def validate(name, X, Y, normal_class):
anomaly = [x for x, y in zip(X, Y) if y != normal_class]
y_anomaly = [y for y in Y if y != normal_class]
normal = [x for x, y in zip(X, Y) if y == normal_class]
y_normal = [y for y in Y if y == normal_class]
nu_range = np.logspace(-3, 0, 30) * 0.2
gamma_range = np.logspace(-5, 4, 10)
validator = Validator(X, Y, normal, y_normal, anomaly, y_anomaly)
"""
hypersphere_valid = validator.hyperspherical_predictor(name, nu_range, gamma_range)
print hypersphere_valid[:-3]
one_class_svm_valid = validator.one_class_svm(name, nu_range, gamma_range)
print one_class_svm_valid[:-3]
plt.plot(hypersphere_valid[-3], hypersphere_valid[-2], 'b')
plt.plot(hypersphere_valid[-3], hypersphere_valid[-1], 'b--')
plt.plot(one_class_svm_valid[-3], one_class_svm_valid[-2], 'g')
plt.plot(one_class_svm_valid[-3], one_class_svm_valid[-1], 'g--')
#plt.show()
plt.savefig('graphs/' + name + '.png')
"""
print validator.svc_biclass(name, nu_range, gamma_range)
print validator.multiclass_hyperspherical_predictor(name, nu_range, gamma_range)
print validator.multiclass_one_class_svm(name, nu_range, gamma_range)
print validator.svc(name, nu_range, gamma_range)
