19 y=c/(1.0+
exp(-(a+b*x)));
36 y=c/(1.0+
exp(-(a+b*x)));
53 y=c/(1.0+
exp(-(a+b*x)));
178 y=c/(1.0+
exp(-(a+b*x)));
193 y=c/(1.0+
exp(-(a+b*x)));
208 y=c/(1.0+
exp(-(a+b*x)));
Base class for dvariable.
d3_array elem_prod(const d3_array &a, const d3_array &b)
Returns d3_array results with computed elements product of a(i, j, k) * b(i, j, k).
Description not yet available.
dvariable logistic3(const double &x, const prevariable &a, const prevariable &b, const prevariable &c)
logistic function with carrying capacity c; scalar
void RETURN_ARRAYS_DECREMENT(void)
Decrements gradient_structure::RETURN_ARRAYS_PTR.
Vector of double precision numbers.
Description not yet available.
d3_array elem_div(const d3_array &a, const d3_array &b)
Returns d3_array results with computed elements division of a(i, j, k) / b(i, j, k).
Description not yet available.
Description not yet available.
d3_array exp(const d3_array &arr3)
Returns d3_array results with computed exp from elements in arr3.
Description not yet available.
void RETURN_ARRAYS_INCREMENT(void)
Increments gradient_structure::RETURN_ARRAYS_PTR.
Fundamental data type for reverse mode automatic differentiation.