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()