Hey all,
I’m running into some trouble with using HTM for a gesture recognition
application and would appreciate some help.
First, the data is collected from 17 users performing 5 gestures of each of 16
different gesture classes using motion sensors. The feature vector for each
sample is a sequence of discretized directions calculated using bezier control
points after curve fitting the gesture trace.
For a baseline, I fed the data to 16 10-state HMMs for training and again for
testing. The classification accuracy achieved is 95.7%.
For HTM, I created 16 CLA models using parameters from a medium swarm. I ran
the data through the models for training where each model is trained on only 1
gesture class. For testing, I fed the same data again with learning turned off
and recorded the anomaly score (averaged across each sequence) for each model.
Classification was done by seeking the model with the minimum anomaly score.
Accuracy turned out to be a puzzling 0.0%!!
Below is the relevant section of the code. I would appreciate any hints.
Thanks,
Nick
def run_experiment():
print "Running experiment..."
model = [0]*16
for i in range(0, 16):
model[i] = ModelFactory.create(model_params, logLevel=0)
model[i].enableInference({"predictedField": FIELD_NAME})
with open(FILE_PATH, "rb") as f:
csv_reader = csv.reader(f)
data = []
labels = []
for row in csv_reader:
r = [int(item) for item in row[:-1]]
data.append(r)
labels.append(int(row[-1]))
# data_train, data_test, labels_train, labels_test =
cross_validation.train_test_split(data, labels, test_size=0.4, random_state=0)
data_train = data
data_test = data
labels_train = labels
labels_test = labels
for passes in range(0, TRAINING_PASSES):
sample = 0
for (ind, row) in enumerate(data_train):
for r in row:
value = int(r)
result = model[labels_train[ind]].run({FIELD_NAME: value,
'_learning': True})
prediction = result.inferences["multiStepBestPredictions"][1]
anomalyScore = result.inferences["anomalyScore"]
model[labels[ind]].resetSequenceStates()
sample += 1
print "Processing training sample %i" % sample
if sample == 100:
break
sample = 0
labels_predicted = []
for row in data_test:
anomaly = [0]*16
for i in range(0, 16):
model[i].resetSequenceStates()
for r in row:
value = int(r)
result = model[i].run({FIELD_NAME: value, '_learning': False})
prediction = result.inferences["multiStepBestPredictions"][1]
anomalyScore = result.inferences["anomalyScore"]
# print value, prediction, anomalyScore
if value == int(prediction) and anomalyScore == 0:
# print "No prediction made"
anomalyScore = 1
anomaly[i] += anomalyScore
anomaly[i] /= len(row)
sample += 1
print "Processing testing sample %i" % sample
labels_predicted.append(np.min(np.array(anomaly)))
print anomaly, row[-1]
if sample == 100:
break
accuracy = np.sum(np.array(labels_predicted) ==
np.array(labels_test))*100.0/len(labels_test)
print "Testing accuracy is %0.2f" % accuracy
