def benchmark_adagrad(self):
bmsvmsgd = BenchmarkSGDAda.BenchmarkSGDAda(
exp_name=self.exp_name,
training_file=self.training_file,
testing_file=self.testing_file,
alpha=self.alpha,
features=self.n_features,
epochs=self.epochs,
labelfix=self.labelfix,
randomize=self.randomize,
split=self.split,
auto=True,
bulk=self.bulk)
bmsvmsgd.load_data()
bmsvmsgd.train()
bmsvmsgd.test()
bmsvmsgd.stats()
tolerance = 0.01
bmsvmsgd = BenchmarkSgdSvm.BenchmarkSgdSVM(exp_name=exp_name, training_file=training_file,
testing_file=testing_file,
alpha=alpha, features=features, epochs=epochs,
labelfix=labelfix, randomize=randomize, split=split,
auto=True)
bmsvmsgdmomentum = BenchmarkSGDMomentum.BenchmarkSGDMomentum(exp_name=exp_name,
training_file=training_file,
testing_file=testing_file,
alpha=alpha, C=C, gamma=gamma,
features=features, epochs=epochs,
labelfix=labelfix,
randomize=randomize, split=split)
bmsvmsgdada = BenchmarkSGDAda.BenchmarkSGDAda(exp_name=exp_name, training_file=training_file,
testing_file=testing_file,
alpha=alpha, features=features, epochs=epochs, labelfix=labelfix,
randomize=randomize, split=split, auto=True, bulk=bulk)
bmsvmsgdrmsprop = BenchmarkSGDRMSProp.BenchmarkSGDRMSProp(exp_name=exp_name, training_file=training_file,
testing_file=testing_file,
alpha=alpha, features=features, epochs=epochs, labelfix=labelfix,
randomize=randomize, split=split, auto=True, bulk=bulk)
bmsvmsgdadam = BenchmarkSGDAdam.BenchmarkSGDAdam(exp_name=exp_name, training_file=training_file,
testing_file=testing_file,
alpha=alpha, features=features, epochs=epochs, labelfix=labelfix,
randomize=randomize, split=split, auto=True, bulk=bulk)
x_training, y_training, x_testing, y_testing = load_light_data(training_file=training_file, testing_file=testing_file, split=split)