Eclipse January supports straightforward creation of arrays. Let’s say we want to create a 2-dimensional array. First we can create a new dataset:

Dataset dataset = DatasetFactory.createFromObject(new double[] { 1, 2, 3, 4, 5, 6, 7, 8, 9 })

This gives us a 1-dimensional array with 9 elements, which you can see by executing the code below:

dataset.toString(true)

We then need to reshape it to be a 3×3 array. Run the code below to do just that.

dataset = dataset.reshape(3, 3)

Running this gives similar but reshaped results.

System.out.println(dataset.toString(true))

Or we can do it all in just one step:

Dataset another = DatasetFactory.createFromObject(new double[] { 1, 1, 2, 3, 5, 8, 13, 21, 34 }).reshape(3, 3)

System.out.println(another.toString(true))

There are methods for obtaining the shape and number of dimensions of datasets. To get the shape of the array use the command below.

dataset.getShape()

To get the dimension of the array use the next command.

dataset.getRank()

Datasets also provide a straightforward way to create an array initialized with a range of numbers. To create a dataset with a range use the code below.

Dataset dataset = DatasetFactory.createRange(IntegerDataset.class, 15).reshape(3, 5)

System.out.println(dataset.toString(true))

Datasets also provide a straightforward way to create an array initialized with random numbers.

To create a dataset with random data we first need to import the random class.

import org.eclipse.january.dataset.Random

Then, use the code below to create a random dataset.

Dataset another = Random.rand(3, 5)

System.out.println(another.toString(true))

The org.eclipse.january.dataset.Maths provides rich functionality for operating on the Dataset classes. For instance, here’s how you could add 2 Dataset arrays:

Dataset add = Maths.add(dataset, another)

System.out.println(add.toString(true))

Or you could do it as an inplace addition. The example below creates a new 3×3 array and then adds 100 to each element of the array.

Dataset inplace = DatasetFactory.createFromObject(new double[] { 1, 2, 3, 4, 5, 6, 7, 8, 9 }).reshape(3, 3)

inplace.iadd(100)

System.out.println(inplace.toString(true))

Slicing datasets simplify extracting portions of the data, known as ‘slices’. First we create a dataset using the code below.

Dataset dataset = DatasetFactory.createFromObject(new double[] { 1, 2, 3, 4, 5, 6, 7, 8, 9 }).reshape(3, 3)

System.out.println(dataset.toString(true))

Now say we want to extract 2,3,5 and 6 from the dataset.

[1, 2, 3,

4, 5, 6,

7, 8, 9]

As you can see this data resides in the first and second rows and the second and third columns.

When slicing the rows and columns begin at 0 and ascend upwarads.

A basic slice consists of a start index which is inclusive (row / column is included in the slice) and a stop index which is exclusive (row / column is not counted in the slice) Use this code to extract 2,3,5 and 6 from the dataset.

Dataset slice = dataset.getSlice(new Slice(0, 2), new Slice(1, 3))

System.out.println(slice.toString(true))