def Main():
x_train = pd.read_csv('Dataset/xtrain_3spirals.txt', sep=' ', header=None)
x_test = pd.read_csv('Dataset/xtest_3spirals.txt', sep=' ', header=None)
d_train = pd.read_csv('Dataset/dtrain_3spirals.txt', sep=',', header=None)
d_test = pd.read_csv('Dataset/dtest_3spirals.txt', sep=',', header=None)
## Aplication of MLP algorithm
mlp = MLP(15000, 0.15, 0.000001, [4, 3], 0.5)
mlp.train(x_train.to_numpy(), d_train.to_numpy())
new_classes = mlp.application(x_test.to_numpy())
comparative = np.concatenate((d_test.to_numpy(), new_classes), 1)
print("Matrix of comparative between classes")
print(comparative)
print("------------------------------")
hit_table = np.zeros((len(new_classes), 1))
for row in range(len(new_classes)):
if all(d_test.to_numpy()[row] == new_classes[row]):
hit_table[row] = 1
tax_hit = sum(hit_table) / len(new_classes)
print("------------------------------")
print("Matrix of hits")
print(hit_table)
print("------------------------------")
print("Tax of hits: " + str(tax_hit))
real_classes[row,1] = 1
'''
x_train = pd.read_csv('Dataset/xtrain_3spirals.txt', sep=' ', header=None)
x_test = pd.read_csv('Dataset/xtest_3spirals.txt', sep=' ', header=None)
#converterClasses('Dataset/dtest_3spirals.txt')
#converterClasses('Dataset/dtrain_3spirals.txt')
d_train = pd.read_csv('Dataset/dtrain_3spirals.txt', sep=',', header=None)
d_test = pd.read_csv('Dataset/dtest_3spirals.txt', sep=',', header=None)
## Aplication of MLP algorithm
mlp = MLP(15000, 0.15, 0.000001, [4, 3], 0.5)
mlp.train(x_train.to_numpy(), d_train.to_numpy())
new_classes = mlp.application(x_test.to_numpy())
comparative = np.concatenate((d_test.to_numpy(), new_classes), 1)
print("Matrix of comparative between classes")
print(comparative)
print("------------------------------")
hit_table = np.zeros((len(new_classes), 1))
for row in range(len(new_classes)):
if all(d_test.to_numpy()[row] == new_classes[row]):
hit_table[row] = 1
tax_hit = sum(hit_table) / len(new_classes)
