LunchBox includes Machine Learning components (LunchBoxML) that make Accord.NET and ML.NET workflows accessible within Grasshopper. These components allow users to train, save, and test machine learning models using a variety of algorithms. This example uses LunchBox’s Regression Trainer alongside Ladybug’s environmental modeling tools to predict incident solar radiation for an irregular surface.
While the status quo for models might be inconsistent or otherwise messy labeling, a utility in LunchBox can simplify label clean up. This article provides step-by-step instructions for preparing a simple Grasshopper script that will swap out a variety of Revit room names with standard labels.
LunchBox's new building massing tools can help you quickly create massing schemes that fit specific boundaries, areas, and number of floors based on your project's unique goals.
Images are powerful communication devices — and, with a few steps, you’re able to leverage them in your Power BI reports. For those in the AEC and Real Estate market, possible use cases might include linking images to floor plans or 3D models (as in the example illustration), attaching images to maintenance tickets, or tracking construction progress. There are likely many other creative uses beyond this author’s imagination!
