adding jupyter notebooks

Sample codes/pso.py

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+#!/usr/bin/env python
+
+'''
+pso.py
+A simple implementation of the Particle Swarm Optimisation Algorithm.
+Uses Numpy for matrix operations. 
+Pradeep Gowda 2009-03-16
+'''
+
+from numpy import *
+from random import random
+from math import sin, sqrt
+
+iter_max = 10000
+pop_size = 100
+dimensions = 2
+c1 = 2
+c2 = 2
+err_crit = 0.00001
+
+class Particle:
+    pass
+        
+
+def f6(param):
+    para = param*10
+    para = param[0:2]
+    num = (sin(sqrt((para[0] * para[0]) + (para[1] * para[1])))) * \
+        (sin(sqrt((para[0] * para[0]) + (para[1] * para[1])))) - 0.5
+    denom = (1.0 + 0.001 * ((para[0] * para[0]) + (para[1] * para[1]))) * \
+            (1.0 + 0.001 * ((para[0] * para[0]) + (para[1] * para[1])))
+    f6 =  0.5 - (num/denom)
+    errorf6 = 1 - f6
+    return f6, errorf6;
+ 
+ 
+#initialize the particles
+particles = []
+for i in range(pop_size):
+    p = Particle()
+    p.params = array([random() for i in range(dimensions)])
+    p.fitness = 0.0
+    p.v = 0.0
+    particles.append(p)
+
+# let the first particle be the global best
+gbest = particles[0]
+err = 999999999
+while i < iter_max :
+    for p in particles:
+        fitness,err = f6(p.params)
+        if fitness > p.fitness:
+            p.fitness = fitness
+            p.best = p.params
+
+        if fitness > gbest.fitness:
+            gbest = p
+        v = p.v + c1 * random() * (p.best - p.params) \
+                + c2 * random() * (gbest.params - p.params)
+        p.params = p.params + v
+          
+    i  += 1
+    if err < err_crit:
+        break
+    #progress bar. '.' = 10%
+    if i % (iter_max/10) == 0:
+        print '.'
+
+print '\nParticle Swarm Optimisation\n'
+print 'PARAMETERS\n','-'*9
+print 'Population size : ', pop_size
+print 'Dimensions      : ', dimensions
+print 'Error Criterion : ', err_crit
+print 'c1              : ', c1
+print 'c2              : ', c2
+print 'function        :  f6'
+
+print 'RESULTS\n', '-'*7
+print 'gbest fitness   : ', gbest.fitness
+print 'gbest params    : ', gbest.params
+print 'iterations      : ', i+1
+## Uncomment to print particles
+#for p in particles:
+#    print 'params: %s, fitness: %s, best: %s' % (p.params, p.fitness, p.best)