def loadRepeatedExperiments(expDir, killCellPercentRange, seedRange,
killCellAt):
meanAccuracy = []
stdAccuracy = []
for killCellPercent in killCellPercentRange:
accuracyList = []
for seed in range(10):
if killCellPercent == 0:
experiment = os.path.join(
expDir, "kill_cell_percent{:1.1f}seed{:1.1f}/0.log".format(
killCellPercent, seed))
else:
experiment = os.path.join(
expDir, "kill_cell_percent{:1.2f}seed{:1.1f}/0.log".format(
killCellPercent, seed))
expResults = readExperiment(experiment)
print "Load Experiment: ", experiment
(accuracy, x) = computeAccuracy(expResults['predictions'],
expResults['truths'],
expResults['iterations'],
resets=expResults['resets'],
randoms=expResults['randoms'])
idx = numpy.array([i for i in range(len(x)) if x[i] > killCellAt])
accuracy = numpy.array(accuracy)
accuracyList.append(
float(numpy.sum(accuracy[idx])) / len(accuracy[idx]))
meanAccuracy.append(numpy.mean(accuracyList))
stdAccuracy.append(numpy.std(accuracyList))
return meanAccuracy, stdAccuracy
def loadRepeatedExperiments(expDir, killCellPercentRange, seedRange, killCellAt):
meanAccuracy = []
stdAccuracy = []
for killCellPercent in killCellPercentRange:
accuracyList = []
for seed in range(10):
if killCellPercent == 0:
experiment = os.path.join(
expDir,"kill_cell_percent{:1.1f}seed{:1.1f}/0.log".format(
killCellPercent, seed))
else:
experiment = os.path.join(
expDir,"kill_cell_percent{:1.2f}seed{:1.1f}/0.log".format(
killCellPercent, seed))
expResults = readExperiment(experiment)
print "Load Experiment: ", experiment
(accuracy, x) = computeAccuracy(expResults['predictions'],
expResults['truths'],
expResults['iterations'],
resets=expResults['resets'],
randoms=expResults['randoms'])
idx = numpy.array([i for i in range(len(x)) if x[i] > killCellAt])
accuracy = numpy.array(accuracy)
accuracyList.append(float(numpy.sum(accuracy[idx])) / len(accuracy[idx]))
meanAccuracy.append(numpy.mean(accuracyList))
stdAccuracy.append(numpy.std(accuracyList))
return meanAccuracy, stdAccuracy
def loadExperiment(experiment):
print "Loading experiment ", experiment
data = readExperiment(experiment)
(accuracy, x) = computeAccuracy(data['predictions'],
data['truths'],
data['iterations'],
resets=data['resets'],
randoms=data['randoms'])
return (accuracy, x)
def loadExperiment(experiment):
print "Loading experiment ", experiment
data = readExperiment(experiment)
(accuracy, x) = computeAccuracy(data['predictions'],
data['truths'],
data['iterations'],
resets=data['resets'],
randoms=data['randoms'])
return (accuracy, x)
from plot import readExperiment
mpl.rcParams['pdf.fonttype'] = 42
pyplot.ion()
pyplot.close('all')
if __name__ == '__main__':
experiments = [
os.path.join("lstm/results", "high-order-noise",
"inject_noise_after0.0", "0.log"),
os.path.join("tm/results", "high-order-noise", "inject_noise_after0.0",
"0.log")
]
for experiment in experiments:
data = readExperiment(experiment)
(accuracy, x) = computeAccuracy(data['predictions'],
data['truths'],
data['iterations'],
resets=data['resets'],
randoms=data['randoms'])
plotAccuracy((accuracy, x),
data['trains'],
window=100,
type=type,
label='NoiseExperiment',
hideTraining=True,
lineSize=1.0)
pyplot.xlim([10500, 14500])
pyplot.xlabel('Number of elements seen')
plt.ion()
plt.close('all')
if __name__ == '__main__':
outdir = 'tm/result/'
KILLCELL_PERCENT = list(numpy.arange(7) / 10.0)
accuracyListTM = []
accuracyListLSTM = []
accuracyListELM = []
for killCellPercent in KILLCELL_PERCENT:
# HTM experiments
experiment = os.path.join(outdir, "kill_cell_percent{:1.1f}".format(
killCellPercent)) + '/0.log'
expResults = readExperiment(experiment)
killCellAt = 10000
(accuracy, x) = computeAccuracy(expResults['predictions'][killCellAt:],
expResults['truths'][killCellAt:],
expResults['iterations'][killCellAt:],
resets=expResults['resets'][killCellAt:],
randoms=expResults['randoms'][killCellAt:])
accuracyListTM.append(float(numpy.sum(accuracy)) / len(accuracy))
# LSTM experiments
experiment = 'lstm/results/high-order-distributed-random-kill-cell/' \
'kill_cell_percent' + "{:1.2f}".format(killCellPercent) + '/0.log'
expResults = readExperiment(experiment)
mpl.rcParams['pdf.fonttype'] = 42
pyplot.ion()
if __name__ == '__main__':
outdir = 'tm/result/'
KILLCELL_PERCENT = list(numpy.arange(7) / 10.0)
accuracyListTM = []
accuracyListLSTM = []
for killCellPercent in KILLCELL_PERCENT:
experiment = os.path.join(
outdir,
"kill_cell_percent{:1.1f}".format(killCellPercent)) + '/0.log'
(predictions, truths, iterations, resets, randoms, trains,
killCell) = readExperiment(experiment)
killCellAt = 10000
(accuracy, x) = computeAccuracy(predictions[killCellAt:],
truths[killCellAt:],
iterations[killCellAt:],
resets=resets[killCellAt:],
randoms=randoms[killCellAt:])
accuracyListTM.append(float(numpy.sum(accuracy)) / len(accuracy))
experiment = 'lstm/results/high-order-distributed-random-kill-cell/' \
'kill_cell_percent' + "{:1.2f}".format(killCellPercent) + '/0.log'
(predictions, truths, iterations, resets, randoms,
killCell) = readExperiment(experiment)
lengths = [10, 20, 40, 60, 80, 100]
tmResults = os.path.join("tm/results",
"high-order-variable-length")
try:
# Load raw experiment results
# You have to run the experiments in ./tm
# python tm_suite.py --experiment="high-order-variable-length" -d
expResults = {}
for length in lengths:
experiment = os.path.join(tmResults,
"sequence_length"+"{:.1f}".format(length),
"0.log")
print "Load Experiment", experiment
data = readExperiment(experiment)
(accuracy, numIteration, numSequences) = computeAccuracyEnding(
data['predictions'],
data['truths'],
data['iterations'],
resets=data['resets'],
randoms=data['randoms'],
sequenceCounter=data['sequenceCounter'])
(accuracyAll, numIterationAll, numSequencesAll) = computeAccuracy(
data['predictions'],
data['truths'],
data['iterations'],
resets=data['resets'],
