def do_windowing(window_size, begin, end):
gestures = PrepareDataSet.getlines()
lines = gestures.readlines()
count = 1
line = ''
return_xy = {}
for index_line in range(begin, end):
if count <= window_size:
if count == 1:
fields = lines[index_line].split(",")
if (end - index_line) >= window_size:
y.append(str(fields[32:]).replace("\\n", "").replace("\\r", "").replace("'", ""))
if count > 1:
line += "," + remove_labels_from_line(lines[index_line])
else:
line += remove_labels_from_line(lines[index_line])
count += 1
else:
count = 1
X.append(line.split(","))
line = ''
return_xy["X"] = copy.deepcopy(X)
return_xy["y"] = copy.deepcopy(y)
X[:] = []
y[:] = []
print "tamanho de x "+str(len(return_xy["X"]))
print "tamanho de y "+str(len(return_xy["y"]))
return return_xy
def execute():
gestures = PrepareDataSet.getlines()
lines = gestures.readlines()
instances_to_be_tested = []
for index_line in range(start_test_line_number, number_of_lines):
line = lines[index_line]
instances_to_be_tested.append(line)
prepared_instances_to_be_tested = prepare_instances_to_be_tested(instances_to_be_tested, False)
results = run_knn(prepared_instances_to_be_tested, False)
print "Percentual de acertos "\
+ str(get_percentage_corrects_predictions(results,
get_real_phase_of_gestures(instances_to_be_tested)))+"%"
from tools import PrepareDataSet
weight = 'distance'
n_neighbors = 7
percent_instances_to_trains_ = 80
window_size = 8
X = []
y = []
number_of_lines = PrepareDataSet.get_total_of_lines()
start_test_line_number = ((number_of_lines * percent_instances_to_trains_)/100)
print "teste começa nessa linha = " + str(start_test_line_number)
print "número total de linhas no arquivo = " + str(number_of_lines)
# lê todo o arquivo de dados do dataset setado no arquivo load_dataset no caso o a1_va3.csv
gestures = PrepareDataSet.getlines()
# pegar % do arquivo não janelado para treino
# refazer isso aqui está indo todo mundo pra o treinamento
def get_instances_to_train():
list_train = []
lines = gestures.readlines()
for index in range(1, start_test_line_number-1):
list_train.append(lines[index])
print "quantidade de linhas na memoria " + str(len(list_train))
return list_train
instances_to_train = get_instances_to_train()
