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selflocalization/random_numbers.py

@@ -0,0 +1,54 @@
+import numpy as np
+
+
+def randn(mu, sigma):
+    """Normal random number generator
+    mean mu, standard deviation sigma"""
+    return sigma * np.random.randn() + mu
+    
+    
+def rand_von_mises(mu, kappa):
+    """Generate random samples from the Von Mises distribution"""
+    if kappa < 1e-6:
+        # kappa is small: sample uniformly on circle
+        theta = 2.0 * np.pi * np.random.ranf() - np.pi
+    else:
+        a = 1.0 + np.sqrt(1.0 + 4.0 * kappa * kappa)
+        b = (a - np.sqrt(2.0 * a)) / (2.0 * kappa)
+        r = (1.0 + b*b) / (2.0 * b)
+
+        not_done = True
+        while not_done:
+            u1 = np.random.ranf()
+            u2 = np.random.ranf()
+
+            z = np.cos(np.pi * u1)
+            f = (1.0 + r * z) / (r + z)
+            c = kappa * (r - f)
+
+            not_done = (u2 >= c * (2.0 - c)) and (np.log(c) - np.log(u2) + 1.0 - c < 0.0)
+            
+            u3 = np.random.ranf()
+            theta = mu + np.sign(u3 - 0.5) * np.arccos(f)
+
+    return theta
+
+
+if __name__ == '__main__':
+    # Tests
+    
+    print("Gaussian distribution:")
+    r = np.zeros(1000)
+    for i in range(1000):
+        r[i] = randn(1.0, 2.0)
+        
+    print("True mean 1.0 == Estimated mean ", np.mean(r))
+    print("True std 2.0 == Estimated std ", np.std(r))
+    
+    print("Von Mises distribution:")
+    
+    t = np.zeros(1000)
+    for i in range(1000):
+        t[i] = rand_von_mises(1.0, 8)
+        
+    print("True mean 1.0 == Estimated mean ", np.mean(t))