# -*- coding: utf-8 -*-
"""
Python code of Spider-Monkey Optimization (SMO)
Coded by: Mukesh Saraswat (emailid: saraswatmukesh@gmail.com), Himanshu Mittal (emailid: himanshu.mittal224@gmail.com) and Raju Pal (emailid: raju3131.pal@gmail.com)
The code template used is similar to code given at link: https://github.com/himanshuRepo/CKGSA-in-Python
 and C++ version of the SMO at link: http://smo.scrs.in/

Reference: Jagdish Chand Bansal, Harish Sharma, Shimpi Singh Jadon, and Maurice Clerc. "Spider monkey optimization algorithm for numerical optimization." Memetic computing 6, no. 1, 31-47, 2014.
@link: http://smo.scrs.in/

-- Main.py: Calling the Spider-Monkey Optimization (SMO) Algorithm
                for minimizing of an objective Function

Code compatible:
 -- Python: 2.* or 3.*
"""
import smo
import benchmarks
import csv
import numpy
import time
import math


def selector(func_details, popSize, Iter, succ_rate, mean_feval):
    function_name = func_details[0]
    lb = func_details[1]
    ub = func_details[2]
    dim = func_details[3]
    acc_err = func_details[4]
    obj_val = func_details[5]


    x, succ_rate, mean_feval = smo.main(getattr(benchmarks, function_name), lb, ub, dim, popSize, Iter, acc_err,
                                        obj_val, succ_rate, mean_feval)
    return x, succ_rate, mean_feval


# Select number of repetitions for each experiment.
# To obtain meaningful statistical results, usually 30 independent runs are executed for each algorithm.
NumOfRuns = 2

# Select general parameters for all optimizers (population size, number of iterations)
PopulationSize = 10
Iterations = 500

mean_error = 0
total_feval = 0
mean1 = 0
var = 0
sd = 0
mean_feval = 0
succ_rate = 0
GlobalMins = numpy.zeros(NumOfRuns)

for k in range(0, NumOfRuns):

    func_details = benchmarks.getFunctionDetails()
    print("Run: {}".format(k + 1))
    x, succ_rate, mean_feval = selector(func_details, PopulationSize, Iterations, succ_rate, mean_feval)
    mean_error = mean_error + x.error;
    mean1 = mean1 + x.convergence[-1]
    total_feval = total_feval + x.feval
    GlobalMins[k] = x.convergence[-1]


mean1 = mean1 / NumOfRuns;
mean_error = mean_error / NumOfRuns
if (succ_rate > 0):
    mean_feval = mean_feval / succ_rate
total_feval = total_feval / NumOfRuns
for k in range(NumOfRuns):
    var = var + math.pow((GlobalMins[k] - mean1), 2)
var = var / NumOfRuns
sd = math.sqrt(var)

print(
    "Values after executing SMO: \n Mean Error:{} \n Mean Function eval:{} \n Total Function eval:{} \n Variance:{} \n STD:{}".format(
        mean_error, mean_feval, total_feval, var, sd))