A very simple solution is to use a decent table-driven approximation. You don’t actually need a lot of data if you reduce your inputs correctly. exp(a)==exp(a/2)*exp(a/2), which means you really only need to calculate exp(x) for 1 < x < 2. Over that range, a runga-kutta approximation would give reasonable results with ~16 entries IIRC. … Read more
You can use exp(x) function of math library, which is same as e^x. Hence you may write your code as: import math x.append(1 – math.exp( -0.5 * (value1*value2)**2)) I have modified the equation by replacing 1/2 as 0.5. Else for Python <2.7, we’ll have to explicitly type cast the division value to float because Python … Read more
The exponential function is e^x where e is a mathematical constant called Euler’s number, approximately 2.718281. This value has a close mathematical relationship with pi and the slope of the curve e^x is equal to its value at every point. np.exp() calculates e^x for each value of x in your input array.