def main():
# BPSO parameters
num_pop = 50
num_feat = 385
num_gens = 1000
# initialize objects
model = mlr.MLR()
fdf = FromDataFileMLR.FromDataFileMLR()
fff = FromFinessFileMLR.FromFinessFileMR(fdf)
bpso = BinaryParticleSwarmOptimization(model, fff, num_pop, num_feat,
num_gens)
# load in data from files
trainX, trainY, validateX, validateY, testX, testY = fdf.getAllOfTheData()
trainX, validateX, testX = fdf.rescaleTheData(trainX, validateX, testX)
# BPSO algorithm
bpso.create_initial_population()
bpso.evaluate_population(trainX, trainY, validateX, validateY, testX,
testY)
bpso.create_initial_velocity()
initial_local_best_matrix, initial_local_fitness = bpso.create_initial_local_best_matrix(
)
bpso.create_initial_global_best_row()
bpso.evolve_population(initial_local_best_matrix, initial_local_fitness,
trainX, trainY, validateX, validateY, testX, testY)
def main():
# GA parameters
num_pop = 50
num_feat = 385
num_gens = 1000
# initialize objects
model = mlr.MLR()
fdf = FromDataFileMLR.FromDataFileMLR()
fff = FromFinessFileMLR.FromFinessFileMR(fdf)
GA = GeneticAlgorithm(model, fff, num_pop, num_feat, num_gens)
# load in data from files
trainX, trainY, validateX, validateY, testX, testY = fdf.getAllOfTheData()
trainX, validateX, testX = fdf.rescaleTheData(trainX, validateX, testX)
# genetic algorithm
GA.create_initial_population()
GA.evaluate_population(trainX, trainY, validateX, validateY, testX, testY)
GA.evolve_population(trainX, trainY, validateX, validateY, testX, testY)
def main():
# DE parameters
num_pop = 50
num_feat = 385
num_gens = 100
# initialize objects
model = mlr.MLR()
FDF = FromDataFileMLR.FromDataFileMLR()
FFF = FromFinessFileMLR.FromFinessFileMR(FDF)
DE = DifferentialEvolution(model, FFF, num_pop, num_feat, num_gens)
# load in data from files
trainX, trainY, validateX, validateY, testX, testY = FDF.getAllOfTheData()
trainX, validateX, testX = FDF.rescaleTheData(trainX, validateX, testX)
# differential evolution algorithm
DE.create_initial_population()
DE.evaluate_population(trainX, trainY, validateX, validateY, testX, testY)
DE.evolve_population(trainX, trainY, validateX, validateY, testX, testY)