Perceive

perceive allows for easy perceptron-based binary classification in Go with a single function. perceive aims to be a lightweight solution for basic machine learning use cases.

The perceive function requires just four arguments.

• A list of inputs in the first class for training
• A list of inputs in the second class for training
• A list of inputs to be classified
• A group of parameters

In the following example, each input has three features.

import "github.com/calebwin/perceive"

trainingInputsClassA := [][]float64{
  []float64{-0.8, -0.7, -0.5,},
  []float64{-0.5, -0.4, -0.4,},
  []float64{-0.5, -0.5, -0.4,},
}

trainingInputsClassB := [][]float64{
  []float64{0.0, 0.2, 0.5,},
  []float64{0.7, 0.8, 0.9,},
}

myInputs := [][]float64{
  []float64{-0.8, -0.7, -0.5,},
  []float64{-0.5, -0.4, -0.4,},
  []float64{0.0, 0.2, 0.5,},
}

myOutputsClassA, myOutputsClassB := perceive.Perceive(trainingInputsClassA, trainingInputsClassB, myInputs, perceive.Parameters{
  []float64{0, 0, 0},
  0.1,
  32,
})

Parameters

perceive requires four parameters that can be fine-tuned to train the perceptron to yield more accurate outputs.

• a list of initial relative weights of each input
• a learning rate (can be set to 0.01 to start with)
• number of epochs (number of times to update classification model during learning phase)

Notes

perceive assumes that all inputs are (a) normalized and (b) scaled perceive uses stochastic gradient descent by default