def runClassifier(params, settings, fold):
classifier.printParameters('Model Parameters', params)
classifier.printParameters('Classifier Settings', settings)
populationsInput = list()
populationsNoiseSource = list()
populationsRN = list()
populationsPN = list()
populationsAN = list()
projectionsPNAN = list() #keep handle to these for saving learnt weights
if settings['LEARNING']:
totalSimulationTime = float(settings['OBSERVATION_EXPOSURE_TIME_MS'] *
settings['NUM_OBSERVATIONS'])
else:
totalSimulationTime = float(settings['OBSERVATION_EXPOSURE_TIME_MS'] *
settings['NUM_OBSERVATIONS_TEST'])
print 'Total Simulation Time will be', totalSimulationTime
DT = 1.0 #ms Integration timestep for simulation
classifier.setupModel(params, settings, DT, totalSimulationTime,
populationsInput, populationsNoiseSource,
populationsRN, populationsPN, populationsAN,
projectionsPNAN)
utils.recordPopulations(populationsInput, settings['RECORD_POP_INPUT'])
utils.recordPopulations(populationsNoiseSource,
settings['RECORD_POP_NOISE_SOURCE'])
utils.recordPopulations(populationsRN, settings['RECORD_POP_RN'])
utils.recordPopulations(populationsPN, settings['RECORD_POP_PN'])
utils.recordPopulations(populationsAN, settings['RECORD_POP_AN'])
#run the model for the whole learning or the whole testing period
classifier.run(totalSimulationTime)
fig1 = plt.figure(figsize=(20, 20))
plt.xlabel('Time[ms]', fontsize=16)
plt.ylabel('Neurons', fontsize=16)
title = 'Testing'
if settings['LEARNING']:
title = 'Training'
title = title + ' - Odour Classification - ' + str(params['NUM_VR']) + \
' Virtual Receptors'
fig1.suptitle(title, fontsize=18)
indexOffset = 0
indexOffset = 1 + utils.plotAllSpikes(populationsInput,
totalSimulationTime, indexOffset,
settings['RECORD_POP_INPUT'])
indexOffset = 1 + utils.plotAllSpikes(populationsNoiseSource,
totalSimulationTime, indexOffset,
settings['RECORD_POP_NOISE_SOURCE'])
indexOffset = 1 + utils.plotAllSpikes(populationsRN, totalSimulationTime,
indexOffset,
settings['RECORD_POP_RN'])
indexOffset = 1 + utils.plotAllSpikes(populationsPN, totalSimulationTime,
indexOffset,
settings['RECORD_POP_PN'])
indexOffset = 1 + utils.plotAllSpikes(populationsAN, totalSimulationTime,
indexOffset,
settings['RECORD_POP_AN'])
filename = 'RasterPlot-Testing-fold' + str(fold) + '.pdf'
if settings['LEARNING']:
filename = 'RasterPlot-Training-fold' + str(fold) + '.pdf'
plt.savefig(filename)
plt.close()
(fig2, (ax1, ax2, ax3)) = plt.subplots(3, 1, figsize=(20, 20), sharex=True)
plt.axes(ax1)
utils.plotAllSpikes(populationsRN, totalSimulationTime, 0,
settings['RECORD_POP_RN'])
plt.axes(ax2)
utils.plotAllSpikes(populationsPN, totalSimulationTime, 0,
settings['RECORD_POP_PN'])
plt.axes(ax3)
utils.plotAllSpikes(populationsAN, totalSimulationTime, 0,
settings['RECORD_POP_AN'])
ax1.set_title('RN layer spikes', fontsize=30)
ax2.set_title('PN layer spikes', fontsize=30)
ax3.set_title('AN layer spikes', fontsize=30)
ax3.set_xlabel('Simulation time[ms]', fontsize=30)
ax3.set_ylabel('Neuron indices', fontsize=30)
ax3.tick_params(labelsize=20)
ax2.tick_params(labelsize=20)
ax1.tick_params(labelsize=20)
filename = 'Separated_RasterPlot-Testing-fold' + str(fold) + '.pdf'
if settings['LEARNING']:
filename = 'Separated_RasterPlot-Training-fold' + str(fold) + '.pdf'
plt.savefig(filename)
plt.close()
# fig.add_subplot(2,1,2)
# utils.plotAllSpikes(populationsAN,totalSimulationTime, 0, settings['RECORD_POP_AN'])
#if in the learning stage
if settings['LEARNING']:
#store the weight values learnt via plasticity, these will be reloaded as
#static weights for test stage
classLabels = utils.loadListFromCsvFile(settings['CLASS_LABELS_TRAIN'],
True)
classifier.saveLearntWeightsPNAN(settings, params, projectionsPNAN,
len(populationsPN),
len(populationsAN))
winningClassesByObservation, winningSpikeCounts = classifier.calculateWinnersAN(
settings, populationsAN, classLabels)
scorePercent = classifier.calculateScore(winningClassesByObservation,
classLabels)
else:
#save the AN layer spike data from the testing run.
#This data will be interrogated to find the winning class (most active AN pop)
#during the presentation of each test observation
#classifier.saveSpikesAN(settings,populationsAN)
classLabels = utils.loadListFromCsvFile(settings['CLASS_LABELS_TEST'],
True)
winningClassesByObservation, winningSpikeCounts = classifier.calculateWinnersAN(
settings, populationsAN, classLabels)
scorePercent = classifier.calculateScore(winningClassesByObservation,
classLabels)
utils.saveListAsCsvFile(winningClassesByObservation,
settings['CLASSIFICATION_RESULTS_PATH'])
utils.saveListAsCsvFile(winningSpikeCounts,
settings['SPIKE_COUNT_RESULTS_PATH'])
classifier.end()
#write a marker file to allow invoking programs to know that the Python/Pynn run completed
utils.saveListToFile(['Pynn Run complete'], settings['RUN_COMPLETE_FILE'])
print 'PyNN run completed.'
return scorePercent
def runClassifier(params, settings, fold):
classifier.printParameters('Model Parameters',params)
classifier.printParameters('Classifier Settings',settings)
populationsInput = list()
populationsNoiseSource = list()
populationsRN = list()
populationsPN = list()
populationsAN = list()
projectionsPNAN = list() #keep handle to these for saving learnt weights
if settings['LEARNING']:
totalSimulationTime = float(settings['OBSERVATION_EXPOSURE_TIME_MS'] *
settings['NUM_OBSERVATIONS'])
else:
totalSimulationTime = float(settings['OBSERVATION_EXPOSURE_TIME_MS'] *
settings['NUM_OBSERVATIONS_TEST'])
print 'Total Simulation Time will be', totalSimulationTime
DT = 1.0 #ms Integration timestep for simulation
classifier.setupModel(params, settings, DT, totalSimulationTime,
populationsInput, populationsNoiseSource,
populationsRN,populationsPN,populationsAN,projectionsPNAN)
utils.recordPopulations(populationsInput,settings['RECORD_POP_INPUT'])
utils.recordPopulations(populationsNoiseSource,settings['RECORD_POP_NOISE_SOURCE'])
utils.recordPopulations(populationsRN,settings['RECORD_POP_RN'])
utils.recordPopulations(populationsPN,settings['RECORD_POP_PN'])
utils.recordPopulations(populationsAN,settings['RECORD_POP_AN'])
#run the model for the whole learning or the whole testing period
classifier.run(totalSimulationTime)
fig1 = plt.figure(figsize=(20,20))
plt.xlabel('Time[ms]', fontsize = 16)
plt.ylabel('Neurons', fontsize = 16)
title = 'Testing'
if settings['LEARNING']:
title = 'Training'
title = title + ' - Odour Classification - ' + str(params['NUM_VR']) + \
' Virtual Receptors'
fig1.suptitle(title, fontsize = 18)
indexOffset = 0
indexOffset = 1 + utils.plotAllSpikes(populationsInput,
totalSimulationTime, indexOffset,
settings['RECORD_POP_INPUT'])
indexOffset = 1 + utils.plotAllSpikes(populationsNoiseSource,
totalSimulationTime, indexOffset,
settings['RECORD_POP_NOISE_SOURCE'])
indexOffset = 1 + utils.plotAllSpikes(populationsRN,
totalSimulationTime,
indexOffset,settings['RECORD_POP_RN'])
indexOffset = 1 + utils.plotAllSpikes(populationsPN,
totalSimulationTime,
indexOffset,settings['RECORD_POP_PN'])
indexOffset = 1 + utils.plotAllSpikes(populationsAN,
totalSimulationTime,
indexOffset,settings['RECORD_POP_AN'])
filename = 'RasterPlot-Testing-fold' + str(fold)+'.pdf'
if settings['LEARNING']:
filename = 'RasterPlot-Training-fold' + str(fold)+'.pdf'
plt.savefig(filename)
plt.close()
(fig2, (ax1, ax2, ax3)) = plt.subplots(3, 1, figsize=(20,20), sharex=True)
plt.axes(ax1)
utils.plotAllSpikes(populationsRN,totalSimulationTime,0, settings['RECORD_POP_RN'])
plt.axes(ax2)
utils.plotAllSpikes(populationsPN,totalSimulationTime,0, settings['RECORD_POP_PN'])
plt.axes(ax3)
utils.plotAllSpikes(populationsAN,totalSimulationTime,0, settings['RECORD_POP_AN'])
ax1.set_title('RN layer spikes', fontsize = 30)
ax2.set_title('PN layer spikes', fontsize = 30)
ax3.set_title('AN layer spikes', fontsize = 30)
ax3.set_xlabel('Simulation time[ms]', fontsize = 30)
ax3.set_ylabel('Neuron indices', fontsize = 30)
ax3.tick_params(labelsize=20)
ax2.tick_params(labelsize=20)
ax1.tick_params(labelsize=20)
filename = 'Separated_RasterPlot-Testing-fold' + str(fold)+'.pdf'
if settings['LEARNING']:
filename = 'Separated_RasterPlot-Training-fold' + str(fold)+'.pdf'
plt.savefig(filename)
plt.close()
# fig.add_subplot(2,1,2)
# utils.plotAllSpikes(populationsAN,totalSimulationTime, 0, settings['RECORD_POP_AN'])
#if in the learning stage
if settings['LEARNING']:
#store the weight values learnt via plasticity, these will be reloaded as
#static weights for test stage
classLabels = utils.loadListFromCsvFile(settings['CLASS_LABELS_TRAIN'],True)
classifier.saveLearntWeightsPNAN(settings, params, projectionsPNAN,
len(populationsPN),len(populationsAN))
winningClassesByObservation, winningSpikeCounts = classifier.calculateWinnersAN(settings,populationsAN, classLabels)
scorePercent = classifier.calculateScore(winningClassesByObservation,classLabels)
else:
#save the AN layer spike data from the testing run.
#This data will be interrogated to find the winning class (most active AN pop)
#during the presentation of each test observation
#classifier.saveSpikesAN(settings,populationsAN)
classLabels = utils.loadListFromCsvFile(settings['CLASS_LABELS_TEST'],True)
winningClassesByObservation, winningSpikeCounts = classifier.calculateWinnersAN(settings,populationsAN, classLabels)
scorePercent = classifier.calculateScore(winningClassesByObservation, classLabels)
utils.saveListAsCsvFile(winningClassesByObservation,settings['CLASSIFICATION_RESULTS_PATH'])
utils.saveListAsCsvFile(winningSpikeCounts,settings['SPIKE_COUNT_RESULTS_PATH'])
classifier.end()
#write a marker file to allow invoking programs to know that the Python/Pynn run completed
utils.saveListToFile(['Pynn Run complete'],settings['RUN_COMPLETE_FILE'])
print 'PyNN run completed.'
return scorePercent
indexOffset = 1 + utils.plotAllSpikes(populationsPN, totalSimulationTime, indexOffset,settings['RECORD_POP_PN'])
indexOffset = 1 + utils.plotAllSpikes(populationsAN, totalSimulationTime, indexOffset,settings['RECORD_POP_AN'])
#if in the learning stage
if settings['LEARNING']:
#store the weight values learnt via plasticity, these will be reloaded as static weights for test stage
classifier.saveLearntWeightsPNAN(settings,params,projectionsPNAN,len(populationsPN),len(populationsAN))
else:
#save the AN layer spike data from the testing run.
#This data will be interrogated to find the winning class (most active AN pop)
#during the presentation of each test observation
#classifier.saveSpikesAN(settings,populationsAN)
winningClassesByObservation = classifier.calculateWinnersAN(settings,populationsAN)
classLabels = utils.loadListFromCsvFile(settings['CLASS_LABELS_PATH'],True)
scorePercent = classifier.calculateScore(winningClassesByObservation,classLabels)
utils.saveListAsCsvFile(winningClassesByObservation,settings['CLASSIFICATION_RESULTS_PATH'])
#make sure spinnaker shut down before blocking figure is put up
classifier.end()
if settings['SAVE_RASTER_PLOT_AS_PNG']:
filename = 'RasterPlot-Testing.png'
if settings['LEARNING']:
filename = 'RasterPlot-Training.png'
plt.savefig(filename)
#write a marker file to allow invoking programs to know that the Python/Pynn run completed
utils.saveListToFile(['Pynn Run complete'],settings['RUN_COMPLETE_FILE'])
if settings['DISPLAY_RASTER_PLOT']:
plt.show()