A single / multi level perceptron classifier with weights estimated from sonar training data set using stochastic gradient descent. Recently I added back propagation algorithm over multilayer perceptron network. The implementation is in dev. Planned features:

• complete future features XD (see above)
• find co-workers
• create a ml library in openqasm (just kidding)
• brainstorming / devtesting other an models

2017-10-04: Introduced Recurrent Neural Network (Elman Network) with "learn to sum integer" task. Big refactoring in code (working on)

2017-08-08: Introduced multi layer perceptron network definition with parametric number of hidden layer and neurons. Back propagation algorithm with different transfer function actived - I wanna thank you dakk because I was truly inspired by your code.

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.

• logrus

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 github.com/sirupsen/logrus
go run main.go

You can setup a MultiLayerPerceptron using PrepareMLPNet. The first parameter, a simple []int, define the entire network struct. Example:

• [4, 3, 3] will define a network struct with 3 layer: input, hidden, output, with respectively 4, 3 and 3 neurons. For classification problems the input layers has to be define with a number of neurons that match features of pattern shown to network. Of course, the output layer should have a number of unit equals to the number of class in training set. The network will have this topology:

• [4, 6, 4, 3] will have this topology:

• test methods

• mathgl for better vector space handling
• some other cool neural model XD