-1.,
0.,
])
params = py_indoor_context.ManhattanHyperParameters(w, 4., 6.)
w2 = np.array([
2.,
-.5,
.1,
])
params2 = py_indoor_context.ManhattanHyperParameters(w2, 3., 7.)
train_ids = [20, 40, 60]
test_ids = [65, 70]
mgr = py_indoor_context.TrainingManager()
mgr.LoadSequence("lab_kitchen1", train_ids + test_ids)
fm = py_indoor_context.FeatureManager('/tmp')
for i in range(mgr.NumInstances()):
fm.ComputeMockFeatures(mgr.GetInstance(i))
fm.CommitFeatures()
train_instances = [mgr.GetInstance(i) for i in range(len(train_ids))]
test_instances = [
mgr.GetInstance(i) for i in range(len(train_ids),
len(train_ids) + len(test_ids))
]
r = training_helpers.Reporter(train_instances, test_instances, fm,
import py_indoor_context as ic
mgr = ic.TrainingManager()
mgr.LoadSequence('lab_ground1', [32])