Congratulations! Your PrismRCL trial is ready.
PrismRCL is available for Windows, Linux, and now macOS (Darwin), with support for both x86_64 and ARM processors.
PrismRCL is a command-line developer tool—it’s designed for scripting, automation, and embedding in larger workflows, rather than clicking through a user interface. All inputs are loaded from files on your system, and all logs, models, and reports are written to user-defined text files, making batch execution and unattended training straightforward.
PrismRCL runs natively on:
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Windows (x86_64 and ARM)
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Linux (x86_64 and ARM) – verified on Ubuntu 22/24, Red Hat Enterprise 9/10, Fedora Workstation 42
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macOS (x86_64 and ARM)
Download the build that matches your environment and start training with CPU-optimized Random Contrast Learning in minutes.
Your free trial download includes access to our Enterprise license for 30 days
Get Started with PrismRCL
This zip file contains the PrismRCL™ executables, technical documentation and getting started guide. You will also receive an email for convenience.
Start your Journey with RCL-ready Datasets
Dive into AI experimentation with confidence. Our datasets are cleaned, formatted, and ready for immediate use in PrismRCL.
Cancer Documents Classification (Text)
This dataset contains text data for classifying biomedical publications. 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.
Click this link to download this dataset.
Example PrismRCL command to train the algorithm on this dataset (adjust your paths!):
C:\PrismRCL\PrismRCL.exe chisquared rclticks=15 boxdown=0 channelpick=5 ^
data=C:\path\to\Cancer_Documents_Classification_mm testsize=0.1 ^
savemodel=C:\path\to\models\mymodel.classify ^
log=C:\path\to\log_files
stopwhendone
The command is shown with the optimal training parameters for this dataset.
Breast Cancer Histopathological Images
This dataset contains histopathological images of breast cancer tissues, divided into two classes: benign and malignant. Each sample is stored in a separate image file, organized into respective class folders. The dataset is structured to be compatible with Lumina AI’s Random Contrast Learning (RCL) algorithm via the PrismRCL application.
Click this link to download this dataset.
Example PrismRCL command to train the algorithm on this dataset (adjust your paths!):
C:\PrismRCL\PrismRCL.exe chisquared rclticks=10 boxdown=0 data=C:\path\to\Breast_Cancer_Histopathological_Dataset\train_data testdata=C:\path\to\Breast_Cancer_Histopathological_Dataset\test_data savemodel=C:\path\to\models\mymodel.classify log=C:\path\to\log_files stopwhendone
The command is shown with the optimal training parameters for this dataset.
Wheat Seeds (Tabular)
This dataset contains tabular data for classifying different varieties of wheat seeds. 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.
Click this link to download the dataset.
Example PrismRCL command to train the algorithm on this dataset (adjust your paths!):
C:\PrismRCL\PrismRCL.exe chisquared rclticks=25 boxdown=0 channelpick=5 ^
data=C:\pathC:\PrismRCL\PrismRCL.exe naivebayes rclticks=7 boxdown=0 channelpick=5 ^
data=C:\path\to\Wheat-Seeds\train_data testdata=C:\path\to\Wheat-Seeds\test_data ^
savemodel=C:\path\to\models\mymodel.classify ^
log=C:\path\to\log_files stopwhendone
The command is shown with the optimal training parameters for this dataset.
Brain Tumor MRI Scans (Images)
This dataset contains MRI images for classifying brain tumors across four categories: glioma, meningioma, pituitary tumor, and no tumor. Each image is stored as an individual .png file, and the dataset is structured to be compatible with Lumina AI’s Random Contrast Learning (RCL) algorithm via the PrismRCL application.
Click this link to download the dataset.
Example PrismRCL command to train the algorithm on this dataset (adjust your paths!):
C:\PrismRCL\PrismRCL.exe chisquared rclticks=65 boxdown=8 channelpick=5 ^
data=C:\patC:\PrismRCL\PrismRCL.exe chisquared rclticks=10 boxdown=0 ^
data=C:\path\to\brain-cancer-4class\train testdata=C:\path\to\brain-cancer-4class\test ^
savemodel=C:\path\to\models\brain_cancer_4class.classify ^
log=C:\path\to\log_files stopwhendone
The command is shown with the optimal training parameters for this dataset.
Discover more of our PrismRCL-ready datasets.
Instantly access our entire collection of pre-formatted data on Hugging Face.