/**
*
* @private
*
* @namespace multi
* @borrows workers as workers
*/
const multi = {
// Workers
workers: require('./workers/workers'),
/**
* Serializes a dataset
*
* @param {number[]} dataSet
* @return {number[]}
*/
serializeDataSet: function(dataSet) {
const serialized = [dataSet[0].input.length, dataSet[0].output.length];
for (let i = 0; i < dataSet.length; i++) {
var j;
for (j = 0; j < serialized[0]; j++) {
serialized.push(dataSet[i].input[j]);
}
for (j = 0; j < serialized[1]; j++) {
serialized.push(dataSet[i].output[j]);
}
}
return serialized;
},
/**
* Activate a serialized network
*
* @todo Add `@param` tag descriptions
* @todo Add `@param` tag defaults
* @todo Document `@param` tag "optional" or "required"
*
* @param {number[]} input
* @param {number[]} A
* @param {number[]} S
* @param {number[]} data
* @param {number[]} F
*
* @return {number[]} An output array
*/
activateSerializedNetwork: function(input, A, S, data, F) {
for (var i = 0; i < data[0]; i++) A[i] = input[i];
for (i = 2; i < data.length; i++) {
const index = data[i++];
const bias = data[i++];
const squash = data[i++];
const selfweight = data[i++];
const selfgater = data[i++];
S[index] = (selfgater === -1 ? 1 : A[selfgater]) * selfweight * S[index] + bias;
while (data[i] !== -2) {
S[index] += A[data[i++]] * data[i++] * (data[i++] === -1 ? 1 : A[data[i - 1]]);
}
A[index] = F[squash](S[index]);
}
const output = [];
for (i = A.length - data[1]; i < A.length; i++) output.push(A[i]);
return output;
},
/**
* Deserializes a dataset to an array of arrays
*
* @param {number[]} serializedSet - A dataset serialzed by serializeDataSet
*
* @return {Array[]} - An array with 2 entries, input data and output data -- each being an array themselves.
*/
deserializeDataSet: function(serializedSet) {
const set = [];
const sampleSize = serializedSet[0] + serializedSet[1];
for (let i = 0; i < (serializedSet.length - 2) / sampleSize; i++) {
const input = [];
for (var j = 2 + i * sampleSize; j < 2 + i * sampleSize + serializedSet[0]; j++) {
input.push(serializedSet[j]);
}
const output = [];
for (j = 2 + i * sampleSize + serializedSet[0]; j < 2 + i * sampleSize + sampleSize; j++) {
output.push(serializedSet[j]);
}
set.push(input);
set.push(output);
}
return set;
},
/**
* Tests a serialized data set
*
* @todo Add `@param` tag descriptions
* @todo Add `@returns` tag description
*
* @param {number[]} set - A dataset serialized by serializeDataSet
* @param {Function} cost
* @param {number[]} A
* @param {number[]} S
* @param {number[]} data
* @param {number[]} F
* @return {number} - Error
*/
testSerializedSet: function(set, cost, A, S, data, F) {
// Calculate how many samples are in the set
let error = 0;
for (let i = 0; i < set.length; i += 2) {
const output = this.activateSerializedNetwork(set[i], A, S, data, F);
error += cost(set[i + 1], output);
}
return error / (set.length / 2);
},
/**
* A list of compiled activation functions in a certain order.
*
* @todo Create an `ActivationFunction` class or type
* @todo Document this array using `ArrayFunction`
*/
activations: [
function(x) {
return 1 / (1 + Math.exp(-x));
},
function(x) {
return Math.tanh(x);
},
function(x) {
return x;
},
function(x) {
return x > 0 ? 1 : 0;
},
function(x) {
return x > 0 ? x : 0;
},
function(x) {
return x / (1 + Math.abs(x));
},
function(x) {
return Math.sin(x);
},
function(x) {
return Math.exp(-Math.pow(x, 2));
},
function(x) {
return (Math.sqrt(Math.pow(x, 2) + 1) - 1) / 2 + x;
},
function(x) {
return x > 0 ? 1 : -1;
},
function(x) {
return 2 / (1 + Math.exp(-x)) - 1;
},
function(x) {
return Math.max(-1, Math.min(1, x));
},
function(x) {
return Math.abs(x);
},
function(x) {
return 1 - x;
},
function(x) {
const a = 1.6732632423543772848170429916717;
return (x > 0 ? x : a * Math.exp(x) - a) * 1.0507009873554804934193349852946;
},
],
};
for (const i in multi) {
module.exports[i] = multi[i];
}