def svm_classification(genres, features_type):
training_set_features = tf.read_features_from_files("../../music/training", genres, features_type)
testing_set_features = tf.read_features_from_files("../../music/testing", genres, features_type)
X = []
y = []
for feature in training_set_features:
(mean, cov_mat, genre_name) = feature
X.append(mean.tolist())
y.append(tf.get_genre_ID(genre_name))
training_data = np.array(X)
training_class = np.array(y)
X = []
y = []
for feature in testing_set_features:
(mean, cov_mat, genre_name) = feature
X.append(mean.tolist())
y.append(tf.get_genre_ID(genre_name))
testing_data = np.array(X)
testing_class = np.array(y)
clf = OneVsOneClassifier(SVC(kernel='linear'))
result_class = np.array(clf.fit(training_data, training_class).predict(testing_data))
rt.print_accuracy(list(testing_class), list(result_class), genres, features_type, "svm")
rt.write_accuracy_to_file("../../music/", list(testing_class), list(result_class), genres, features_type, "svm")
def knn_classification(genres, features_type='mfcc', weight='distance', n_neighbors_min=3, n_neighbors_max=5):
training_array = []
training_classes = []
training_set_features = tf.read_features_from_files("../../music/training", genres, features_type)
n_features = len(training_set_features[0][0])
# convert each cortege to array
for track_features in training_set_features:
training_array[len(training_array):] = [tf.cortege_to_list(track_features)]
training_classes[len(training_classes):] = [tf.get_genre_ID(str(track_features[2]))]
testing_set_features = tf.read_features_from_files("../../music/testing", genres, features_type)
testing_array = []
expected_genres = []
# convert each cortege to array
for track_features in testing_set_features:
testing_array[len(testing_array):] = [tf.cortege_to_list(track_features)]
expected_genres[len(expected_genres):] = [tf.get_genre_ID(str(track_features[2]))]
for n_neighbors in range(n_neighbors_min, n_neighbors_max+1):
knn_classifier = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors, weights=weight, algorithm='ball_tree',
metric=kl.calculate_KL_divergence, metric_params={"n_features": n_features})
knn_classifier.fit(training_array,training_classes)
result_of_classification = knn_classifier.predict(testing_array)
result = []
params_string = "weight: " + str(weight) + " n_neighbors: " + str(n_neighbors)
rt.print_accuracy(expected_genres, result_of_classification, genres, features_type, "knn", params_string)
rt.write_accuracy_to_file("../../music/", expected_genres, result_of_classification, genres, features_type, "knn", params_string)
def knn_classification(genres,
features_type='mfcc',
weight='distance',
n_neighbors_min=3,
n_neighbors_max=5):
training_array = []
training_classes = []
training_set_features = tf.read_features_from_files(
"../../music/training", genres, features_type)
n_features = len(training_set_features[0][0])
# convert each cortege to array
for track_features in training_set_features:
training_array[len(training_array):] = [
tf.cortege_to_list(track_features)
]
training_classes[len(training_classes):] = [
tf.get_genre_ID(str(track_features[2]))
]
testing_set_features = tf.read_features_from_files("../../music/testing",
genres, features_type)
testing_array = []
expected_genres = []
# convert each cortege to array
for track_features in testing_set_features:
testing_array[len(testing_array):] = [
tf.cortege_to_list(track_features)
]
expected_genres[len(expected_genres):] = [
tf.get_genre_ID(str(track_features[2]))
]
for n_neighbors in range(n_neighbors_min, n_neighbors_max + 1):
knn_classifier = neighbors.KNeighborsClassifier(
n_neighbors=n_neighbors,
weights=weight,
algorithm='ball_tree',
metric=kl.calculate_KL_divergence,
metric_params={"n_features": n_features})
knn_classifier.fit(training_array, training_classes)
result_of_classification = knn_classifier.predict(testing_array)
result = []
params_string = "weight: " + str(weight) + " n_neighbors: " + str(
n_neighbors)
rt.print_accuracy(expected_genres, result_of_classification, genres,
features_type, "knn", params_string)
rt.write_accuracy_to_file("../../music/", expected_genres,
result_of_classification, genres,
features_type, "knn", params_string)
def get_accuracy(expected_class, actual_class, genres, features_type,
*extra_strings):
genres_correct = [0 for i in range(len(genres))]
genres_total = [
expected_class.count(tf.get_genre_ID(genres[i]))
for i in range(len(genres))
]
genres_correct_total = 0
for i in range(len(expected_class)):
if (expected_class[i] == actual_class[i]):
pos = genres.index(tf.get_genre_name(expected_class[i]))
genres_correct[pos] += 1
genres_correct_total += 1
extra_string = ""
for string in extra_strings:
if len(string) != 0:
extra_string += str(string) + "\n"
result = ">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> \n" + \
str(features_type) + "\n" + \
str(genres) + "\n" + \
str(extra_string) + \
"Accuracy: \n"
for i in range(len(genres)):
result += str(genres[i]) + ": " + str(genres_correct[i]) + " " + str(
float(genres_correct[i]) / float(genres_total[i])) + "\n"
result += "average: " + str(float(genres_correct_total) / float(len(actual_class))) + "\n" + \
"<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< \n"
return result
