def train_beats(**kwargs):
# ============================================================= #
# ======================== TRAINING BEATS ===================== #
# ============================================================= #
extraction = kwargs.get('extraction', 'pixel')
max_normalize = kwargs.get('max_normalize', 255)
learn_rate = kwargs.get('learning_rate', 0.05)
n_epochs = kwargs.get('max_epoch', 100)
n_codebooks = kwargs.get('n_codebooks', 3)
print("learning rate: " + str(learn_rate))
print("epoch: " + str(n_epochs))
print("class: " + str(n_codebooks))
print()
train_beats = LVQ()
train_beats.set_n_codebooks(n_codebooks)
# load and prepare data train
filename = 'train_beats.csv'
train_beats.load_csv(filename, 'train')
if extraction == 'pixel':
for i in range(len(train_beats.data_train[0]) - 1):
train_beats.min_max_normalize(train_beats.data_train, i, 0, 255)
else:
for i in range(len(train_beats.data_train[0]) - 1):
train_beats.min_max_normalize(train_beats.data_train, i, 0, 50)
# Training process
start_time = time.time()
train_beats.train_codebooks(learn_rate, n_epochs)
duration = time.time() - start_time
print("\nclass codebooks: ", end="")
print([row[-1] for row in train_beats.codebooks])
score, wrong_data, actual, predictions = train_beats.accuracy_metric(
'train')
print("===============train beats==============")
print("Waktu proses pembelajaran: %s detik ---" % (duration))
print("score: " + str(round(score, 3)) + "%\n")
print("wrong data: ", end="")
print(wrong_data)
train_beats.export_codebooks("beats_codebooks")
beats, beats_test, dataset_path = create_dataset.group_data_beats()
# Show wrong data image
# helper.show_wrong_data(wrong_data, predictions, beats, dataset_path)
# exit()
return score, duration
n_codebooks = 9
print("learning rate: " + str(learn_rate))
print("epoch: " + str(n_epochs))
print("class: " + str(n_codebooks))
print()
train_beats = LVQ()
train_beats.set_n_codebooks(n_codebooks)
# load and prepare data train
filename = 'train_three.csv'
train_beats.load_csv(filename, 'train')
for i in range(len(train_beats.data_train[0]) - 1):
if i != 5:
train_beats.min_max_normalize(train_beats.data_train, i, 0, 50)
else:
train_beats.min_max_normalize(train_beats.data_train, i, 0, 30)
# load and prepare data test
filename = 'test_three.csv'
train_beats.load_csv(filename, 'test')
for i in range(len(train_beats.data_test[0]) - 1):
if i != 5:
train_beats.min_max_normalize(train_beats.data_test, i, 0, 50)
else:
train_beats.min_max_normalize(train_beats.data_test, i, 0, 30)
train_beats.train_codebooks(learn_rate, n_epochs)
print("class codebooks: ", end="")
n_codebooks = 9
print("learning rate: " + str(learn_rate))
print("epoch: " + str(n_epochs))
print("class: " + str(n_codebooks))
print()
train_whole = LVQ()
train_whole.set_n_codebooks(n_codebooks)
# load and prepare data train
filename = 'train_whole.csv'
train_whole.load_csv(filename, 'train')
for i in range(len(train_whole.data_train[0]) - 1):
if i != 5: # difference normalization for average value
train_whole.min_max_normalize(train_whole.data_train, i, 0, 50)
else:
train_whole.min_max_normalize(train_whole.data_train, i, 0, 30)
# load and prepare data test
# filename = 'test_whole.csv'
# train_whole.load_csv(filename, 'test')
# for i in range(len(train_whole.data_test[0])-1):
# if i != 5:
# train_whole.min_max_normalize(train_whole.data_test, i, 0, 50)
# else:
# train_whole.min_max_normalize(train_whole.data_test, i, 0, 30)
train_whole.train_codebooks(learn_rate, n_epochs)
print("class codebooks: ", end="")
def train_pitch(**kwargs):
# ============================================================= #
# ======================== TRAINING PITCH ===================== #
# ============================================================= #
identifier = kwargs.get('identifier', 'quarter')
extraction = kwargs.get('extraction', 'paranada')
max_normalize = kwargs.get('max_normalize', 255)
learn_rate = kwargs.get('learning_rate', 0.05)
n_epochs = kwargs.get('max_epoch', 100)
n_codebooks = kwargs.get('n_codebooks', 9)
show_wrong_data = kwargs.get('show_wrong_data', False)
print("learning rate: " + str(learn_rate))
print("epoch: " + str(n_epochs))
print("class: " + str(n_codebooks))
print()
train_pitch = LVQ()
train_pitch.set_n_codebooks(n_codebooks)
# load and prepare data train
filename = 'train_' + identifier + '.csv'
train_pitch.load_csv(filename, 'train')
if extraction == 'paranada':
for i in range(len(train_pitch.data_train[0]) - 1):
if i != 5: # difference normalization for average value
train_pitch.min_max_normalize(train_pitch.data_train, i, 0, 50)
else:
train_pitch.min_max_normalize(train_pitch.data_train, i, 0, 30)
elif extraction == 'pixel':
for i in range(len(train_pitch.data_train[0]) - 1):
train_pitch.min_max_normalize(train_pitch.data_train, i, 0, 255)
else:
for i in range(len(train_pitch.data_train[0]) - 1):
train_pitch.min_max_normalize(train_pitch.data_train, i, 0, 30)
# load and prepare data test
# filename = 'test_whole.csv'
# train_pitch.load_csv(filename, 'test')
# for i in range(len(train_pitch.data_test[0])-1):
# if i != 5:
# train_pitch.min_max_normalize(train_pitch.data_test, i, 0, 50)
# else:
# train_pitch.min_max_normalize(train_pitch.data_test, i, 0, 30)
# Training process
start_time = time.time()
train_pitch.train_codebooks(learn_rate, n_epochs)
duration = time.time() - start_time
print("class codebooks: ", end="")
print([row[-1] for row in train_pitch.codebooks])
score, wrong_data, actual, predictions = train_pitch.accuracy_metric(
'train')
# score_test, wrong_data_test, actual_test, predictions_test = train_pitch.accuracy_metric('test')
print("===============train " + identifier + "==============")
print("score: " + str(round(score, 3)) + "%")
print("\n")
print("wrong data: ", end="")
print(wrong_data)
# print("\n===============test===============")
# print("score test: " + str(round(score_test, 3)) + "%")
# print("\n")
# print("wrong data test: ", end="")
# print(wrong_data_test)
train_pitch.export_codebooks(identifier + "_codebooks")
pitch, pitch_test, dataset_path = helper.get_dataset_info(
identifier, "train")
# print(pitch)
# print()
# print(pitch_test)
# print()
# print(dataset_path)
# exit()
# Show wrong data train image
if show_wrong_data:
helper.show_wrong_data(wrong_data, predictions, pitch, dataset_path)
# exit()
# Show wrong data test image
# helper.show_wrong_data(wrong_data_test, predictions_test, whole_test, dataset_path)
# exit()
return score, duration
create_dataset.create_csv_test(max_num_class=0, length_area=7)
learning_rate = 0.05
max_epoh = 4000
n_codebooks = 9
test_pitch = LVQ()
test_pitch.set_n_codebooks(n_codebooks)
# load and prepare data test
filename = 'all_codebooks.csv'
test_pitch.import_codebooks(filename)
test_pitch.load_csv("test_paranada.csv", "test")
for i in range(len(test_pitch.data_test[0]) - 1):
test_pitch.min_max_normalize(test_pitch.data_test, i, 0, 50)
score_pitch, wrong_data_pitch, actual_pitch, predictions_pitch = test_pitch.accuracy_metric(
'test')
# =========
# Statistic
# =========
pitch_info = open("test_paranada_info.csv", 'r')
pitch_data = pitch_info.read()
pitch_data = pitch_data.split("\n")
pitch_data.pop()
pitch_info.close()
file_info = open("test_histogram_info.csv", 'r')