// EVALUATE -- RECURSIVE
double Neuron::evaluate()
{
// IF INPUT, RETURN VALUE
if( isInput() )
return value;
// ELSE EVALUATE
// FIND WEIGHTED SUM
double sum = 0.;
for( int i = 0; i < num_inputs; i++ )
sum += weights[i] * inputs[i]->evaluate();
// ACTIVATION FUNCTION
switch( config->activation_function )
{
case SIGMOID:
return fsigmoid( sum, config->activation_slope );
case BINARY:
return binary_activation( sum, threshold );
case HYPERBOLIC_TANGENT:
return hyperbolic_tangent( sum, config->activation_slope );
}
return 0.;
}
double ANN_activation_function(double _input, uint8_t _type, uint8_t is_backward)
{
double ret = 0;
switch (_type) {
case HYPERBOLIC_TANGENT:
if (is_backward == true)
{
ret = diff_hyperbolic_tangent(_input);
}
else
{
ret = hyperbolic_tangent(_input);
}
break;
case LOGISTIC:
if (is_backward == true)
{
ret = diff_logistic_function(_input);
}
else
{
logistic_function(_input);
}
break;
case LINEAR:
if (is_backward == true)
{
ret = diff_linear_function(_input);
}
else
{
ret = lenear_function(_input);
}
break;
default:
break;
}
return ret;
}
double hyperbolic_cotangent(double angle, enum angle_type atype)
{
return (1.0 / hyperbolic_tangent(angle, atype));
}
