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Added GMR computation for given GMM model.

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Rozo Leonel (CR/PJ-AI-R31)
2019-10-25 11:56:00 +02:00
parent 5de0cd347b
commit f91761f5e3
1 changed files with 17 additions and 0 deletions
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examples/models/gmm.py
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@@ -65,3 +65,20 @@ fig, ax = plt.subplots(1, 2)
plot_gmm(gmm, X=X, label=True, ax=ax[0], title='Our Trained GMM', option=1, xlim=xlim, ylim=ylim)
plot_gmm_sklearn(gmm_sklearn, X, label=True, ax=ax[1], title="Sklearn's Trained GMM", xlim=xlim, ylim=ylim)
plt.show()
# GMR: condition on the input variable and plot
means, std_devs = [], []
time_linspace = np.linspace(-6, 6, 100)
for t in time_linspace:
g = gmm.condition(np.array([t]), idx_out=[1], idx_in=[0]).approximate_by_single_gaussian()
means.append(g.mean[0])
std_devs.append(np.sqrt(g.covariance[0, 0]))
means, std_devs = np.asarray(means), np.asarray(std_devs)
plt.plot(time_linspace, means)
plt.fill_between(time_linspace, means - 2 * std_devs, means + 2 * std_devs, facecolor='green', alpha=0.3)
plt.fill_between(time_linspace, means - std_devs, means + std_devs, facecolor='green', alpha=0.5)
plt.title('GMR')
plt.scatter(X[:, 0], X[:, 1])
plt.show()