def run():
# train_files, train_labels = read_data_file('./train_test_data_combined.txt')
data_set, data_labels = load_test_set('./IRMAS-TrainingData/')
train_files, test_files, train_labels, test_labels = train_test_split(data_set, data_labels, test_size=0.25)
train_set, train_labels, test_set, test_labels = fit_train_test(train_files, train_labels, test_files,
test_labels)
print("test_labels length: ", len(test_labels))
print("test_files length: ", len(test_files))
print()
print("train_labels length: ", len(train_labels))
print("train_files length: ", len(train_files))
print()
train_classes, encoded_classes = label_encoder(train_labels)
test_classes = label_encoder_for_test(encoded_classes, test_labels)
train_set = get_feature_vector_2(train_files)
test_set = get_feature_vector_2(test_files)
# linear_regression(train_set, train_classes, test_set, test_classes, encoded_classes)
knn(train_set, train_classes, test_set, test_classes, encoded_classes)
svm(train_set, train_classes, test_set, test_classes, encoded_classes)
random_forest(train_set, train_classes, test_set, test_classes, encoded_classes)
def run():
# train_files, train_labels = read_data_file('./train_test_data_combined.txt')
train_files, train_labels = load_train_set('./IRMAS-TrainingData/',
single_instrument=True)
train_set = get_feature_vector(train_files)
song_name = "(02) dont kill the whale-1"
test_files, test_labels = cut_a_single_song(
f'./IRMAS-TestingData/Part1/{song_name}')
print("test_labels length: ", len(test_labels))
print("test_files length: ", len(test_files))
print()
print("train_labels length: ", len(train_labels))
print("train_files length: ", len(train_files))
#
train_classes, encoded_classes = label_encoder(train_labels)
test_classes = label_encoder_for_test(encoded_classes, test_labels)
test_set = get_feature_vector_2(test_files)
knn(train_set, train_classes, test_set, test_classes, encoded_classes)
svm(train_set, train_classes, test_set, test_classes, encoded_classes)
random_forest(train_set, train_classes, test_set, test_classes,
encoded_classes)
def run():
# train_files, train_labels = load_train_set('./data-set/')
train_files, train_labels = read_data_file()
test_files, test_labels = load_test_set('./IRMAS-TrainingData/')
train_files, train_labels, test_files, test_labels = fit_train_test(
train_files, train_labels, test_files, test_labels)
print("test_labels length: ", len(test_labels))
print("test_files length: ", len(test_files))
print()
print("train_labels length: ", len(train_labels))
print("train_files length: ", len(train_files))
train_classes, encoded_classes = label_encoder(train_labels)
test_classes = label_encoder_for_test(encoded_classes, test_labels)
# train_set = get_feature_vector(train_files)
train_set = train_files
test_set = get_feature_vector(test_files)
# read_write_data.create_data_file(train_set, train_labels)
knn(train_set, train_classes, test_set, test_classes, encoded_classes)
svm(train_set, train_classes, test_set, test_classes, encoded_classes)
random_forest(train_set, train_classes, test_set, test_classes,
encoded_classes)
adaboost(train_set, train_classes, test_set, test_classes, encoded_classes)
def run():
data_set, data_labels = read_data_file('./train_test_data_combined.txt')
train_files, test_files, train_labels, test_labels = train_test_split(data_set, data_labels, test_size=0.25)
train_set, train_labels, test_set, test_labels = fit_train_test(train_files, train_labels, test_files,
test_labels)
train_classes, encoded_classes = label_encoder(train_labels)
test_classes = label_encoder_for_test(encoded_classes, test_labels)
knn(train_set, train_classes, test_set, test_classes, encoded_classes)
svm(train_set, train_classes, test_set, test_classes, encoded_classes)
random_forest(train_set, train_classes, test_set, test_classes, encoded_classes)
def run():
data_set, data_labels = load_by_genre(path='./IRMAS-TrainingData/')
X = get_feature_vector_2(data_set)
train_set, test_set, train_labels, test_labels = train_test_split(
X, data_labels, test_size=0.25)
train_set, train_labels, test_set, test_labels = fit_train_test(
train_set, train_labels, test_set, test_labels)
train_classes, encoded_classes = label_encoder(train_labels)
test_classes = label_encoder_for_test(encoded_classes, test_labels)
knn(train_set, train_classes, test_set, test_classes, encoded_classes)
svm(train_set, train_classes, test_set, test_classes, encoded_classes)
random_forest(train_set, train_classes, test_set, test_classes,
encoded_classes)
adaboost(train_set, train_classes, test_set, test_classes, encoded_classes)
def run():
data_set, data_labels = load_train_set('./IRMAS-TrainingData/')
data_set, data_labels = load_test_set()
train_files, test_files, train_labels, test_labels = train_test_split(
data_set, data_labels, test_size=0.25)
train_files, train_labels, test_files, test_labels = fit_train_test(
train_files, train_labels, test_files, test_labels)
train_classes, encoded_classes = label_encoder(train_labels)
test_classes = label_encoder_for_test(encoded_classes, test_labels)
train_set = get_feature_vector(train_files)
test_set = get_feature_vector(test_files)
knn(train_set, train_classes, test_set, test_classes, encoded_classes)
svm(train_set, train_classes, test_set, test_classes, encoded_classes)
random_forest(train_set, train_classes, test_set, test_classes,
encoded_classes)
adaboost(train_set, train_classes, test_set, test_classes, encoded_classes)
def run():
# data_set, data_labels = load_train_set('./data-set/')
data_set, data_labels = read_data_file()
train_files, test_files, train_labels, test_labels = train_test_split(
data_set, data_labels, test_size=0.25)
train_files, train_labels, test_files, test_labels = fit_train_test(
train_files, train_labels, test_files, test_labels)
train_classes, encoded_classes = label_encoder(train_labels)
test_classes = label_encoder_for_test(encoded_classes, test_labels)
# train_set = get_feature_vector(train_files)
train_set = train_files
test_set = test_files
# test_set = get_feature_vector(test_files)
knn(train_set, train_classes, test_set, test_classes, encoded_classes)
svm(train_set, train_classes, test_set, test_classes, encoded_classes)
random_forest(train_set, train_classes, test_set, test_classes,
encoded_classes)
adaboost(train_set, train_classes, test_set, test_classes, encoded_classes)