- 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
