def loadExperiment(self, experiment):
suite = Suite()
suite.parse_opt()
suite.parse_cfg()
experiment_dir = experiment.split('/')[1]
params = suite.items_to_params(suite.cfgparser.items(experiment_dir))
self.params = params
predictions = suite.get_history(experiment, 0, 'predictions')
truth = suite.get_history(experiment, 0, 'truth')
self.iteration = suite.get_history(experiment, 0, 'iteration')
self.train = suite.get_history(experiment, 0, 'train')
self.truth = np.array(truth, dtype=np.float)
if params['output_encoding'] == 'likelihood':
from nupic.encoders.scalar import ScalarEncoder as NupicScalarEncoder
self.outputEncoder = NupicScalarEncoder(w=1,
minval=0,
maxval=40000,
n=22,
forced=True)
predictions_np = np.zeros((len(predictions), self.outputEncoder.n))
for i in xrange(len(predictions)):
if predictions[i] is not None:
predictions_np[i, :] = np.array(predictions[i])
self.predictions = predictions_np
else:
self.predictions = np.array(predictions, dtype=np.float)
def collect_results(experiment):
suite = PyExperimentSuite()
params = suite.get_params(experiment)
reps = params['repetitions']
datasets = []
for rep in range(reps):
results = suite.get_history(experiment, rep, 'all')
if len(results) == 0:
raise MissingDataException
datasets.append(results)
return datasets
def collect_results(experiment):
suite = PyExperimentSuite()
params = suite.get_params(experiment)
reps = params['repetitions']
datasets = []
for rep in range(reps):
results = suite.get_history(experiment, rep, 'all')
if len(results) == 0:
raise MissingDataException
datasets.append(results)
return datasets
suite = PyExperimentSuite()
args = parser.parse_args()
from pylab import rcParams
rcParams.update({'figure.autolayout': True})
rcParams.update({'figure.facecolor': 'white'})
rcParams.update({'ytick.labelsize': 8})
rcParams.update({'figure.figsize': (12, 6)})
rcParams.update({'pdf.fonttype': 42})
experiments = args.experiments
for i, experiment in enumerate(experiments):
iteration = suite.get_history(experiment, 0, 'iteration')
predictions = suite.get_history(experiment, 0, 'predictions')
truth = suite.get_history(experiment, 0, 'truth')
train = suite.get_history(experiment, 0, 'train')
resets = None if args.full else suite.get_history(
experiment, 0, 'reset')
randoms = None if args.full else suite.get_history(
experiment, 0, 'random')
type = "elements" if args.full else "sequences"
hideTraining = args.training_hide is not None and len(
args.training_hide) > i and args.training_hide[i] > 0
lineSize = args.size_of_line[
i] if args.size_of_line is not None and len(
args.size_of_line) > i else 0.8
suite = PyExperimentSuite()
args = parser.parse_args()
from pylab import rcParams
rcParams.update({'figure.autolayout': True})
rcParams.update({'figure.facecolor': 'white'})
rcParams.update({'ytick.labelsize': 8})
rcParams.update({'figure.figsize': (12, 6)})
rcParams.update({'pdf.fonttype': 42})
experiments = args.experiments
for i, experiment in enumerate(experiments):
iteration = suite.get_history(experiment, 0, 'iteration')
predictions = suite.get_history(experiment, 0, 'predictions')
truth = suite.get_history(experiment, 0, 'truth')
train = suite.get_history(experiment, 0, 'train')
resets = None if args.full else suite.get_history(experiment, 0, 'reset')
randoms = None if args.full else suite.get_history(experiment, 0, 'random')
type = "elements" if args.full else "sequences"
hideTraining = args.training_hide is not None and len(args.training_hide) > i and args.training_hide[i] > 0
lineSize = args.size_of_line[i] if args.size_of_line is not None and len(args.size_of_line) > i else 0.8
label = args.graph_labels[i] if args.graph_labels is not None and len(args.graph_labels) > i else experiment
plotAccuracy(computeAccuracy(predictions, truth, iteration, resets=resets, randoms=randoms, num=args.num),
train,
window=args.window,
