iqiukp / BinaryDataset

MATLAB code for 2D or 3D binary dataset for classification.

🔥🔥 BinaryDataset

MATLAB code for 2D or 3D binary dataset.

✨ MAIN FEATURES

• 2D or 3D binary dataset of "banana" and "circle" shapes.
• Partitioning of training dataset/label and test dataset/label.

🔨 HOW TO USE

ocdata = BinaryDataset();
[data, label] = ocdata.generate;
[trainData, trainLabel, testData, testLabel] = ocdata.partition;

The full Name-Value Arguments of class BinaryDataset are

• shape: shape of dataset, 'banana' or 'circle'.
• dimensionality: dimensionality of dataset, 2 or 3.
• number: number of samples per class, for example: [200, 200].
• display: visualization, 'on' or 'off'.
• noise: noise added to dataset with range [0, 1]. For example: 0.2.
• ratio: ratio of the test set with range (0, 1). For example: 0.3.

👉 Example 1

Generate a 3D banana-shaped dataset with 200 and 100 samples for each class, and divide 10% of the data into the test dataset.

ocdata = BinaryDataset( 'shape', 'banana',...
                        'dimensionality', 3,...
                        'number', [200, 100],...
                        'display', 'on', ...
                        'noise', 0.2,...
                        'ratio', 0.1);
[data, label] = ocdata.generate;
[trainData, trainLabel, testData, testLabel] = ocdata.partition;

👉 Example 2

Generate a 2D circle-shaped dataset with 100 and 300 samples for each class, and divide 50% of the data into the test dataset.

ocdata = BinaryDataset( 'shape', 'circle',...
                        'dimensionality', 2,...
                        'number', [100, 300],...
                        'display', 'on', ...
                        'noise', 0.2,...
                        'ratio', 0.5);
[data, label] = ocdata.generate;
[trainData, trainLabel, testData, testLabel] = ocdata.partition;