def svm_local(isDecision=False, n_predict=0, n_neighbors=5000, isSgd=True):
start = time.time()
db_manager = DbManager()
db_manager.connect('../db/annotated.db')
print("Start converting train points")
train_points_tmp = train_points.tolist()
train_classes_tmp = train_classes.tolist()
print("Conversion finished in time {0}s".format(time.time()-start))
predict_classes = []
for point_id, point_vector in enumerate(test_points):
if point_id >= 100:
break
if point_id % 10 == 0:
print("Start query test point {0}".format(point_id))
neighbors = [int(x) for x in db_manager.query_knn('../db/annotated.db', point_id, n_neighbors=n_neighbors)[0].split()]
neighbor_points = [train_points_tmp[neighbor_id] for neighbor_id in neighbors]
neighbor_classes = [train_classes_tmp[neighbor_id] for neighbor_id in neighbors]
svmManager = SvmManager(None, verbose=verbose, isSgd=isSgd)
svmManager.fit(np.array(neighbor_points), np.array(neighbor_classes), store=False)
if not isDecision:
predict_classes.append(svmManager.predict([point_vector])[0]) #only one class result
else:
predict_classes.append(svmManager.decision_function([point_vector], n_predict)[0] )
end = time.time()
logging.info("testing local svm finished in {0}s with {1}".format(end - start, model_file))
return predict_classes
def svm_fit_to_file(proba=False):
start = time.time()
with open(model_file,"wb") as handle:
svmManager = SvmManager(handle, verbose=verbose, isSgd=True, proba=proba)
svmManager.fit(train_points, train_classes)
end = time.time()
logging.info("training fit to file finished in {0}s with {1} ".format(end - start, model_file))
def svm_predict_from_file(proba=False, n_predict=5, isDecision=False):
start = time.time()
with open(model_file,"rb") as handle:
svmManager = SvmManager(handle, verbose=verbose, isSgd=True, proba=proba)
svmManager.load()
if not proba:
result = svmManager.predict(test_points)
elif not isDecision:
result = svmManager.predict_proba(test_points, n_predict)
else:
result = svmManager.decision_function(test_points, n_predict)
end = time.time()
logging.info("testing predict from file finished in {0}s with {1}".format(end - start, model_file))
print("[RESULT LENGTH]", len(result))
return result
