add 2 examples in models

examples/models/gmm.py

@@ -0,0 +1,66 @@
+#!/usr/bin/env python
+"""Provide some examples using GMMs.
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.mixture import GaussianMixture
+
+from pyrobolearn.models.gmm import Gaussian, GMM, plot_gmm, plot_gmm_sklearn
+
+
+# create manually a GMM
+dim, num_components = 2, 5
+gmm = GMM(gaussians=[Gaussian(mean=np.random.uniform(-1., 1., size=dim),
+                              covariance=0.1*np.identity(dim)) for _ in range(num_components)])
+gmm_sklearn = GaussianMixture(n_components=num_components)
+
+
+# plot initial GMM
+plot_gmm(gmm, title='Initial GMM')
+plt.show()
+
+
+# create data: Generate random sample following a sine curve
+# Ref: https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm_sin.html#sphx-glr-auto-examples-mixture-\
+# plot-gmm-sin-py
+n_samples = 100
+np.random.seed(0)
+X = np.zeros((n_samples, 2))
+step = 4. * np.pi / n_samples
+
+for i in range(X.shape[0]):
+    x = i * step - 6.
+    X[i, 0] = x + np.random.normal(0, 0.1)
+    X[i, 1] = 3. * (np.sin(x) + np.random.normal(0, .2))
+
+xlim, ylim = [-8, 8], [-8, 8]
+
+# plot data
+plt.title('Training data')
+plt.scatter(X[:, 0], X[:, 1])
+plt.show()
+
+
+# init GMM
+init_method = 'k-means'  # 'random', 'k-means', 'uniform', 'sklearn', 'curvature'
+gmm.init(X, method=init_method)
+fig, ax = plt.subplots(1, 1)
+plot_gmm(gmm, X=X, ax=ax, title='GMM after ' + init_method.capitalize(), xlim=xlim, ylim=ylim)
+plt.show()
+
+
+# fit a GMM using EM
+result = gmm.fit(X, init=None)
+gmm_sklearn.fit(X)
+
+# plot EM optimization
+plt.plot(result['losses'])
+plt.title('EM per iteration')
+plt.show()
+
+# plot trained GMM
+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()

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()