A single level perceptron classifier with weights estimated from sonar training data set using stochastic gradient descent. The implementation is in dev. Planned features:
- complete future features XD (see above)
- find co-workers
- dev a three (then k-parameter) level networks with backprop
- create a ml library in openqasm (just kidding)
- brainstorming / devtesting other algorithms in ml
2017-08-01: Introduced validation package and k-fold cross validation.
2017-07-31: I started working on
mlp branch for MLP + back prop. It doens't work yet but...I push first commit after some exp in dev. I delete
dev because of some structs optimization.
2017-07-31: we started working on k-fold validation.
To run a simple test just open a shell and run the following:
git clone https://github.com/made2591/go-perceptron-go cd go-perceptron-go go get https://github.com/sirupsen/logrus go run main.go
To complete yet
- test methods
- mathgl for better vector space handling
- multilevel (3 and then parametric) level perceptron to resolve non-linearly separable problems
- some other cool neural model XD