* Issue #349: Provide a new NGramTokenizer.
* Issue #349: Add tests.
* Fixes from code review.
* Implement NGramTokenizer with min and max gram support
* Add missing tests for ngram
* Add info about NGramTokenizer to docs and readme
* Add performance test for tokenization
* 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
* Add support for coveralls.io
* Generate coverage report only on php 7.2 build
* Fix osx travis build and move tools to bin dir
* Update php version badge
* Fix travis conditional statement
* Fix travis conditional statement
* 🤦 fix bin path
* 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.
### Features
* Works only with primitive types int, float, string
* Implements set theortic operations union, intersection, complement
* Modifies set by adding, removing elements
* Implements \IteratorAggregate for use in loops
### Implementation details
Based on array functions:
* array_diff,
* array_merge,
* array_intersection,
* array_unique,
* array_values,
* sort.
### Drawbacks
* **Do not work with objects.**
* Power set and Cartesian product returning array of Set
* 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
bimsuru
ctfblackflag
Andrew Feeney
Attila Bakos
Marcin Michalski
KenorFR
Arkadiusz Kondas
Pol Dellaiera
Yuji Uchiyama
Ivana Momcilovic
Jonathan Baldie
David Monllaó
Tomáš Votruba
Bill Nunney
Robert Boloc
Ken Seah
Patrick Florek