https://github.com/ben-ng/learnkit

High-performance Cocoa framework for Machine Learning on iOS and OS X.
https://github.com/ben-ng/learnkit

Last synced: 21 days ago
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High-performance Cocoa framework for Machine Learning on iOS and OS X.

• Host: GitHub
• URL: https://github.com/ben-ng/learnkit
• Owner: ben-ng
• License: mit
• Created: 2014-10-09T21:22:34.000Z (about 10 years ago)
• Default Branch: master
• Last Pushed: 2014-12-17T07:17:48.000Z (almost 10 years ago)
• Last Synced: 2024-04-16T01:10:12.928Z (7 months ago)
• Homepage: http://mattrajca.com
• Size: 8.72 MB
• Stars: 0
• Watchers: 3
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
LearnKit

========

LearnKit is a Cocoa framework for Machine Learning. It currently runs on top of the Accelerate framework on iOS and OS X.

Supported Algorithms

--------------------

- k-Means

- k-Nearest Neighbors

- Linear Regression

- Logistic Regression

- Naive Bayes

- Neural Networks

- Principal Component Analysis

Example

-------

In this example, we have a matrix that contains 5000 20x20 digits. Each 20x20 digit has been flattened into a row of 400 pixel intensities. We load it as such:

	NSURL *matrixURL = [NSURL fileURLWithPath:matrixPath];

	NSURL *matrixOutputURL = [NSURL fileURLWithPath:outputVectorPath];

	

	LNKMatrix *matrix = [[LNKMatrix alloc] initWithBinaryMatrixAtURL:matrixURL

													 matrixValueType:LNKValueTypeDouble

												   outputVectorAtURL:matrixOutputURL

											   outputVectorValueType:LNKValueTypeUInt8

														exampleCount:5000

														 columnCount:400

													addingOnesColumn:YES];

Next, we set up a conjugate gradient optimization algorithm to train the neural network.

	LNKOptimizationAlgorithmCG *algorithm = [[LNKOptimizationAlgorithmCG alloc] init];

	algorithm.iterationCount = 400;

Now we initialize a neural network classifier with our matrix and optimization algorithm. We also indicate the possible outputs are digits ranging from 1 to 10.

	LNKNeuralNetClassifier *classifier = [[LNKNeuralNetClassifier alloc] initWithMatrix:matrix 

																	 implementationType:LNKImplementationTypeAccelerate

																  optimizationAlgorithm:algorithm

																				classes:[LNKClasses withRange:NSMakeRange(1, 10)]];

	classifier.hiddenLayerCount = 1;

	classifier.hiddenLayerUnitCount = 25;

The neural network parameters above were picked with performance in mind. They can be fine-tuned to increase the accuracy of the classifier. Finally, we train the neural network classifer and predict the class of a previously-unseen digit.

	[classifier train];

	

	LNKClass *someDigit = [classifier predictValueForFeatureVector:someImage length:someImageLength];

With the right parameters, classification accuracy rates of over 99% can be attained.

Future Tasks

------------

- Support collaborative filtering

- Support decision trees

- Support SVMs

- Port to Metal and OpenCL

License

-------

LearnKit is available under the MIT license.

Credits

-------

LearnKit uses:

- `fmincg` by Carl Edward Rasmussen

- `liblbfgs`

- Data prepared by Andrew Ng for [Machine Learning](https://www.coursera.org/course/ml)