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typings.d.ts
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typings.d.ts
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declare namespace Neural {
interface Network<T> {
process: (input: T) => ProcessResult[][]
costSummaryOf: (trainingDataFromInput: TrainingDataset<T>) => CostSummary
trainWithBackwardPropagation: (trainingDataFromInput: TrainingDataset<T>) => TrainingResult<T>
trainWithGradientDescent: (trainingDataFromInput: TrainingDataset<T>) => TrainingResult<T>
getWeightsAndBiases: () => WeightsAndBias[][]
apply: (weightsAndBiases: WeightsAndBias[][]) => Network<T>
clone: () => Network<T>
getNeurons: () => Neuron[][]
toString: () => string
}
interface Neuron {}
interface SavedValue {
neuron: Neuron
number: number
}
type Link = SavedValue & { dead?: boolean }
type ProcessResult = SavedValue & { rawValue: number }
interface TrainingDataset<T> {
inputs: T[]
expectedResults?: number[][]
rounds?: number
theory?: (input: T) => number[]
}
interface CostSummary {
costs: number[]
average: number
}
interface TrainingResult<T> {
weightsAndBiases: WeightsAndBias[][]
remainingCost: number
remainingCostList: number[]
trainedNetwork: Network<T>
}
interface WeightsAndBias {
bias?: number
weights?: number[]
}
interface Derivatives {
outputError: number
inputError: number
accumulatedFromInputError: number
numberOfAccumulatedErrors: number
linksDerivatives: {
neuron: Neuron
outputError: number
accumulatedError: number
numberOfAccumulatedErrors: number
}[]
}
type AfterEachNeuronTraining<T> = (
network: Network<T>,
round: number,
iteration: number,
total: number
) => void
type ActivationFunction = {
apply: (x: number) => number
derivative: (x: number) => number
}
type ActivationFunctionName = keyof {
[name: string]: ActivationFunction
}
}