labels.append(label)
for i in range(0, 10):
arr = [random.randint(10, 20) * np.sign(random.random() - 0.5) for x in range(0, 2)]
label = 'yes' # Note: this is artificially misclassified
arrs.append(arr)
labels.append(label)
ann = Ann(arrs, labels, n_h=2)
(models, test_accuracies, test_costs) = ann.train()
best_test_accuracy = 0
best_i = -1
for i in range(0, len(test_accuracies)):
if (test_accuracies[i] > best_test_accuracy):
best_test_accuracy = test_accuracies[i]
best_i = i
if (best_i > -1):
model_name = models[i].name
directory = '../Ann-models'
path_to_file = directory + '/' + model_name
if not os.path.exists(directory):
os.makedirs(directory)
pickle.dump(models[i], open(path_to_file, 'wb'))
else:
logger.error('Error!')
if __name__ == "__main__":
Ann.init_logger('debug')
unittest.main()
for x in range(0, 2)
]
label = 'yes' # Note: this is artificially misclassified
arrs.append(arr)
labels.append(label)
ann = Ann(arrs, labels, n_h=2)
(models, test_accuracies, test_costs) = ann.train()
best_test_accuracy = 0
best_i = -1
for i in range(0, len(test_accuracies)):
if (test_accuracies[i] > best_test_accuracy):
best_test_accuracy = test_accuracies[i]
best_i = i
if (best_i > -1):
model_name = models[i].name
directory = '../Ann-models'
path_to_file = directory + '/' + model_name
if not os.path.exists(directory):
os.makedirs(directory)
pickle.dump(models[i], open(path_to_file, 'wb'))
else:
logger.error('Error!')
if __name__ == "__main__":
Ann.init_logger('debug')
unittest.main()
