add 2 examples in models

examples/models/promp.py

@@ -0,0 +1,56 @@
+#!/usr/bin/env python
+"""Provide some examples using ProMPs.
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from pyrobolearn.models.promp.promp import DiscreteProMP, plot_state, plot_proba_state, plot_weighted_basis
+
+
+# create data and plot it
+N = 8
+t = np.linspace(0., 1., 100)
+# eps = 0.1
+# y = np.array([np.sin(2*np.pi*t) + eps * np.random.rand(len(t)) for _ in range(N)])      # shape: NxT
+# dy = np.array([2*np.pi*np.cos(2*np.pi*t) + eps * np.random.rand(len(t)) for _ in range(N)])     # shape: NxT
+phi = np.random.uniform(low=-1., high=1., size=N)
+y = np.array([np.sin(2 * np.pi * t + phi[i]) for i in range(int(N/2))])  # shape: NxT
+y1 = np.array([np.cos(2 * np.pi * t + phi[i]) for i in range(int(N/2))])
+y = np.vstack((y, y1))
+dy = np.array([2 * np.pi * np.cos(2 * np.pi * t + phi[i]) for i in range(int(N/2))])  # shape: NxT
+dy1 = np.array([2 * np.pi * np.sin(2 * np.pi * t + phi[i]) for i in range(int(N/2))])
+dy = np.vstack((dy, dy1))
+Y = np.dstack((y, dy))  # N,T,2D  --> why not N,2D,T
+plot_state(Y, title='Training data')
+plt.show()
+
+# create discrete and rhythmic ProMP
+promp = DiscreteProMP(num_dofs=1, num_basis=10, basis_width=1./20)
+
+# plot the basis function activations
+plt.plot(promp.Phi(t)[:, :, 0].T)
+plt.title('basis functions')
+plt.show()
+
+# plot ProMPs
+y_pred = promp.rollout()
+fig, ax = plt.subplots(1, 2)
+plot_state(y_pred[None], ax=ax, title='ProMP prediction before learning', linewidth=2.)    # shape: N,T,2D
+plot_weighted_basis(t, promp, ax=ax)
+plt.show()
+
+# learn from demonstrations
+promp.imitate(Y)
+y_pred = promp.rollout()
+fig, ax = plt.subplots(1, 2)
+plot_state(y_pred[None], ax=ax, title='ProMP prediction after learning', linewidth=3.)   # N,T,2D
+plot_weighted_basis(t, promp, ax=ax)
+plt.show()
+
+method = 'marginal'
+means, covariances = promp.rollout_proba(method=method, return_gaussian=False)
+fig, ax = plt.subplots(1, 2)
+# plot_state(Y, ax=ax, title='Training data')
+plot_proba_state(means, covariances, ax=ax, title='ProMP prediction after learning', linewidth=3.)
+plt.show()