A programmer has successfully implemented the K-means clustering algorithm on an Apple II computer using APPLESOFT BASIC, sparking discussions about the evolution of machine learning and programming constraints. The project demonstrates how fundamental machine learning concepts can run on hardware from the late 1970s, complete with visual decision boundaries and real-time algorithm iteration.
K-Means Algorithm Steps
- Initialize: Create k cluster centroids by randomly selecting k observations from dataset
- Step 1 - Assignment: Assign each observation to nearest cluster using Euclidean distance
- Step 2 - Update: Recompute centroids based on assigned points, repeat until convergence
Nostalgia Meets Modern Machine Learning
The implementation showcases K-means clustering with visual feedback, drawing decision boundaries as the algorithm converges on data points. The programmer reduced dataset sizes to speed up execution and handled the computational limitations of the vintage hardware. The project achieved 90% accuracy on a simple two-class clustering problem, proving that core machine learning principles can work even on severely constrained systems.
Community members shared their own memories of early programming adventures. One developer recalled creating genetic algorithms in Pascal during the 1990s, while another mentioned implementing a perceptron on the Apple II in 1984 that took 20 minutes per recognition pass. These stories highlight how programming constraints often led to creative solutions and important learning experiences.
Technical Implementation Details
- Platform: Apple II computer with APPLESOFT BASIC
- Dataset: Reduced to 5 samples per class for faster execution
- Accuracy: 90% (1 of 10 observations misclassified)
- Distance calculation: Squared Euclidean distance (avoiding expensive square root)
- Convergence tolerance: 0.001
Technical Challenges and Creative Solutions
The Apple II implementation faced several technical hurdles typical of vintage computing. Memory limitations required careful array management, as APPLESOFT BASIC throws errors when trying to redeclare existing arrays. The programmer had to optimize distance calculations by avoiding expensive square root operations, using squared Euclidean distances for comparison instead.
The visual component proved particularly challenging. Drawing decision boundaries required complex geometric calculations to ensure lines stayed within screen boundaries. The code handles edge cases like vertical slopes and off-screen centroids, though the programmer noted some limitations remain for scenarios with more than two clusters.
Apple II Hardware Constraints
- Memory limitations requiring careful array management
- No ability to redeclare existing arrays in APPLESOFT BASIC
- Expensive mathematical operations (SQR function)
- Limited screen resolution for graphics display
- Single-threaded execution with no modern optimization
Debate Over Machine Learning Classification
The project sparked debate about what qualifies as machine learning. Some community members questioned whether simple regression techniques deserve the machine learning label, while others argued that solving computationally complex problems on limited hardware definitely counts as ML territory. The discussion reflects broader questions about how the field has evolved and expanded over decades.
When you find yourself solving NP-hard problems on an Apple II, chances are strong you've entered machine learning territory.
The programmer plans to explore more advanced algorithms like Expectation Maximization, which could better handle the Gaussian nature of the test data. However, implementing neural networks with backpropagation on APPLESOFT BASIC would present significant additional challenges.
Legacy Hardware Programming Renaissance
This project joins a growing trend of implementing modern algorithms on vintage computers. The combination of nostalgia and technical challenge appeals to programmers interested in understanding both historical computing constraints and fundamental algorithmic concepts. Working within severe limitations often provides deeper insights into how algorithms actually function.
The Apple II implementation serves as both an educational exercise and a reminder of how far computing has progressed. What once required careful optimization and creative workarounds can now run effortlessly on smartphones thousands of times more powerful than the original hardware.
Reference: K-Means By Another Means