class Classifier:
def __init__(self):
if os.path.getsize("bd_path.txt") > 0:
with open("bd_path.txt", 'rb') as f:
self._bd_path = f.readline()
f.close()
self._genre_features = np.load('genres_features.npy')
self._knn = KNN(self._bd_path, self._genre_features)
self._bayes = Bayes(self._bd_path, self._genre_features)
self._genres = np.load('genres.npy')
def change_db(self, path):
self._bd_path = path
with open("bd_path.txt", 'w') as f:
f.write(path)
f.close()
# self._genres, self._genre_features = extract_funtions.extrakt_features_for_genres(self._bd_path)
# np.save('genres.npy', self._genres)
# np.save("genres_features.npy", self._genre_features)
self._knn = KNN(self._bd_path, self._genre_features)
self._bayes = Bayes(self._bd_path, self._genre_features)
#self._knn.train_model()
self._bayes.train_model()
def interpret_approx_array(self, track_path, model: Model):
genres = []
probabilities = []
probs = model.approx_genres(track_path)
probssort = np.argsort(probs)
probssort = np.flip(probssort, 0)
for i in probssort:
genres.append(self._genres[i])
probabilities.append(round(probs[i] * 100, 2))
return genres, probabilities # od największych do najmniejszych
def approximate(self, track_path, mode) -> (list, list):
if mode == "knn":
return self.interpret_approx_array(track_path, self._knn)
if mode == "bayes":
return self.interpret_approx_array(track_path, self._bayes)
return list(), list()
def get_db_name(self):
return self._bd_path
