本文整理了Java中org.nd4j.linalg.factory.Nd4j.getCompressor()方法的一些代码示例，展示了Nd4j.getCompressor()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.getCompressor()方法的具体详情如下：
包路径：org.nd4j.linalg.factory.Nd4j
类名称：Nd4j
方法名：getCompressor

Nd4j.getCompressor介绍

[英]This method returns BasicNDArrayCompressor instance, suitable for NDArray compression/decompression at runtime
[中]此方法返回BasicNDArrayCompressor实例，适用于运行时的NDArray压缩/解压缩

代码示例

代码示例来源：origin: deeplearning4j/nd4j

/**
 *
 * @param img
 * @param kernel
 * @param stride
 * @param padding
 * @return
 */
public static INDArray im2col(INDArray img, int[] kernel, int[] stride, int[] padding) {
  Nd4j.getCompressor().autoDecompress(img);
  return im2col(img, kernel[0], kernel[1], stride[0], stride[1], padding[0], padding[1], 0, false);
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Replaces all elements in this ndarray that are matching give condition, with corresponding elements from given array
 *
 * @param arr
 * @param condition
 * @return
 */
@Override
public INDArray replaceWhere(INDArray arr, Condition condition) {
  Nd4j.getCompressor().autoDecompress(this);
  BooleanIndexing.replaceWhere(this, arr, condition);
  return this;
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray put(INDArrayIndex[] indices, Number element) {
  Nd4j.getCompressor().autoDecompress(this);
  INDArray get = get(indices);
  for (int i = 0; i < get.length(); i++)
    get.putScalar(i, element.doubleValue());
  return this;
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Returns the (euclidean) distance.
 */
@Override
public double distance2(INDArray other) {
  Nd4j.getCompressor().autoDecompress(this);
  return Nd4j.getExecutioner().execAndReturn(new EuclideanDistance(this, other)).getFinalResult().doubleValue();
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Returns the (1-norm) distance.
 */
@Override
public double distance1(INDArray other) {
  Nd4j.getCompressor().autoDecompress(this);
  return Nd4j.getExecutioner().execAndReturn(new ManhattanDistance(this, other)).getFinalResult().doubleValue();
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray putScalar(long row, long col, double value) {
  Nd4j.getCompressor().autoDecompress(this);
  autoProcessScalarCall();
  if (rank() > 2)
    throw new IllegalStateException("Cannot use putScalar(int,int,double) on a rank " + rank() + " INDArray");
  long offset = Shape.getOffsetUnsafe(javaShapeInformation, row, col);
  data.put(offset, value);
  return this;
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray putScalar(long dim0, long dim1, long dim2, long dim3, double value) {
  Nd4j.getCompressor().autoDecompress(this);
  autoProcessScalarCall();
  if (rank() != 4)
    throw new IllegalStateException(
        "Cannot use putScalar(int,int,int,int,double) on a rank " + rank() + " INDArray");
  long offset = Shape.getOffsetUnsafe(javaShapeInformation, dim0, dim1, dim2, dim3);
  data.put(offset, value);
  return this;
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray putScalar(long dim0, long dim1, long dim2, double value) {
  Nd4j.getCompressor().autoDecompress(this);
  autoProcessScalarCall();
  if (rank() != 3)
    throw new IllegalStateException(
        "Cannot use putScalar(int,int,int,double) on a rank " + rank() + " INDArray");
  long offset = 0; // Shape.getOffsetUnsafe(javaShapeInformation, dim0, dim1, dim2);
  long size_0 = javaShapeInformation[1];
  long size_1 = javaShapeInformation[1 + 1];
  long size_2 = javaShapeInformation[1 + 2];
  if (size_0 != 1)
    offset += dim0 * javaShapeInformation[1 + 0 + 3];
  if (size_1 != 1)
    offset += dim1 * javaShapeInformation[1 + 1 + 3];
  if (size_2 != 1)
    offset += dim2 * javaShapeInformation[1 + 2 + 3];
  data.put(offset, value);
  return this;
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray condi(Condition condition) {
  Nd4j.getCompressor().autoDecompress(this);
  INDArray linear = this;
  for (int i = 0; i < length(); i++) {
    boolean met = condition.apply(linear.getDouble(i));
    linear.putScalar(i, met ? 1 : 0);
  }
  return this;
}

代码示例来源：origin: deeplearning4j/nd4j

protected void checkForCompression(Op op) {
  // check for INT datatype arrays
  interceptIntDataType(op);
  if (op.x() != null && op.x().isCompressed())
    Nd4j.getCompressor().decompressi(op.x());
  if (op.y() != null && op.y().isCompressed())
    Nd4j.getCompressor().decompressi(op.y());
  if (op.z() != null && op.z().isCompressed())
    Nd4j.getCompressor().decompressi(op.z());
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray putWhere(INDArray comp, INDArray put, Condition condition) {
  Nd4j.getCompressor().autoDecompress(this);
  MatchConditionTransform matchCondition = new MatchConditionTransform(this,comp,condition);
  Nd4j.getExecutioner().exec(matchCondition);
  return putWhereWithMask(matchCondition.z(),put);
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Get the specified column
 *
 * @param c
 */
@Override
public INDArray getColumn(long c) {
  Nd4j.getCompressor().autoDecompress(this);
  if (isColumnVector() && c == 0)
    return this;
  else if (isColumnVector() && c > 0)
    throw new IllegalArgumentException("Illegal index for row");
  else if(isRowVector()) {
    return Nd4j.scalar(getDouble(c));
  }
  return get(NDArrayIndex.all(), NDArrayIndex.point(c));
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray dup() {
  WorkspaceUtils.assertValidArray(this, "Cannot duplicate INDArray");
  if (this.isCompressed() && this.ordering() == Nd4j.order().charValue()) {
    INDArray ret = Nd4j.createArrayFromShapeBuffer(data().dup(), this.shapeInfoDataBuffer());
    ret.markAsCompressed(true);
    return ret;
  }
  Nd4j.getCompressor().autoDecompress(this);
  INDArray ret = Shape.toOffsetZeroCopy(this);
  return ret;
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Insert a column in to this array
 * Will throw an exception if this
 * ndarray is not a matrix
 *
 * @param column the column to insert
 * @param toPut  the array to put
 * @return this
 */
@Override
public INDArray putColumn(int column, INDArray toPut) {
  Nd4j.getCompressor().autoDecompress(this);
  if (isColumnVector() && toPut.isVector()) {
    return assign(toPut);
  }
  return put(new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.point(column)}, toPut);
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public double getDouble(long i) {
  Nd4j.getCompressor().autoDecompress(this);
  if (i >= length()) {
    throw new IllegalArgumentException("Unable to get linear index >= " + length());
  }
  autoProcessScalarCall();
  if (i == 0)
    return data().getDouble(i);
  long[] dimensions = ordering() == 'c' ? Shape.ind2subC(this, i) : Shape.ind2sub(this, i);
  Shape.assertShapeLessThan(dimensions, shape());
  return getDouble(dimensions);
}

代码示例来源：origin: deeplearning4j/nd4j

public static INDArray im2col(INDArray img, int kh, int kw, int sy, int sx, int ph, int pw, int dh, int dw, boolean isSameMode) {
  Nd4j.getCompressor().autoDecompress(img);
  //Input: NCHW format
  // FIXME: int cast
  int outH = outputSize((int) img.size(2), kh, sy, ph, dh, isSameMode);
  int outW = outputSize((int) img.size(3), kw, sx, pw, dw, isSameMode);
  //[miniBatch,depth,kH,kW,outH,outW]
  INDArray out = Nd4j.create(new long[]{img.size(0), img.size(1), kh, kw, outH, outW}, 'c');
  return im2col(img, kh, kw, sy, sx, ph, pw, dh, dw, isSameMode, out);
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Flattens the array for linear indexing
 *
 * @return the flattened version of this array
 */
@Override
public INDArray ravel(char ordering) {
  Nd4j.getCompressor().autoDecompress(this);
  if (length() >= Integer.MAX_VALUE)
    throw new IllegalArgumentException("Length can not be >= Integer.MAX_VALUE");
  INDArray ret = create(new int[] {1, (int) length()}, ordering);
  NDArrayIndex index = new NDArrayIndex(this.shape());
  for (int i = 0; i < length(); i++) {
    // FIXME: LONG
    double val = getDouble((int) index.next());
    ret.putScalar(new int[] {0, i}, val);
  }
  return ret;
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray dup(char order) {
  WorkspaceUtils.assertValidArray(this, "Cannot duplicate INDArray");
  if (this.isCompressed() && this.ordering() == order) {
    INDArray ret = Nd4j.createArrayFromShapeBuffer(data().dup(), this.shapeInfoDataBuffer());
    ret.markAsCompressed(true);
    return ret;
  }
  Nd4j.getCompressor().autoDecompress(this);
  return Shape.toOffsetZeroCopy(this, order);
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray put(INDArrayIndex[] indices, INDArray element) {
  Nd4j.getCompressor().autoDecompress(this);
  if (indices[0] instanceof SpecifiedIndex && element.isVector()) {
    indices[0].reset();
    int cnt = 0;
    while (indices[0].hasNext()) {
      long idx = indices[0].next();
      // FIXME: LONG
      putScalar((int) idx, element.getDouble(cnt));
      cnt++;
    }
    return this;
  } else {
    return get(indices).assign(element);
  }
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Replicate and tile array to fill out to the given shape
 * See:
 * https://github.com/numpy/numpy/blob/master/numpy/matlib.py#L310-L358
 * @param shape the new shape of this ndarray
 * @return the shape to fill out to
 */
@Override
public INDArray repmat(int[] shape) {
  Nd4j.getCompressor().autoDecompress(this);
  long rows = rows() * shape[0];
  long cols = columns() * shape[1];
  INDArray ret = reshape(1, length()).repeat(0, shape[0]).reshape(rows, columns()).repeat(0, shape[1]);
  return ret.reshape(rows, cols);
}