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SASearch.py

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+import random
+import time
+import math
+
+# File Format:
+# Each line format should be "Order,A,B"
+# Ex.
+# 1,0.43,0.52
+# 2,0.59,0.678
+# 3,0.023,0.134
+# ...
+
+#--------- Classes -----------#
+
+class Vane:
+
+    def __init__(self, order, A, B):
+        self.order = order
+        self.A = A
+        self.B = B
+
+#-------- Functions ---------#
+
+def compute_avg_area(vanes):
+    totalArea = 0.0
+    numVanes = len(vanes)
+
+    for i in range(numVanes):
+        totalArea += vanes[i].A + vanes[i].B
+
+    return totalArea / numVanes
+
+def compute_dist(vanes, avgArea):
+    score = 0.0
+    numVanes = len(vanes)
+
+    for i in range(numVanes - 1):
+        score += abs(avgArea - vanes[i].A - vanes[i+1].B)
+
+    score += abs(avgArea - vanes[numVanes - 1].A - vanes[0].B)
+
+    return score
+
+def reduce_temp(temp, min_temp, alpha):
+    if (temp*alpha <= min_temp):
+        return min_temp
+    else:
+        return temp * alpha
+
+def random_swap(vanes):
+    a = random.randrange(0, len(vanes))
+    b = random.randrange(0, len(vanes) - 1)
+
+    if b >= a:
+        b = b + 1
+
+    temp = vanes[a]
+    vanes[a] = vanes[b]
+    vanes[b] = temp
+
+def check_acceptance(nextDist, bestDist, temp):
+    if nextDist <= bestDist:
+        return True
+    else:
+        delta = bestDist - nextDist
+        p_acceptance = math.exp(delta/temp)
+        x = random.uniform(0, 1)
+
+        if x < p_acceptance:
+            return True
+        else:
+            return False
+
+def check_match(set1, set2):
+    if len(set1) != len(set2):
+        return False
+
+    for i in range(len(set1)):
+        if set1[i].order != set2[i].order:
+            return False
+
+    return True
+
+def get_input_vanes():
+    file = input("Enter input file name (press enter to default to \"dataset.txt\"): ")
+
+    if file == "":
+        file = "dataset.txt"
+
+    fin = open(file)
+
+    vanes = []
+
+    for line in fin:
+        spline = line.split(",")
+        vanes.append(Vane(int(spline[0]), float(spline[1]), float(spline[2])))
+
+    fin.close()
+
+    return vanes
+
+#--------- Program ----------#
+
+vanes = get_input_vanes()
+
+start = time.perf_counter()
+
+avgArea = compute_avg_area(vanes)
+
+bestVanes = vanes
+random.shuffle(bestVanes)
+bestDist = compute_dist(bestVanes, avgArea)
+
+alpha = 0.9
+temp = 10
+final_temp = 0.0001
+numIterations = 1000
+numSwaps = 1
+
+done = False
+
+while not(done):
+    curVanes = list(bestVanes)
+
+    for i in range(numIterations):
+        nextVanes = list(bestVanes)
+        for j in range(numSwaps):
+            random_swap(nextVanes)
+
+        nextDist = compute_dist(nextVanes, avgArea)
+
+        if check_acceptance(nextDist, bestDist, temp):
+            bestDist = nextDist
+            bestVanes = nextVanes
+
+    if temp == final_temp and check_match(bestVanes, curVanes):
+        done = True
+
+    temp = reduce_temp(temp, final_temp, alpha)
+
+end = time.perf_counter()
+
+elapsed = "%.12f" % (end - start)
+
+fout = open("output.txt", "wt")
+fout.write("Elapsed Time: " + elapsed + " sec\n")
+fout.write("Solution's Deviation Score: " + str(bestDist) + "\n")
+
+for i in range(len(bestVanes)):
+    fout.write(str(bestVanes[i].order) + "," + str(bestVanes[i].A) + "," + str(bestVanes[i].B) + "\n")
+
+fout.close()
+
+print("Results outputted to \"output.txt\"")

generateDataset.py

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+import random
+import decimal
+
+print("Enter number of data points to generate:")
+numVanes = input()
+
+fout = open("dataset.txt", "wt")
+for i in range(int(numVanes)):
+	valueA = random.randint(2, 44)/1000
+	valueB = random.randint(2, 44)/1000
+	fout.write(str(i) + "," + str(valueA) + "," + str(valueB) + "\n")
+fout.close()