LuminaAI/RCL-Iris-Flowers

Iris Flowers Dataset

Overview

This dataset contains tabular data for classifying different species of iris flowers. Each sample is stored in a separate text file, with features space-separated on a single line. The dataset is structured to be compatible with Lumina AI's Random Contrast Learning (RCL) algorithm via the PrismRCL application or API.

Dataset Structure

The dataset is organized into the following structure:

Iris-Flowers/
    train_data/
        class_Iris-setosa/
            sample_0.txt
            sample_1.txt
            ...
        class_Iris-versicolor/
            sample_0.txt
            sample_1.txt
            ...
        class_Iris-virginica/
            sample_0.txt
            sample_1.txt
            ...
    test_data/
        class_Iris-setosa/
            sample_0.txt
            sample_1.txt
            ...
        class_Iris-versicolor/
            sample_0.txt
            sample_1.txt
            ...
        class_Iris-virginica/
            sample_0.txt
            sample_1.txt
            ...

Note: All text file names must be unique across all class folders.

Features

• Tabular Data: Each text file contains space-separated values representing the features of a sample.
• Classes: There are three classes, each represented by a separate folder.

Usage (pre-split; optimal parameters)

Here is an example of how to load the dataset using PrismRCL:

C:\PrismRCL\PrismRCL.exe naivebayes rclticks=4 boxdown=0 channelpick=5 data=C:\path\to\Iris-Flowers\train_data testdata=C:\path\to\Iris-Flowers\test_data savemodel=C:\path\to\models\mymodel.classify log=C:\path\to\log_files stopwhendone

Explanation of Command:

• C:\PrismRCL\PrismRCL.exe: Path to the PrismRCL executable for classification
• naivebayes: Specifies Naive Bayes as the training evaluation method
• rclticks=4: Sets the number of RCL iterations during training to 4
• boxdown=0: Configuration parameter for training behavior
• channelpick=5: RCL training parameter
• data=C:\path\to\Iris-Flowers\train_data: Path to the training data for Iris flowers classification
• testdata=C:\path\to\Iris-Flowers\test_data: Path to the testing data for evaluation
• savemodel=C:\path\to\models\mymodel.classify: Path to save the resulting trained model
• log=C:\path\to\log_files: Directory path for storing log files of the training process
• stopwhendone: Instructs PrismRCL to end the session once training is complete

This revised version maintains consistency with the previous example while accurately reflecting the specifics of this Iris Flowers dataset command.

This format uses Markdown list syntax with each item on a new line, preceded by a hyphen and space. The command parameters are enclosed in backticks for better readability.

License

This dataset is licensed under the Creative Commons Attribution 4.0 International License. See the LICENSE file for more details.

Original Source

This dataset was originally sourced from the UCI Machine Learning Repository. Please cite the original source if you use this dataset in your research or applications.

Fisher, R.A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179-188. doi:10.1111/j.1469-1809.1936.tb02137.x

Additional Information

The data values have been prepared to ensure compatibility with PrismRCL. No normalization is required as of version 2.4.0.