def apply_preprocess(aggregated, devices, method, threshold=np.float32(0.0)):
"""
Apply the given preprocessing method to the devices.
"""
if method == 'raw':
return
indicators = [d.indicators(threshold) for d in devices]
if method == 'constant':
(energies, _) = solve_constant_energy(aggregated, indicators)
for (e, d) in zip(energies, devices):
log.info('Setting constant energy %s for device %s.' % (e, d.name))
d.powers = e * d.indicators(np.float32(10))
elif method == 'interval':
energy_dict = confidence_estimator(aggregated, devices, sort_data,
threshold)
for d in devices:
log.info('Setting constant energy %s for device %s.' %
(energy_dict[d.name], d.name))
d.powers = energy_dict[d.name] * d.indicators(np.float32(10))
elif method == 'edge':
energy_dict = confidence_estimator(aggregated, devices,
get_changed_data, threshold)
for d in devices:
log.info('Setting constant energy %s for device %s.' %
(energy_dict[d.name], d.name))
d.powers = energy_dict[d.name] * d.indicators(np.float32(10))
elif method == 'markov':
agg = dict((time, power) for (time, power) in enumerate(aggregated))
device = [i for i in xrange(len(devices))]
active = {}
for i, d in enumerate(devices):
active[(i,0)] = 0
for t, ind in enumerate(d.indicators(threshold)):
active[(i,t+1)] = ind * 1
for d in device:
print devices[d].name
try:
p = fit_data(agg, d, active, 3, device)
for (t, power) in p.iteritems():
devices[d].powers[t-1] = power
except KeyError:
devices[d].powers = np.zeros(len(devices[d].powers))
def test_get_changed_data(self):
"""
Test the estimator by looking for immediate power changes from a single
device changing state.
"""
estimate = confidence_estimator(self.aggregate.powers, self.devices,
get_changed_data)
estimate_test = {'a': np.float(0.0), 'b': np.float(2.5)}
self.assertEqual(estimate, estimate_test)
def test_sort_data(self):
"""Test the estimator by finding intervals of a single active device."""
estimate = confidence_estimator(self.aggregate.powers, self.devices,
sort_data)
estimate_test = {'a': np.float32(4.5), 'b': np.float32(-2.5)}
self.assertEqual(estimate, estimate_test)
