NDArray

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. def arange(start: Float, stop: Option[Float] = None, step: Float = 1.0f, repeat: Int = 1, ctx: Context = Context.defaultCtx, dType: DType.DType = Base.MX_REAL_TYPE): NDArray

   

   Returns evenly spaced values within a given interval.

   Returns evenly spaced values within a given interval. Values are generated within the half-open interval [start, stop). In other words, the interval includes start but excludes stop.

   start

   Start of interval. The default start value is 0.

   stop

   End of interval.

   step

   Spacing between values. The default step size is 1.

   repeat

   Number of times to repeat each element. The default repeat count is 1.

   ctx

   Device context. Default context is the current default context.

   dType

   The data type of the NDArray. The default datatype is DType.Float32.

   returns

   NDArray of evenly spaced values in the specified range.

5. def array(sourceArr: Array[Float], shape: Shape, ctx: Context = null): NDArray

   

   Create a new NDArray that copies content from source_array.

   Create a new NDArray that copies content from source_array.

   sourceArr

   Source data to create NDArray from.

   shape

   shape of the NDArray

   ctx

   The context of the NDArray, default to current default context.

   returns

   The created NDArray.

6. final def asInstanceOf[T0]: T0

   

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7. def clone(): AnyRef

   

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8. def concatenate(arrays: NDArray*): NDArray

   

9. def concatenate(arrays: Seq[NDArray], axis: Int = 0, alwaysCopy: Boolean = true): NDArray

   

   Concatenate a list of NDArrays along the specified dimension.

   Concatenate a list of NDArrays along the specified dimension.

   arrays

   Arrays to be concatenate. They must have identical shape except the first dimension. They also must have the same data type.

   axis

   The axis along which to concatenate.

   alwaysCopy

   Default True. When not True, if the arrays only contain one NDArray, that element will be returned directly, avoid copying.

   returns

   An NDArray that lives on the same context as arrays[0].context.

10. def deserialize(bytes: Array[Byte]): NDArray

    

11. def empty(ctx: Context, shape: Int*): NDArray

    

12. def empty(shape: Int*): NDArray

    

13. def empty(shape: Shape, ctx: Context = null, dtype: DType.DType = Base.MX_REAL_TYPE): NDArray

    

    Create an empty uninitialized new NDArray, with specified shape.

    Create an empty uninitialized new NDArray, with specified shape.

    shape

    shape of the NDArray.

    ctx

    The context of the NDArray, default to current default context.

    returns

    The created NDArray.

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

    

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15. def equal(lhs: NDArray, rhs: Float): NDArray

    

16. def equal(lhs: NDArray, rhs: NDArray): NDArray

    

    Returns the result of element-wise **equal to** (==) comparison operation with broadcasting.

    Returns the result of element-wise **equal to** (==) comparison operation with broadcasting. For each element in input arrays, return 1(true) if corresponding elements are same, otherwise return 0(false).

17. def equals(arg0: Any): Boolean

    

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18. def finalize(): Unit

    

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    protected[java.lang]

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    @throws( classOf[java.lang.Throwable] )

19. def full(shape: Shape, value: Float, ctx: Context = null): NDArray

    

    Create a new NDArray filled with given value, with specified shape.

    Create a new NDArray filled with given value, with specified shape.

    shape

    shape of the NDArray.

    value

    value to be filled with

    ctx

    The context of the NDArray, default to current default context

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

    

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21. implicit def getFirstResult(ret: NDArrayFuncReturn): NDArray

    

22. def greater(lhs: NDArray, rhs: Float): NDArray

    

23. def greater(lhs: NDArray, rhs: NDArray): NDArray

    

    Returns the result of element-wise **greater than** (>) comparison operation with broadcasting.

    Returns the result of element-wise **greater than** (>) comparison operation with broadcasting. For each element in input arrays, return 1(true) if lhs elements are greater than rhs, otherwise return 0(false).

24. def greaterEqual(lhs: NDArray, rhs: Float): NDArray

    

25. def greaterEqual(lhs: NDArray, rhs: NDArray): NDArray

    

    Returns the result of element-wise **greater than or equal to** (>=) comparison operation with broadcasting.

    Returns the result of element-wise **greater than or equal to** (>=) comparison operation with broadcasting. For each element in input arrays, return 1(true) if lhs elements are greater than equal to rhs, otherwise return 0(false).

26. def hashCode(): Int

    

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27. final def isInstanceOf[T0]: Boolean

    

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28. def lesser(lhs: NDArray, rhs: Float): NDArray

    

29. def lesser(lhs: NDArray, rhs: NDArray): NDArray

    

    Returns the result of element-wise **lesser than** (<) comparison operation with broadcasting.

    Returns the result of element-wise **lesser than** (<) comparison operation with broadcasting. For each element in input arrays, return 1(true) if lhs elements are less than rhs, otherwise return 0(false).

30. def lesserEqual(lhs: NDArray, rhs: Float): NDArray

    

31. def lesserEqual(lhs: NDArray, rhs: NDArray): NDArray

    

    Returns the result of element-wise **lesser than or equal to** (<=) comparison operation with broadcasting.

    Returns the result of element-wise **lesser than or equal to** (<=) comparison operation with broadcasting. For each element in input arrays, return 1(true) if lhs elements are lesser than equal to rhs, otherwise return 0(false).

32. def load(fname: String): (Array[String], Array[NDArray])

    

    Load ndarray from binary file.

    Load ndarray from binary file.

    You can also use pickle to do the job if you only work on python. The advantage of load/save is the file is language agnostic. This means the file saved using save can be loaded by other language binding of mxnet. You also get the benefit being able to directly load/save from cloud storage(S3, HDFS)

    fname

    The name of the file.Can be S3 or HDFS address (remember built with S3 support). Example of fname:

    • s3://my-bucket/path/my-s3-ndarray
    • hdfs://my-bucket/path/my-hdfs-ndarray
    • /path-to/my-local-ndarray

    returns

    dict of str->NDArray to be saved

33. def load2Array(fname: String): Array[NDArray]

    

34. def load2Map(fname: String): Map[String, NDArray]

    

35. def maximum(lhs: Float, rhs: NDArray): NDArray

    

36. def maximum(lhs: NDArray, rhs: Float): NDArray

    

37. def maximum(lhs: NDArray, rhs: NDArray): NDArray

    

38. def minimum(lhs: Float, rhs: NDArray): NDArray

    

39. def minimum(lhs: NDArray, rhs: Float): NDArray

    

40. def minimum(lhs: NDArray, rhs: NDArray): NDArray

    

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

    

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42. def notEqual(lhs: NDArray, rhs: Float): NDArray

    

43. def notEqual(lhs: NDArray, rhs: NDArray): NDArray

    

    Returns the result of element-wise **not equal to** (!=) comparison operation with broadcasting.

    Returns the result of element-wise **not equal to** (!=) comparison operation with broadcasting. For each element in input arrays, return 1(true) if corresponding elements are different, otherwise return 0(false).

44. final def notify(): Unit

    

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45. final def notifyAll(): Unit

    

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46. def onehotEncode(indices: NDArray, out: NDArray): NDArray

    

    One hot encoding indices into matrix out.

    One hot encoding indices into matrix out.

    indices

    An NDArray containing indices of the categorical features.

    out

    The result holder of the encoding.

    returns

    Same as out.

47. def ones(ctx: Context, shape: Int*): NDArray

    

48. def ones(shape: Int*): NDArray

    

49. def ones(shape: Shape, ctx: Context = null, dtype: DType.DType = Base.MX_REAL_TYPE): NDArray

    

    Create a new NDArray filled with 1, with specified shape.

    Create a new NDArray filled with 1, with specified shape.

    shape

    shape of the NDArray.

    ctx

    The context of the NDArray, default to current default context.

    returns

    The created NDArray.

50. def power(lhs: Float, rhs: NDArray): NDArray

    

51. def power(lhs: NDArray, rhs: Float): NDArray

    

52. def power(lhs: NDArray, rhs: NDArray): NDArray

    

53. def save(fname: String, data: Traversable[NDArray]): Unit

    

54. def save(fname: String, data: Map[String, NDArray]): Unit

    

    Save list of NDArray or dict of str->NDArray to binary file.

    Save list of NDArray or dict of str->NDArray to binary file.

    You can also use pickle to do the job if you only work on python. The advantage of load/save is the file is language agnostic. This means the file saved using save can be loaded by other language binding of mxnet. You also get the benefit being able to directly load/save from cloud storage(S3, HDFS)

    fname

    The name of the file.Can be S3 or HDFS address (remember built with S3 support). Example of fname:

    • s3://my-bucket/path/my-s3-ndarray
    • hdfs://my-bucket/path/my-hdfs-ndarray
    • /path-to/my-local-ndarray

    data

    dict of str->NDArray

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

    

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56. def toString(): String

    

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57. final def wait(): Unit

    

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

    

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

    

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60. def waitall(): Unit

    

    Wait all async operation to finish in MXNet This function is used for benchmark only

61. def zeros(ctx: Context, shape: Int*): NDArray

    

62. def zeros(shape: Int*): NDArray

    

63. def zeros(shape: Shape, ctx: Context = null, dtype: DType.DType = Base.MX_REAL_TYPE): NDArray

    

    Create a new NDArray filled with 0, with specified shape.

    Create a new NDArray filled with 0, with specified shape.

    shape

    shape of the NDArray.

    ctx

    The context of the NDArray, default to current default context.

    returns

    The created NDArray.