refactor everything as operators of an iterative search

sho/snp.py

@@ -1,38 +1,157 @@
-import matplotlib.pyplot as plt
 import numpy as np
+import matplotlib.pyplot as plt
+import copy
 
-def x(p):
+########################################################################
-    return p[0]
+# Utilities
+########################################################################
 
-def y(p):
+def x(a):
-    return p[1]
+    return a[0]
+
+def y(a):
+    return a[1]
 
 def distance(a,b):
     return np.sqrt( (x(a)-x(b))**2 + (y(a)-y(b))**2 )
 
-def count(domain_shape, sensors_positions, radius, output_domain = None):
+
-    s = 0
+########################################################################
-    Y,X = domain_shape
+# Objective functions
-    for y in range(Y):
+########################################################################
-        for x in range(X):
+
-            p = (x,y)
+def coverage(domain,sensors,sensor_range):
-            t = 0
+    for py in range(len(domain)):
-            for sensor in sensors_positions:
+        for px in range(len(domain[py])):
-                if distance( p, sensor ) < radius:
+            p = (px,py)
-                    t += 1
+            for x in sensors:
-                    # break
+                if distance(x,p) < sensor_range:
-            if output_domain is not None:
+                    domain[py][px] = 1
-                output_domain[y][x] = t
+                    break
-            s += t
+    return domain
-    return s
+
+def cover_bit(sol,domain_width,sensor_range):
+    domain = np.zeros((domain_width,domain_width))
+    sensors = []
+    for i in range(domain_width):
+        for j in range(domain_width):
+            if sol[i][j] == 1:
+                sensors.append( (j,i) )
+    return np.sum(coverage(domain, sensors, sensor_range))
+
+def cover_num(sol,domain_width,sensor_range):
+    domain = np.zeros((domain_width,domain_width))
+    sensors = []
+    for i in range(0,len(sol),2):
+        sensors.append( (sol[i],sol[i+1]) )
+    return np.sum(coverage(domain, sensors, sensor_range))
+
+def make_func(cover,**kwargs):
+    def f(sol):
+        return cover(sol,**kwargs)
+    return f
+
+
+########################################################################
+# Initialization
+########################################################################
+
+def rand_num(dim):
+    return np.random.random(dim)
+
+def rand_bit(domain_width,nb_sensors):
+    domain = np.zeros((domain_width,domain_width))
+    for x,y in np.random.randint(0,domain_width,(nb_sensors,2)):
+        domain[y][x] = 1
+    return domain
+
+def make_init(init,**kwargs):
+    def f():
+        return init(**kwargs)
+    return f
+
+
+########################################################################
+# Neighborhood
+########################################################################
+
+def neighb_num_rect(sol, scale):
+    return np.random.random(len(sol)) * scale - scale/2
+
+def neighb_bit_rect(sol, scale):
+    # Copy, because Python pass by reference.
+    new = copy.copy(sol)
+    for yy in range(len(sol)):
+        for xx in range(len(sol[yy])):
+            if sol[yy][xx] == 1:
+                new[yy][xx] = 0
+                d = np.random.randint(-scale//2,scale//2,2)
+                new[yy+y(d)][xx+x(d)] = 1
+    return new
+
+def make_neig(neighb,**kwargs):
+    def f(sol):
+        return neighb(sol, **kwargs)
+    return f
+
+
+########################################################################
+# Stopping criterions
+########################################################################
+
+def iters_nb(val,sol,nb_it):
+    for i in range(nb_it):
+        yield i
+    yield i
+
+def make_iter(iters,nb_it):
+    def cont(val,sol):
+        return iters(val,sol,nb_it)
+    return cont
+
+
+########################################################################
+# Algorithms
+########################################################################
+
+def search(func, init, neighb, iters):
+    best_sol = init()
+    best_val = func(best_sol)
+    for i in iters(best_val, best_sol):
+        sol = neighb(best_sol)
+        val = func(sol)
+        if val > best_val:
+            best_val = val
+            best_sol = sol
+    return val,sol
 
 if __name__=="__main__":
 
-    domain = np.zeros( (100,100) )
+    d = 2
+    nb_sensors = 3
+    sensor_range = 2
+    domain_width = 10
 
-    sensors = np.round(np.random.random( (3,2) ) * 100)
+    # domain = np.zeros((domain_width,domain_width))
+    # domain = coverage(domain,[(10,50),(40,80)],50)
+    # plt.imshow(domain)
+    # plt.show()
 
-    s = count(domain.shape, sensors, 40, domain)
+    print(
+            search(
+                make_func(cover_num, domain_width=domain_width, sensor_range=sensor_range),
+                make_init(rand_num, dim=d * nb_sensors),
+                make_neig(neighb_num_rect, scale=domain_width/10),
+                make_iter(iters_nb,10)
+            )
+        )
+
+    print(
+            search(
+                make_func(cover_bit, domain_width=domain_width, sensor_range=sensor_range),
+                make_init(rand_bit, domain_width=domain_width, nb_sensors=nb_sensors),
+                make_neig(neighb_bit_rect, scale=3),
+                make_iter(iters_nb,10)
+            )
+        )
 
-    plt.imshow(domain)
-    plt.show()