Tensor

Value Members

1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. val START_INDEX: Int

   

   Start index in BigDL.

   Start index in BigDL. We count from 1.

5. def apply(matrix: DenseMatrix): Tensor[Double]

   

   create a tensor with a given spark Densematrix.

   create a tensor with a given spark Densematrix. The tensor will have the same size with the given spark Densematrix.

6. def apply[T](matrix: DenseMatrix[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

   

   create a tensor with a given breeze matrix.

   create a tensor with a given breeze matrix. The tensor will have the same size with the given breeze matrix.

   matrix

   the given breeze matrix

7. def apply(vector: DenseVector): Tensor[Double]

   

   create a tensor with a given spark Densevector.

   create a tensor with a given spark Densevector. The tensor will have the same size with the given spark Densevector.

   vector

   the given spark Densevector

8. def apply[T](vector: DenseVector[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

   

   create a tensor with a given breeze vector.

   create a tensor with a given breeze vector. The tensor will have the same size with the given breeze vector.

   vector

   the given breeze vector

9. def apply[T](other: Tensor[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

   

   create a tensor with a given tensor.

   create a tensor with a given tensor. The tensor will have same size with the given tensor.

   other

   the given tensor

10. def apply[T](storage: Storage[T], storageOffset: Int, size: Array[Int] = null, stride: Array[Int] = null)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

    Returns a tensor which uses the existing Storage storage, starting at position storageOffset (>=1).

    Returns a tensor which uses the existing Storage storage, starting at position storageOffset (>=1). The size of each dimension of the tensor is given by the optional Array size. If not given, the size will be computed as the length of storage. The jump necessary to go from one element to the next one in each dimension is given by the optional Array stride. If not given, the stride() will be computed such that the tensor is as contiguous as possible in memory.

11. def apply[T](data: Array[T], shape: Array[Int])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

    Returns a tensor with the given array and shape

    Returns a tensor with the given array and shape

    data

    the given storage

    shape

    the given shape

12. def apply[T](storage: Storage[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

    Returns a tensor which uses the existing Storage storage.

    Returns a tensor which uses the existing Storage storage.

    storage

    the given storage

13. def apply[T](sizes: Array[Int])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

    Create a tensor on given sizes.

    Create a tensor on given sizes. The tensor size will be the product of sizes

14. def apply[T](dims: Int*)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

    Create a tensor on given dimensions.

    Create a tensor on given dimensions. The tensor size will be the product of dims

15. def apply[T](xs: Table)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

    Create a tensor with a table

    Create a tensor with a table

    xs

    the table contains a multi-dimensional numbers

    returns

    a new Tensor

16. def apply[T](d1: Int, d2: Int, d3: Int, d4: Int, d5: Int)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

17. def apply[T](d1: Int, d2: Int, d3: Int, d4: Int)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

18. def apply[T](d1: Int, d2: Int, d3: Int)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

19. def apply[T](d1: Int, d2: Int)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

20. def apply[T](d1: Int)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

    Create a tensor up to 5 dimensions.

    Create a tensor up to 5 dimensions. The tensor size will be d1 x d2 x d3 x d4 x d5.

21. def apply[T]()(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

    Returns an empty tensor.

22. final def asInstanceOf[T0]: T0

    

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23. def clone(): AnyRef

    

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24. def dense[T](sparseTensor: Tensor[T], res: Tensor[T] = null)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

    Transform a sparseTensor to DenseTensor.

    Transform a sparseTensor to DenseTensor.

    sparseTensor

    a sparse tensor

    res

    if defined, override to res, else will generate a new tensor.

    returns

    a DenseTensor.

25. final def eq(arg0: AnyRef): Boolean

    

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26. def equals(arg0: Any): Boolean

    

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27. def expand[T](tensor: Tensor[T], sizes: Int*): Tensor[T]

    

    This is equivalent to tensor.expand(sizes.toArray)

28. def expandAs[T](tensor: Tensor[T], template: Tensor[T]): Tensor[T]

    

    This is equivalent to tensor.expandAs(template)

29. def finalize(): Unit

    

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30. def gaussian1D[T](size: Int = 3, sigma: Double = 0.25, amplitude: Int = 1, normalize: Boolean = false, mean: Double = 0.5, tensor: Tensor[T] = null)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

    Returns a 1D Gaussian kernel of size size, mean mean and standard deviation sigma.

    Returns a 1D Gaussian kernel of size size, mean mean and standard deviation sigma.

    tensor

    If tensor is set, will discard size, and write result to tensor.

31. final def getClass(): Class[_]

    

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32. def hashCode(): Int

    

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33. final def isInstanceOf[T0]: Boolean

    

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34. def load[T](path: String): Tensor[T]

    

35. final def ne(arg0: AnyRef): Boolean

    

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36. final def notify(): Unit

    

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37. final def notifyAll(): Unit

    

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38. def ones[T](sizes: Int*)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

    return a tensor of sizes filled with 1.

    return a tensor of sizes filled with 1.

    returns

    a tensor

39. def randperm[T](size: Int)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

    This is equivalent to DenseTensor.randperm[T](size)

40. def range[T](xmin: Double, xmax: Double, step: Int = 1)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

    This is equivalent to DenseTensor.range(xmin, xmax, step)

41. def repeatTensor[T](tensor: Tensor[T], sizes: Int*): Tensor[T]

    

    This is equivalent to tensor.repeatTensor(sizes.toArray)

42. def scalar[T](value: T)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

    Create a scalar tensor of this value

    Create a scalar tensor of this value

    returns

    the created scalar tensor

43. def sparse[T](shape: Array[Int], nElement: Int = 1)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

    Create a sparse tensor with shape and number of non-zero elements.

    Create a sparse tensor with shape and number of non-zero elements.

    shape

    tensor's shape.

    nElement

    number of non-zero elements.

44. def sparse[T](denseTensor: Tensor[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

    Transform a DenseTensor to SparseTensor.

45. def sparse[T](indices: Array[Array[Int]], values: Array[T], shape: Array[Int], dimension: Int)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

    Create a SparseTensor.

    Create a SparseTensor.

    indices

    dimension-D array to describe the indices of values, should be zero-based and ascending.

    values

    non-zero values in this SparseTensor.

    shape

    shape

    dimension

    dimension

46. def sparse[T](indices: Array[Array[Int]], values: Storage[T], shape: Array[Int], dimension: Int)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

    Create a SparseTensor.

    Create a SparseTensor.

    indices

    dimension-D array to describe the indices of values, should be zero-based and ascending.

    values

    non-zero values in this SparseTensor.

    shape

    shape

    dimension

    dimension

47. def sparse[T](indices: Array[Array[Int]], values: Array[T], shape: Array[Int])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

    Create a SparseTensor.

    Create a SparseTensor.

    indices

    dimension-D array to describe the indices of values, should be zero-based and ascending.

    values

    non-zero values in this SparseTensor.

    shape

    shape

48. def sparse[T](indices: Array[Array[Int]], values: Storage[T], shape: Array[Int])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

    Create a SparseTensor.

    Create a SparseTensor.

    indices

    dimension-D array to describe the indices of values, should be zero-based and ascending.

    values

    non-zero values in this SparseTensor.

    shape

    shape

49. final def synchronized[T0](arg0: ⇒ T0): T0

    

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50. def toString(): String

    

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51. def unique[T](tensor: Tensor[T], distinctBuffer: Tensor[T] = null, indicesBuffer: Tensor[Int] = null)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): (Tensor[T], Tensor[Int])

    

    Find the distinct value and its indices in a 1D tensor.

    Find the distinct value and its indices in a 1D tensor.

    tensor

    a 1D tensor

    distinctBuffer

    a buffer for its distinct values.

    indicesBuffer

    a buffer for its indcies.

    returns

    (distinctValues, indices)

52. final def wait(): Unit

    

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53. final def wait(arg0: Long, arg1: Int): Unit

    

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54. final def wait(arg0: Long): Unit

    

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