def run_iris_comparison(num=25):
""" Compare a few different test and
training configurations
"""
print("Running neural network {} times each for three different sets of training and testing files".format(num))
test_files = ['iris_tes.txt', 'iris_tes50.txt',\
'iris_tes30.txt']
train_files = ['iris_tra.txt', 'iris_tra100.txt',\
'iris_tra120.txt']
for i in range(0, len(test_files)):
print("trainfile = {} testfile = {}".format(train_files[i], test_files[i]))
config_obj = openJsonConfig('conf/annconfig_iris.json')
summary = {}
for i in range(0, len(test_files)):
config_obj['testing_file'] = test_files[i]
config_obj['training_file'] = train_files[i]
config_obj['plot_error'] = False
config_obj['test'] = False
crates = []
for j in range(0, num):
nn = NeuralNetwork(config_obj)
nn.back_propagation()
cmat, crate, cout = nn.classification_test(nn.testing_data, nn.weights_best)
crates.append(crate)
summary[config_obj['testing_file']] =\
nn_stats(np.array(crates))
print print_stat_summary(summary)
def test_nn(config):
nn = NeuralNetwork(config)
nn.back_propagation()
cmat, crate, cout = nn.classification_test(nn.testing_data, nn.weights_best)
print cmat
print crate
