def sample(self, n_samples):
ts = TimeSeries()
ts.y = self.model.sample(n_samples)[0]
ts.x = range(len(ts.y))
out_path = "{}/plots/{}/sample.png".format(script_dir,
self.__class__.__name__)
plot_procedures.plot_ts(ts, out_path, compress=True)
def simulate():
ts_len = 1000
l1 = np.random.normal(1, 0.2, ts_len)
l2 = np.random.normal(5, 0.2, ts_len)
l = np.append(l1, l2)
ts = TimeSeries(compressed=True)
ts.x = range(1, len(l) + 1)
ts.y = l
return ts
def plot(self, server, mac, dt_start, dt_end, ts, hidden_state_path):
ts_hidden_state_path = TimeSeries()
ts_hidden_state_path.x = copy.deepcopy(ts.x)
ts_hidden_state_path.y = copy.deepcopy(hidden_state_path)
out_file_name = utils.get_out_file_name(server, mac, dt_start, dt_end)
out_path = "{}/plots/{}/{}.png".format(script_dir,
self.__class__.__name__,
out_file_name)
plot_procedures.plot_ts_share_x(
ts,
ts_hidden_state_path,
out_path,
compress=True,
title1="raw time series",
title2="best hidden state path",
plot_type2="scatter",
yticks2=range(self.model.n_components),
ylim2=[-0.5, self.model.n_components - 0.5])