fix: bad range management in bit.py

- use sensor_range * domain_width
- add asserts
- cleaner bit.neighborhood

sho/bit.py

@@ -1,3 +1,4 @@
+import math
 import numpy as np
 import copy
 
@@ -9,9 +10,15 @@ from . import x,y,pb
 
 def cover_sum(sol, domain_width, sensor_range):
     """Compute the coverage quality of the given array of bits."""
+    assert(0 < sensor_range <= math.sqrt(2))
+    assert(0 < domain_width)
+    assert(len(sol)>0)
     domain = np.zeros((domain_width,domain_width))
     sensors = to_sensors(sol)
-    return np.sum(pb.coverage(domain, sensors, sensor_range))
+    cov = pb.coverage(domain, sensors, sensor_range*domain_width)
+    s = np.sum(cov)
+    assert(s >= len(sensors))
+    return s
 
 
 def to_sensors(sol):
@@ -21,6 +28,7 @@ def to_sensors(sol):
     >>> to_sensors([[1,0],[1,0]])
     [(0, 0), (0, 1)]
     """
+    assert(len(sol)>0)
     sensors = []
     for i in range(len(sol)):
         for j in range(len(sol[i])):
@@ -46,23 +54,22 @@ def rand(domain_width, nb_sensors):
 ########################################################################
 
 def neighb_square(sol, scale, domain_width):
-    """Draw a random array by moving ones to adjacent cells."""
+    """Draw a random array by moving every ones to adjacent cells."""
     # Copy, because Python pass by reference
     # and we may not want to alter the original solution.
     new = copy.copy(sol)
     for py in range(len(sol)):
         for px in range(len(sol[py])):
+            # Indices order is (y,x) in order to match
+            # coordinates of images (row,col).
             if sol[py][px] == 1:
                 new[py][px] = 0 # Remove original position.
-                d = np.random.randint(-scale//2,scale//2,2)
+                # Add a one somewhere around.
-                if py+y(d) < 0 :
+                w = scale//2 * domain_width
-                    d[1] = np.random.randint(-py,scale//2)
+                ny = np.random.randint(py-w,py+w)
-                if py+y(d) >= domain_width :
+                nx = np.random.randint(px-w,px+w)
-                    d[1] = np.random.randint(-scale//2,domain_width-py)
+                ny = min(max(0,ny),domain_width-1)
-                if px+y(d) < 0 :
+                nx = min(max(0,nx),domain_width-1)
-                    d[0] = np.random.randint(0,scale//2)
+                new[ny][nx] = 1
-                if px+x(d) >= domain_width :
-                    d[0] = np.random.randint(-scale//2,domain_width-px)
-                new[py+y(d)][px+x(d)] = 1
     return new
 

sho/num.py

@@ -1,3 +1,4 @@
+import math
 import numpy as np
 
 from . import pb
@@ -13,6 +14,7 @@ def to_sensors(sol):
     >>> to_sensors([0,1,2,3])
     [(0, 1), (2, 3)]
     """
+    assert(len(sol)>0)
     sensors = []
     for i in range(0,len(sol),2):
         sensors.append( ( int(round(sol[i])), int(round(sol[i+1])) ) )
@@ -21,9 +23,15 @@ def to_sensors(sol):
 
 def cover_sum(sol, domain_width, sensor_range):
     """Compute the coverage quality of the given vector."""
+    assert(0 < sensor_range <= math.sqrt(2))
+    assert(0 < domain_width)
+    assert(len(sol)>0)
     domain = np.zeros((domain_width,domain_width))
     sensors = to_sensors(sol)
-    return np.sum(pb.coverage(domain, sensors, sensor_range))
+    cov = pb.coverage(domain, sensors, sensor_range*domain_width)
+    s = np.sum(cov)
+    assert(s >= len(sensors))
+    return s
 
 
 ########################################################################
@@ -42,7 +50,6 @@ def rand(dim, scale):
 def neighb_square(sol, scale, domain_width):
     """Draw a random vector in a square of witdh `scale`
     around the given one."""
-    # TODO handle constraints
     new = sol + (np.random.random(len(sol)) * scale - scale/2)
     return new
 

snp.py

@@ -1,3 +1,4 @@
+import math
 import numpy as np
 import matplotlib.pyplot as plt
 
@@ -19,7 +20,7 @@ if __name__=="__main__":
             help="Number of sensors")
 
     can.add_argument("-r", "--sensor-range", metavar="RATIO", default=0.3, type=float,
-            help="Sensors' range (as a fraction of domain width)")
+            help="Sensors' range (as a fraction of domain width, max is √2)")
 
     can.add_argument("-w", "--domain-width", metavar="NB", default=30, type=int,
             help="Domain width (a number of cells)")
@@ -48,7 +49,7 @@ if __name__=="__main__":
 
     # Minimum checks.
     assert(0 < the.nb_sensors)
-    assert(0 < the.sensor_range <= 1)
+    assert(0 < the.sensor_range <= math.sqrt(2))
     assert(0 < the.domain_width)
     assert(0 < the.iters)
 
@@ -89,7 +90,7 @@ if __name__=="__main__":
         val,sol = algo.greedy(
                 make.func(num.cover_sum,
                     domain_width = the.domain_width,
-                    sensor_range = the.sensor_range * the.domain_width),
+                    sensor_range = the.sensor_range),
                 make.init(num.rand,
                     dim = d * the.nb_sensors,
                     scale = the.domain_width),