def runHillClimbingGridSearch():
print("4. Hill Climbing (Grid) used ",end="")
start=time.clock()
minn=100000000
probes=0
iterator=0
l=[]
#create a matrix of specific points
for x in range(0,11):
for y in range(0,11):
l.append(x)
l.append(y)
poin=Vector(random()*DOMAIN_LIMIT,random()*DOMAIN_LIMIT)
bestN=findBestNeighbor(poin)
while time.clock()-start<=.1 and iterator<200:
newp=Vector(l[iterator], l[iterator+1])
iterator+=2
newBN=findBestNeighbor(newp)
probes+=1
if bestN.cost()>newBN.cost():
poin=newp
bestN=newBN
print(probes," probes")
print("X= ",bestN[0],end="")
print("Y= ",bestN[1]," Cost= ", bestN.cost())
print("Search Time= ",time.clock()-start)
#exactley the same, except teh newP is from a matrix
coont=0
def findBestNeighbor(point):
from math import sin,cos,pi
radius=HILL_CLIMBING_RADIUS
for t in frange(0, 2*pi, 2*pi/16):
x=point[0]+radius*cos(t)
y=point[1]+radius*sin(t)
bestNeighbor=Vector(x,y)
if(bestNeighbor.cost()<point.cost()):
point.equals(bestNeighbor)
return point
def runRandomSearch(): #working?
#evaluate 'a million random solutions and choose the best one
from random import random
print("2. Random Probing used", end=" ")
start= time.clock()
count=0
minV=Vector(9999999,999999)
while time.clock()-start<.1:
xmin=random()*DOMAIN_LIMIT
ymin=random()*DOMAIN_LIMIT
vector= Vector(xmin,ymin)
minV= minV.minVec(vector)
count+=1
print(count, end="")
print(" probes")
print("x=",minV[0]," y=",(minV[1]),"cost=",minV.cost())
print("Time= ",.1)
def runHillClimbingRandomResetSearch():
#neighbor by neighbor solution. move to the best neighbor solution. repeat
#until local min or max
#then start over and find the smallest of many local mins.
#instead of using sines and cosines, pre calculate once at begining and put in table
print("3. Hill Climbing (Random Reset) used ",end="")
start=time.clock()
minn=100000000
probes=0
poin=Vector(random()*DOMAIN_LIMIT,random()*DOMAIN_LIMIT)
bestN=findBestNeighbor(poin)
while time.clock()-start<=.1:
probes+=1
newp=Vector(random()*DOMAIN_LIMIT,random()*DOMAIN_LIMIT)
newBN=findBestNeighbor(newp)
if bestN.cost()>newBN.cost():
poin=newp
bestN=newBN
print(probes," probes")
print("X= ",bestN[0],end="")
print("Y= ",bestN[1]," Cost= ", bestN.cost())
print("Search Time= ",.1)
def runNelderMead():
start= time.clock()
print("1. Nelder-Mead used ",end="")
from random import random
triangleCount=0
a= Vector(DOMAIN_LIMIT*random(), DOMAIN_LIMIT*random()) #someow find a random point
b=Vector(DOMAIN_LIMIT*random(), DOMAIN_LIMIT*random())
c=Vector(DOMAIN_LIMIT*random(), DOMAIN_LIMIT*random())
while triangleCount<MAX_TRIANGLE_COUNT and time.clock()-start<.1:
if b.dist(a)<.02:#dist btwn a and b
break
if a.cost()<b.cost():
a.swap(b)
if c.cost()<b.cost():
c.swap(b)
if a.cost()<b.cost():
a.swap(b)
d=b+c-a
e=(3*(b+c)-4*a)/2
f=(3*(b+c)-2*a)/4
g=(2*a+b+c)/4
x=f.minVec(g)
triangleCount+=1
if d.cost()<a.cost() and e.cost()<a.cost():
a.equals(e)
elif d.cost()<a.cost():
a.equals(d)
elif x.cost()<a.cost(): a.equals(x)
else:
a.equals((a+b)/2)
c.equals((b+c)/2)
#if d.cost() <A and E.cost()<A.cost, E isnow A. go to while
#elif D<A.cost() A=D. go to while loop
#elif X= vector (G or F) with min cost. if X.cost()<A.cost(), then A=X
#else: A=H, C=I. Best is still B. go to #3
print(triangleCount," random triangles.")
print("x= ",b[0],end="")
print(" y=",b[1],end="")
print(" cost=" ,b.cost())
timee=time.clock()-start
print("Search Time= ",timee)
print("\n")
