GoNet

A Simple Machine Learning Framework and Matrix Library

For the purpose of explaining a neural network from scratch

Getting Started

Install GoNet:

$ go get github.com/typio/gonet

Documentation

Basics:

• Import the package:
  import "github.com/typio/gonet"

Neural Network Library:

• Instantiates a neural network with 4 input nodes, 4 hidden nodes, and 1 output node:
  nn := gonet.NewNN(4, 4, 1)

• Trains the network once on matrix inputsM and matrix targetsM:
  nn.Train(inputsM, targetsM)

• Returns a guess on 1 by n matrix; representing a data point, as a float64:
  nn.Predict(gonet.FromArray(dataInputs[0]))

Matrix Library:

Basic Methods

• Creates a 2 by 3 matrix of zeros:
  m := gonet.Create(2, 3)

• Creates a 1 by n matrix from a 1D slice:
  m := gonet.fromArray([]float)

• Returns the matrix:
  m.Read()

• Prints the matrix and its dimensions:
  m.PrintM()

• Returns int array of matrix's dimensions ([rows, cols]):
  m.GetSize()

• Fills matrix with random floats in range [-1, 1):
  m.Randomize()

Scalar Methods

• Adds n (float64) to every element in matrix:
  m.Add(n)

• Multiplies n (float64) to every element in matrix:
  m.Multiply(n)

Elementwise Methods

• Returns new matrix of which every element is m[i][j] + n[i][j] (must both be same dimensions):
  s := m.AddM(n)

• Returns new matrix of which every element is m[i][j] - n[i][j] (must both be same dimensions):
  d := m.SubtractM(n)

• Returns new matrix of which every element is m[i][j] * n[i][j] (must both be same dimensions):
  p := m.MultiplyM(n)

• Passes every element in matrix through func fn():
  p := m.MapM(fn)

• Returns new matrix in which every element in matrix is passed through func fn() :
  p := m.MapNM(fn)

Linear Algebra Methods

• Returns new matrix which is the product of matrix m and matrix n (dimensions of m and n must have m cols = n rows)
  p := m.MatrixP(n)

• Returns new transposed matrix (swaps rows and cols)
  mT := m.Tranpose()