add KMP example (+fix KMP)

examples/models/gmm.py

@@ -70,7 +70,7 @@ plt.show()
 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()
+    g = gmm.condition(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]))
 

examples/models/gmr.py

@@ -67,7 +67,7 @@ plt.show()
 # GMR: condition on the input variable and plot
 gaussians = []
 for t in time_linspace:
-    g = gmm.condition(np.array([t]), idx_out=[1, 2], idx_in=[0]).approximate_by_single_gaussian()
+    g = gmm.condition(t, idx_out=[1, 2], idx_in=0).approximate_by_single_gaussian()
     gaussians.append(g)
 
 # plot figures for GMR

examples/models/kmp.py

@@ -0,0 +1,57 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""Provide some examples using KMP.
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from pyrobolearn.models.kmp import KMP
+
+
+# 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()
+
+# create KMP
+print("Creating the KMP model")
+kmp = KMP()
+
+# fit a KMP on the data
+print("Training the KMP...")
+kmp.fit(X=X[:, 0].reshape(1, -1, 1), Y=X[:, 1].reshape(1, -1, 1), gmm_num_components=5, mean_reg=1.,
+        covariance_reg=60., database_size_limit=n_samples)
+print("Finished the training")
+
+# predict using the KMP
+means, std_devs = [], []
+time_linspace = np.linspace(-6, 6, 100)
+for t in time_linspace:
+    g = kmp.predict_proba(t, return_gaussian=True)
+    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('KMP')
+plt.scatter(X[:, 0], X[:, 1])
+plt.show()