Sample predictions test

sample_predictions.py

import numpy as np
import matplotlib.pyplot as plt
from pylds.models import DefaultLDS

inputs = \
    np.array([[ 0.        ,  0.        ],
    [ 2.72785283,  7.53608657],
    [ 0.        ,  7.23201033],
    [ 2.0261219 ,  7.07866193],
    [ 2.42989525,  7.67644583],
    [ 2.80870737,  7.83475645],
    [ 0.        ,  7.9209767 ],
    [ 0.        ,  7.79186987],
    [ 2.78714571,  8.37162161],
    [ 2.49732917,  8.14119562],
    [ 2.71444   ,  7.94740989],
    [ 2.54583288,  8.05844122],
    [ 2.51490101,  8.18854467],
    [ 1.61097519,  7.88220925],
    [ 2.58138165,  7.85400085],
    [ 2.45695553,  7.71646971],
    [ 2.86483477,  7.96989029]])


data = \
    np.array([[ 7.53608657],
        [ 7.23201033],
        [ 7.07866193],
        [ 7.67644583],
        [ 7.83475645],
        [ 7.9209767 ],
        [ 7.79186987],
        [ 8.37162161],
        [ 8.14119562],
        [ 7.94740989],
        [ 8.05844122],
        [ 8.18854467],
        [ 7.88220925],
        [ 7.85400085],
        [ 7.71646971],
        [ 7.96989029],
        [ 7.68803383]])

offset = data.mean(axis=0)
data -= offset

model = DefaultLDS(D_obs=1, D_latent=2, D_input=2)
model.add_data(data, inputs=inputs)

for _ in range(50):
    model.EM_step()

T_given = 13
T_predict = 4
given_data = data[:T_given]
given_inputs = inputs[:T_given]

smooth_data = model.smooth(given_data, given_inputs)

preds = \
    model.sample_predictions(
        given_data, inputs=given_inputs,
        Tpred=T_predict,
        inputs_pred=inputs[T_given:T_given + T_predict],
        states_noise=False, obs_noise=False)

smooth_plus_pred = np.concatenate((smooth_data, preds))

plt.plot(data, label="true")
plt.plot(smooth_plus_pred[:T_given], label="smoothed")
plt.plot(np.arange(T_given-1, T_given+T_predict), smooth_plus_pred[T_given-1:], label="pred")
plt.xlabel("Time")
plt.ylabel("Data")
plt.legend(loc="upper left")

ylim = plt.ylim()
plt.plot([T_given-1, T_given-1], ylim, '-k')
plt.ylim(ylim)
plt.show()