def main():
fileW = FromFinessFileMLR.createAnOutputFile()
model = mlr.MLR()
#Number of descriptor should be 396 and number of population should be 50 or more
numOfPop = 50
numOfFea = 396
unfit = 1000
# Final model requirements
R2req_train = .6
R2req_validate = .5
R2req_test = .5
TrainX, TrainY, ValidateX, ValidateY, TestX, TestY = FromDataFileMLR.getAllOfTheData(
)
TrainX, ValidateX, TestX = FromDataFileMLR.rescaleTheData(
TrainX, ValidateX, TestX)
unfit = 1000
fittingStatus = unfit
"""Create a population based on the number of features selected, in this case 10, from the pool of features"""
population = DifferentialEvolution.Create_A_Population(numOfPop, numOfFea)
fittingStatus, fitness = FromFinessFileMLR.validate_model(model,fileW, population, \
TrainX, TrainY, ValidateX, ValidateY, TestX, TestY)
import time # provides timing for benchmarks from numpy import * # provides complex math and array functions from sklearn import svm # provides Support Vector Regression import csv import math import sys # Local files created by me import mlr import FromDataFileMLR import FromFinessFileMLR import BPSO """Create the output file""" fileW = FromFinessFileMLR.createAnOutputFile() #fileW = 0 model = mlr.MLR() start = time.time() #Number of descriptor should be 396 and number of population should be 50 or more """Number of population""" numOfPop = 50 """Number of total features""" numOfFea = 396 # Final model requirements R2req_train = .6 R2req_validate = .5 R2req_test = .5 alpha = 0.5