbedding(embeddingToPrune, featureIdsToKeep);
  progressLogger.log("Feature Pruning: Pruned embeddings");
  Feature[] prunedFeatures = new Feature[featureIdsToKeep.length];
  for (int index = 0; index < featureIdsToKeep.length; index++) {
    prunedFeatures[index] = featuresToPrune[featureIdsToKeep[index]];
  }
  return new Embedding(prunedFeatures, prunedNDEmbedding);
}

代码示例来源：origin: neo4j-graph-analytics/ml-models

@Override
public INDArray ndOp(INDArray features, INDArray adjacencyMatrix) {
  INDArray[] maxes = new INDArray[features.columns()];
  for (int fCol = 0; fCol < features.columns(); fCol++) {
    INDArray mul = adjacencyMatrix.transpose().mulColumnVector(features.getColumn(fCol));
    maxes[fCol] = mul.max(0).transpose();
  }
  return Nd4j.hstack(maxes);
}

代码示例来源：origin: org.deeplearning4j/deeplearning4j-datavec-iterators

f = Nd4j.hstack(f1, f2);
} else {

代码示例来源：origin: org.deeplearning4j/deeplearning4j-core

f = Nd4j.hstack(f1, f2);
} else {