def _create_model(self, obs, pred): m = PfModel() for f in pred: name = f.split('/')[-1].split('.')[0] with open(f, 'r') as a: m.add_prediction_matrix(name, np.loadtxt(a)) for f in obs: name = f.split('/')[-1].split('.')[0] with open(f, 'r') as a: m.add_observation_matrix(name, np.loadtxt(a)) return m.get()
def _create_model(self, obs, pred): m = PfModel() for f in pred: name = f.split('/')[-1].split('.')[0] with open(f, 'r') as a: m.add_prediction_matrix(name, np.loadtxt(a)) for f in obs: name = f.split('/')[-1].split('.')[0] with open(f, 'r') as a: m.add_observation_matrix(name, np.loadtxt(a)) return m.get()
def load_files(path): m = PfModel() for f in os.listdir(path): if f.endswith(".pred"): name = f.split('.')[0] filename = path + '/' + f with open(filename, 'r') as a: m.add_prediction_matrix(name, np.loadtxt(a)) elif f.endswith(".obs"): name = f.split('.')[0] filename = path + '/' + f with open(filename, 'r') as a: m.add_observation_matrix(name, np.loadtxt(a)) return m.get()
def load_files(path): m = PfModel() for f in os.listdir(path): if f.endswith(".pred"): name = f.split('.')[0] filename = path + '/' + f with open(filename, 'r') as a: m.add_prediction_matrix(name, np.loadtxt(a)) elif f.endswith(".obs"): name = f.split('.')[0] filename = path + '/' + f with open(filename, 'r') as a: m.add_observation_matrix(name, np.loadtxt(a)) return m.get()