def mean_error(gen_hmm, obs_seq, actual_states):
predicted_states = gen_hmm.define_labels_seq(obs_seq)
error_array=errors(
predicted = numpy.array(predicted_states),
actual = numpy.array(actual_states)
)
print(error_array.eval())
return error_array.eval().mean()
def get_error_on_patient(model, visible_set, hidden_set, algo, all_labels=True, pat=""):
predicted_states = model.predict(numpy.asarray(visible_set).reshape((-1, 1)))
if all_labels:
error_array = errors(predicted=predicted_states, actual=hidden_set)
return error_array.eval().mean()
else:
error_array = confusion_matrix(predicted_states=predicted_states, actual_states=hidden_set, pat=pat)
return error_array
def errors(self, y):
return errors(
predicted = self.y_pred,
actual = y,
not_shared = False
)
def errors(self, y):
return errors(predicted=self.y_pred, actual=y, not_shared=False)
