def extract_rtp_known_state(self, path_list):
healthy_x = []
healthy_y = []
healthy_bs = []
healthy_new_stroke = []
disease_x = []
disease_y = []
disease_bs = []
disease_new_stroke = []
states = []
for path in path_list:
# Acuqisisco l'id del paziente
id = HandManager.get_id_from_path(path)
# Acquisisco lo stato del paziente
state = HandManager.get_state_from_id(id)
# Leggo i punti campionati nel file
partial_x, partial_y, partial_bs = RTPExtraction.__read_samples_from_file(
path)
# Trasformo i punti in segmenti RHS.
partial_x, partial_y, partial_bs = self.__transform_point_in_rtp(
partial_x, partial_y, partial_bs)
partial_new_stroke = RTPExtraction.__get_new_stroke(partial_bs)
# Raddoppio il numero dei campioni RHS così da ampliare il dataset.
partial_x, partial_y, partial_bs, partial_new_stroke = self.__extract_subs_from_samples(
partial_x, partial_y, partial_bs, partial_new_stroke)
# Suddivido i capioni in base allo stato di salute del paziente a cui appartengono, dopo di che genero degli
# array mono dimensionali di lunghezza pari alla lunghezza dei campioni richiesta dal Modello.
if state == HEALTHY_STATE:
self.__create_sample_sequence(healthy_x, healthy_y, healthy_bs,
healthy_new_stroke, partial_x,
partial_y, partial_bs,
partial_new_stroke)
else:
self.__create_sample_sequence(disease_x, disease_y, disease_bs,
disease_new_stroke, partial_x,
partial_y, partial_bs,
partial_new_stroke)
# Dopo aver ultimato l'estrazione genero due tensori tridimensionali, uno per i pazienti sani e uno per i pazienti malati
healthy_tensor = np.reshape(
np.array(healthy_x + healthy_y + healthy_bs + healthy_new_stroke),
(len(healthy_x), self.__num_samples, FEATURES))
disease_tensor = np.reshape(
np.array(disease_x + disease_y + disease_bs + disease_new_stroke),
(len(disease_x), self.__num_samples, FEATURES))
# A questo punto per ottenere un dataset bilanciato in ogni situazione valuto quale dei due tensori possiede meno.
healthy_tensor, disease_tensor = HandManager.balance_dataset(
healthy_tensor, disease_tensor)
# Il tensore con meno campioni verrà utilizzato per generare il tensore finale, il quale verrà composto inserendo
# prima tutti gli utenti sani e poi tutti gli utenti sani, si è già provato un approccio alternato, ma ha dato
# scarsi risultati.
final_tensor = np.concatenate((healthy_tensor, disease_tensor))
# Genero infine il vettore gli stati
states += [HEALTHY_STATE for _ in range(len(healthy_tensor))
] + [DISEASE_STATE for _ in range(len(disease_tensor))]
return np.array(final_tensor), np.array(states), len(final_tensor)
def extract_rhs_file(self, path):
x_samples = []
y_samples = []
bs_samples = []
id = HandManager.get_id_from_path(path)
state = HandManager.get_state_from_id(id)
partial_x, partial_y, partial_bs = RHSDistanceExtract.read_samples_from_file(path)
partial_x, partial_y, partial_bs = self.__transform_point_in_rhs(partial_x, partial_y, partial_bs)
partial_x, partial_y, partial_bs = self.__extract_subs_from_samples(partial_x, partial_y, partial_bs)
self.__create_sample_sequence(x_samples, y_samples, bs_samples, partial_x, partial_y, partial_bs)
tensor = np.reshape((x_samples + y_samples + bs_samples), (len(x_samples), self.__num_samples, FEATURES))
states = [state for _ in range(len(x_samples))]
return np.array(tensor), np.array(states)
def rtp(self, path):
x_samples = []
y_samples = []
bs_samples = []
new_stroke_samples = []
id = HandManager.get_id_from_path(path)
state = HandManager.get_state_from_id(id)
partial_x, partial_y, partial_bs = RTPExtraction.__read_samples_from_file(
path)
partial_x, partial_y, partial_bs = self.__transform_point_in_rtp(
partial_x, partial_y, partial_bs)
partial_new_stroke = RTPExtraction.__get_new_stroke(partial_bs)
partial_x, partial_y, partial_bs, partial_new_stroke = self.__extract_subs_from_samples(
partial_x, partial_y, partial_bs, partial_new_stroke)
self.__create_sample_sequence(x_samples, y_samples, bs_samples,
new_stroke_samples, partial_x, partial_y,
partial_bs, partial_new_stroke)
tensor = np.reshape(
(x_samples + y_samples + bs_samples + new_stroke_samples),
(len(x_samples), self.__num_samples, FEATURES))
states = [state for _ in range(len(x_samples))]
return np.array(tensor), np.array(states)