const main = require('./main');
/**
* The activation functions a neuron can have.
*
* They can be used in the arguments of neural network's constructor.
*
* @hideconstructor
*/
class fun {
/**
* Sigmoid takes any value as input and outputs values in the range of 0.0 to 1.0.
*
* ```
* x => 1.0 / (1.0 + Math.exp(-x))
* ```
*/
static SIGMOID = main.FunJs.sigmoid;
/**
* Identity is a linear function where the output is equal to the input.
*
* ```
* x => x
* ```
*/
static IDENTITY = main.FunJs.identity;
/**
* Tanh is similar to sigmoid, but outputs values in the range of -1.0 and 1.0.
*
* ```
* x => Math.tanh(x)
* ```
*/
static TANH = main.FunJs.tanh;
/**
* LeakyReLU gives a small proportion of x if x is negative and x otherwise.
*
* ```
* x => (x < 0.0) ? 0.01 * x : x
* ```
*/
static LEAKY_RE_LU = main.FunJs.leakyReLU;
}
module.exports = fun;