Module org.tensorflow.ndarray
Package org.tensorflow.ndarray.impl.sparse.slice

Class SparseSlice<T,​U extends NdArray<T>>

    • Constructor Detail

      • SparseSlice

        public SparseSlice​(AbstractSparseNdArray<T,​U> source,
                   long sourcePosition,
                   DimensionalSpace dimensions)
        Creates a SparseSlice
        Parameters:
        source - the source Sparse Array that this object slices.
        sourcePosition - the relative position into the source array
        dimensions - the dimensional space for the window

    • Method Detail

      • equals

        public boolean equals​(Object obj)
        Checks equality between n-dimensional arrays.

        An array is equal to another object if this object is another NdArray of the same shape, type and the elements are equal and in the same order. For example: 

        
 IntNdArray array = NdArrays.ofInts(Shape.of(2, 2))
    .set(NdArrays.vectorOf(1, 2), 0)
    .set(NdArrays.vectorOf(3, 4), 1);

 assertEquals(array, StdArrays.ndCopyOf(new int[][] {{1, 2}, {3, 4}}));  // true
 assertEquals(array, StdArrays.ndCopyOf(new Integer[][] {{1, 2}, {3, 4}}));  // true, as Integers are equal to ints
 assertNotEquals(array, NdArrays.vectorOf(1, 2, 3, 4));  // false, different shapes
 assertNotEquals(array, StdArrays.ndCopyOf(new int[][] {{3, 4}, {1, 2}}));  // false, different order
 assertNotEquals(array, StdArrays.ndCopyOf(new long[][] {{1L, 2L}, {3L, 4L}}));  // false, different types

        Note that the computation required to verify equality between two arrays can be expensive in some cases and therefore, it is recommended to not use this method in a critical path where performances matter.

        Specified by:
        equals in interface NdArray<T>
        Overrides:
        equals in class AbstractSparseNdArray<T,​U extends NdArray<T>>
        Parameters:
        obj - object to compare this array with
        Returns:
        true if this array is equal to the provided object

      • getObject

        public T getObject​(long... coordinates)
        Returns the value of the scalar found at the given coordinates.

        To access the scalar element, the number of coordinates provided must be equal to the number of dimensions of this array (i.e. its rank). For example: 

        
  FloatNdArray matrix = NdArrays.ofFloats(shape(2, 2));  // matrix rank = 2
  matrix.getObject(0, 1);  // succeeds, returns 0.0f
  matrix.getObject(0);  // throws IllegalRankException

  FloatNdArray scalar = matrix.get(0, 1);  // scalar rank = 0
  scalar.getObject();  // succeeds, returns 0.0f
        Note: if this array stores values of a primitive type, prefer the usage of the specialized method in the subclass for that type. For example, floatArray.getFloat(0); .
        Specified by:
        getObject in interface NdArray<T>
        Overrides:
        getObject in class AbstractSparseNdArray<T,​U extends NdArray<T>>
        Parameters:
        coordinates - coordinates of the scalar to resolve
        Returns:
        value of that scalar

      • get

        public NdArray<T> get​(long... coordinates)
        Returns the N-dimensional element of this array at the given coordinates.

        Elements of any of the dimensions of this array can be retrieved. For example, if the number of coordinates is equal to the number of dimensions of this array, then a rank-0 (scalar) array is returned, which value can then be obtained by calling `array.getObject()`.

        Any changes applied to the returned elements affect the data of this array as well, as there is no copy involved.

        Note that invoking this method is an equivalent and more efficient way to slice this array on single scalar, i.e. array.get(x, y, z) is equal to array.slice(at(x), at(y), at(z))

        Specified by:
        get in interface NdArray<T>
        Overrides:
        get in class AbstractSparseNdArray<T,​U extends NdArray<T>>
        Parameters:
        coordinates - coordinates of the element to access, none will return this array
        Returns:
        the element at this index

      • slice

        public NdArray<T> slice​(Index... indices)
        Creates a multi-dimensional view (or slice) of this array by mapping one or more dimensions to the given index selectors.

        Slices allow to traverse an N-dimensional array in any of its axis and/or to filter only elements of interest. For example, for a given matrix on the [x, y] axes, it is possible to iterate elements at y=0 for all x.

        Any changes applied to the returned slice affect the data of this array as well, as there is no copy involved.

        Example of usage: 

        
    FloatNdArray matrix3d = NdArrays.ofFloats(shape(3, 2, 4));  // with [x, y, z] axes

    // Iterates elements on the x axis by preserving only the 3rd value on the z axis,
    // (i.e. [x, y, 2])
    matrix3d.slice(all(), all(), at(2)).elements(0).forEach(m -> {
      assertEquals(shape(2), m); // y=2, z=0 (scalar)
    });

    // Creates a slice that contains only the last element of the y axis and elements with an
    // odd `z` coordinate.
    FloatNdArray slice = matrix3d.slice(all(), at(1), odd());
    assertEquals(shape(3, 2), slice.shape());  // x=3, y=0 (scalar), z=2 (odd coordinates)

    // Iterates backward the elements on the x axis
    matrix3d.slice(flip()).elements(0).forEach(m -> {
      assertEquals(shape(2, 4), m);  // y=2, z=4
    });
        Specified by:
        slice in interface NdArray<T>
        Overrides:
        slice in class AbstractSparseNdArray<T,​U extends NdArray<T>>
        Parameters:
        indices - index selectors per dimensions, starting from dimension 0 of this array.
        Returns:
        the element resulting of the index selection

      • elements

        public NdArraySequence<U> elements​(int dimensionIdx)
        Returns a sequence of all elements at a given dimension.

        Logically, the N-dimensional array can be flatten in a single vector, where the scalars of the (n - 1)th element precedes those of the (n)th element, for a total of Shaped.size() values.

        For example, given a n x m matrix on the [x, y] axes, elements are iterated in the following order:

        x0y0, x0y1, ..., x0ym-1, x1y0, x1y1, ..., xn-1ym-1

        The returned sequence can then be iterated to visit each elements, either by calling Iterable.forEach(Consumer) or NdArraySequence.forEachIndexed(BiConsumer). 

        
    // Iterate matrix for initializing each of its vectors
    matrixOfFloats.elements(0).forEach(v -> {
      v.set(vector(1.0f, 2.0f, 3.0f));
    });

    // Iterate a vector for reading each of its scalar
    vectorOfFloats.scalars().forEachIdx((coords, s) -> {
      System.out.println("Value " + s.getFloat() + " found at " + coords);
    });
        Specified by:
        elements in interface NdArray<T>
        Overrides:
        elements in class AbstractSparseNdArray<T,​U extends NdArray<T>>
        Parameters:
        dimensionIdx - index of the dimension
        Returns:
        an NdArray sequence

      • write

        public NdArray<T> write​(DataBuffer<T> src)
        Write the content of this N-dimensional array from the source buffer.

        The size of the buffer must be equal or greater to the Shaped.size() of this array, or an exception is thrown. After the copy, content of the buffer and of the array can be altered independently, without affecting each other.

        Parameters:
        src - the source buffer
        Returns:
        this array
        See Also:
        DataBuffer.size()

      • createValues

        public U createValues​(Shape shape)
        Creates a dense array of the type that this sparse array represents.
        Specified by:
        createValues in class AbstractSparseNdArray<T,​U extends NdArray<T>>
        Parameters:
        shape - the shape of the dense array.
        Returns:
        the dense of the type that this sparse array represents.