def custom_plot(self, additional): for k in additional: if(k == Simulation.DISCRETIZE): for i in range(self.examples.noutputs): for j in range(self.nbr_epoch): if(self.plots['discretize_div'][i][j] != 0): self.plots['discretize'][i][j] /= (self.plots['discretize_div'][i][j] * (self.nbDiscre ** self.nbDiscre)) colors = [(0.2, 0.8, 0.88), 'b', 'g', 'r', 'c', 'm', 'y', 'k', (0.8, 0.1, 0.8), (0., 0.2, 0.5)] fig = plt.figure() ax = fig.add_subplot(111, projection='3d') for j in range(self.examples.noutputs): ax.scatter([self.plots['discretize'][j][k] for k in self.plots['discretize_valid'][j]], [j] * len(self.plots['discretize_valid'][j]), self.plots['discretize_valid'][j], color=colors[j], marker='x') ax.set_xlabel('DISCRETIZED VALUE') ax.set_ylabel('SHAPE') ax.set_zlabel('EPOCH') path = "/tmp/pyplot.%s.%s.png" % (sys.argv[0], time.strftime("%m-%d-%H-%M-%S", time.localtime())) plt.savefig(path) plt.show() plt.title('Discretize hidden layer') plt.ylabel('DISCRETIZED VALUE') plt.xlabel("EPOCHS") for j in range(self.examples.noutputs): plt.plot(self.plots['discretize_valid'][j], [self.plots['discretize'][j][k] for k in self.plots['discretize_valid'][j]], '.', color=colors[j]) path = "/tmp/pyplot.%s.%s.png" % (sys.argv[0], time.strftime("%m-%d-%H-%M-%S", time.localtime())) try: plt.savefig(path) except ValueError: print('Cannot save discretize_cloud') try: plt.show() except ValueError: print('Cannot display discretize_cloud') elif(k == Simulation.PROTOTYPE): lplot = [[0. for _ in range(self.examples.ninputs)] for _ in range(self.examples.noutputs)] for network in self.networks: for i in range(len(self.examples.inputs)): network['FoN'].calc_output(self.examples.inputs[i]) network['SoN'].calc_output(network['FoN'].stateHiddenNeurons) im = index_max(self.examples.outputs[i]) for j in range(self.examples.ninputs): lplot[im][j] += network['SoN'].stateOutputNeurons[j] fig = plt.figure() plt.clf() for i in range(self.examples.noutputs): rpr.show_repr(lplot[i], self.width, fig, 250 + i, i) path = "/tmp/pyplot.%s.%s.png" % (sys.argv[0], time.strftime("%m-%d-%H-%M-%S", time.localtime())) plt.savefig(path) plt.show()
def prototypes(self): lplot = [[0. for _ in range(self.examples.ninputs)] for _ in range(self.examples.noutputs)] for i in range(self.examples.noutputs): for j in range(self.examples.ninputs): for k in range(len(self.examples.inputs)): if(i == index_max(self.examples.outputs[k])): lplot[i][j] += self.examples.inputs[k][j] fig = plt.figure() plt.clf() for i in range(self.examples.noutputs): rpr.show_repr(lplot[i], 16, fig, 250 + i, i) path = "/tmp/pyplot.%s.%s.png" % (sys.argv[0], time.strftime("%m-%d-%H-%M-%S", time.localtime())) plt.savefig(path) plt.show()
def prototypes(self): lplot = [[0. for _ in range(self.examples.ninputs)] for _ in range(self.examples.noutputs)] for i in range(self.examples.noutputs): for j in range(self.examples.ninputs): for k in range(len(self.examples.inputs)): if (i == index_max(self.examples.outputs[k])): lplot[i][j] += self.examples.inputs[k][j] fig = plt.figure() plt.clf() for i in range(self.examples.noutputs): rpr.show_repr(lplot[i], 16, fig, 250 + i, i) path = "/tmp/pyplot.%s.%s.png" % ( sys.argv[0], time.strftime("%m-%d-%H-%M-%S", time.localtime())) plt.savefig(path) plt.show()
def custom_plot(self, additional): for k in additional: if (k == Simulation.DISCRETIZE): for i in range(self.examples.noutputs): for j in range(self.nbr_epoch): if (self.plots['discretize_div'][i][j] != 0): self.plots['discretize'][i][j] /= ( self.plots['discretize_div'][i][j] * (self.nbDiscre**self.nbDiscre)) colors = [(0.2, 0.8, 0.88), 'b', 'g', 'r', 'c', 'm', 'y', 'k', (0.8, 0.1, 0.8), (0., 0.2, 0.5)] fig = plt.figure() ax = fig.add_subplot(111, projection='3d') for j in range(self.examples.noutputs): ax.scatter([ self.plots['discretize'][j][k] for k in self.plots['discretize_valid'][j] ], [j] * len(self.plots['discretize_valid'][j]), self.plots['discretize_valid'][j], color=colors[j], marker='x') ax.set_xlabel('DISCRETIZED VALUE') ax.set_ylabel('SHAPE') ax.set_zlabel('EPOCH') path = "/tmp/pyplot.%s.%s.png" % ( sys.argv[0], time.strftime("%m-%d-%H-%M-%S", time.localtime())) plt.savefig(path) plt.show() plt.title('Discretize hidden layer') plt.ylabel('DISCRETIZED VALUE') plt.xlabel("EPOCHS") for j in range(self.examples.noutputs): plt.plot(self.plots['discretize_valid'][j], [ self.plots['discretize'][j][k] for k in self.plots['discretize_valid'][j] ], '.', color=colors[j]) path = "/tmp/pyplot.%s.%s.png" % ( sys.argv[0], time.strftime("%m-%d-%H-%M-%S", time.localtime())) try: plt.savefig(path) except ValueError: print('Cannot save discretize_cloud') try: plt.show() except ValueError: print('Cannot display discretize_cloud') elif (k == Simulation.PROTOTYPE): lplot = [[0. for _ in range(self.examples.ninputs)] for _ in range(self.examples.noutputs)] for network in self.networks: for i in range(len(self.examples.inputs)): network['FoN'].calc_output(self.examples.inputs[i]) network['SoN'].calc_output( network['FoN'].stateHiddenNeurons) im = index_max(self.examples.outputs[i]) for j in range(self.examples.ninputs): lplot[im][j] += network['SoN'].stateOutputNeurons[ j] fig = plt.figure() plt.clf() for i in range(self.examples.noutputs): rpr.show_repr(lplot[i], self.width, fig, 250 + i, i) path = "/tmp/pyplot.%s.%s.png" % ( sys.argv[0], time.strftime("%m-%d-%H-%M-%S", time.localtime())) plt.savefig(path) plt.show()