def survival_stat(filename, thresholds = None): data = np.array(read_data_file(filename, ",")) D, t = parse_data(data, inputcols = (2, 3, 4, 5, 6, 7, 8, 9, 10), ignorerows = [0], normalize = False) T = D[:, (2, 3)] outputs = D[:, (-1, 3)] C = get_C_index(T, outputs) print("C-index: " + str(C)) print("Genetic error: " + str(1 / C)) th = kaplanmeier(D, 2, 3, -1, threshold = thresholds) print("Threshold dividing the set in two equal pieces: " + str(th)) if plt: plt.show()
Created on Jun 7, 2011 @author: jonask ''' from kalderstam.neural.error_functions.sum_squares import total_error from kalderstam.neural.network import build_feedforward from kalderstam.util.filehandling import parse_data from kalderstam.neural.training.gradientdescent import traingd from kalderstam.neural.training.davis_genetic import train_evolutionary import numpy xor_set = [[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 0]] xor_set = numpy.array(xor_set) P, T = parse_data(xor_set, targetcols = 2, inputcols = [0, 1], normalize = False) net = build_feedforward(2, 4, 1) print("Error before training: " + str(total_error(T, net.sim(P)))) net = traingd(net, (P, T), (None, None), epochs = 1000, learning_rate = 0.1, block_size = 0) print("Error after training: " + str(total_error(T, net.sim(P)))) net = build_feedforward(2, 4, 1) print("Error before genetic training: " + str(total_error(T, net.sim(P)))) net = train_evolutionary(net, (P, T), (None, None), epochs = 100, population_size = 100) print("Error after genetic training: " + str(total_error(T, net.sim(P))))