AI.HNN.Neuron

Neuron module, defining an artificial neuron type and the basical operations we can do on it

Type Definitions, type class instances

data Neuron

Our Artificial Neuron type

Constructors

Neuron

Fields

threshold :: Double

weights :: UArr Double

func :: Double -> Double

Instances

Show Neuron

Neuron creation

createNeuronU :: Double -> UArr Double -> (Double -> Double) -> Neuron

Creates a Neuron with the given threshold, weights and transfer function

createNeuronHeavysideU :: Double -> UArr Double -> Neuron

Equivalent to `createNeuronU t ws heavyside'

createNeuronSigmoidU :: Double -> UArr Double -> Neuron

Equivalent to `createNeuronU t ws sigmoid'

createNeuron :: Double -> [Double] -> (Double -> Double) -> Neuron

Same as createNeuronU, with a list instead of an UArr for the weights (converted to UArr anyway)

createNeuronHeavyside :: Double -> [Double] -> Neuron

Same as createNeuronHeavysideU, with a list instead of an UArr for the weights (converted to UArr anyway)

createNeuronSigmoid :: Double -> [Double] -> Neuron

Same as createNeuronSigmoidU, with a list instead of an UArr for the weights (converted to UArr anyway)

Transfer functions

heavyside :: Double -> Double

The Heavyside function

sigmoid :: Double -> Double

The Sigmoid function

Neuron output computation

computeU :: Neuron -> UArr Double -> Double

Computes the output of a given Neuron for given inputs

compute :: Neuron -> [Double] -> Double

Computes the output of a given Neuron for given inputs

Neuron learning with Widrow-Hoff (Delta rule)

learnSampleU :: Double -> Neuron -> (UArr Double, Double) -> Neuron

Trains a neuron with the given sample, of the form (inputs, wanted_result) and the given learning ratio (alpha)

learnSample :: Double -> Neuron -> ([Double], Double) -> Neuron

learnSamplesU :: Double -> Neuron -> [(UArr Double, Double)] -> Neuron

Trains a neuron with the given samples and the given learning ratio (alpha)

learnSamples :: Double -> Neuron -> [([Double], Double)] -> Neuron

Trains a neuron with the given samples and the given learning ratio (alpha)