* KMeans associative clustering added
* fix travis error
* KMeans will return provided keys as point label if they are provided
* fix travis
* fix travis
* Add data loader for svm format
* Add tests for error cases
* Set proper exception messages
* Add documents
* Add error checking code for invalid column format
* Add missing documents
* Add test for svm model with probability estimation
* Extract buildPredictCommand method
* Fix test to use PHP_EOL
* Add predictProbability method (not completed)
* Add test for DataTransformer::predictions
* Fix SVM to use PHP_EOL
* Support probability estimation in SVM
* Add documentation
* Add InvalidOperationException class
* Throw InvalidOperationException before executing libsvm if probability estimation is not supported
* ability to specify per-layer activation function
* some tests for new addition to layer
* appease style CI whitespace issue
* more flexible addition of layers, and developer can pass Layer object in manually
* new test for layer object in mlp constructor
* documentation for added MLP functionality
- Backpropagation using the neuron activation functions derivative
- instead of hardcoded sigmoid derivative
- Added missing activation functions derivatives
- Sigmoid forced for the output layer
- Updated ThresholdedReLU default threshold to 0 (acts as a ReLU)
- Unit tests for derivatives
- Unit tests for classifiers using different activation functions
- Added missing docs
* Multiple training data sets allowed
* Tests with multiple training data sets
* Updating docs according to #38
Documenting all models which predictions will be based on all
training data provided.
Some models already supported multiple training data sets.
* Remove user-specific gitignore
* Add return type hints
* Avoid global namespace in docs
* Rename rules -> getRules
* Split up rule generation
Todo:
* Move set theory out to math
* Extract rule generation
* Generating frequent k-length item sets
* Generating rules based on frequent item sets
* Algorithm has exponential complexity, be aware of it
* Apriori algorithm is split into apriori and candidates method
* Second step rule generation is implemented by rules method
* Internal methods are invoked for fine grain unit tests
* Wikipedia's train samples and an alternative are provided for test cases
* Small documentation for public interface is also shipped
