def gradPhiApprox(params, cD, h):
e_1 = [1, 0]
e_2 = [0, 1]
point_diff1 = M.addVek(params, M.scaleVek(h, e_1))
quotient_diff1 = phi(point_diff1, cD) / h - phi(params, cD) / h
point_diff2 = M.addVek(params, M.scaleVek(h, e_2))
quotient_diff2 = phi(point_diff2, cD) / h - phi(params, cD) / h
return [quotient_diff1, quotient_diff2]
def stepSize_posGrad(cD, point, d, n):
alpha = 0.5
nextPoint = M.addVek(point, M.scaleVek(alpha, d))
while (cV.phi(point, cD) + eps < cV.phi(
nextPoint, cD)): #or pS.scalGrad( cD , nextPoint ) < 0 ):
alpha = alpha * 0.5
nextPoint = M.addVek(point, M.scaleVek(alpha, d))
if n % 300 == 0:
print(alpha)
if alpha < eps == 1:
return 0
return alpha
def diffConeVolIteratorApprox(params, cD, h):
e_1 = [1, 0]
e_2 = [0, 1]
point_diff1 = M.addVek(params, M.scaleVek(h, e_1))
quotient_diff1 = M.subVek(coneVolIterator(cD, point_diff1),
coneVolIterator(cD, params))
quotient_diff1 = M.scaleVek(1.0 / h, quotient_diff1)
point_diff2 = M.addVek(params, M.scaleVek(h, e_2))
quotient_diff2 = M.subVek(coneVolIterator(cD, point_diff2),
coneVolIterator(cD, params))
quotient_diff2 = M.scaleVek(1.0 / h, quotient_diff2)
return M.Matrix([quotient_diff1, quotient_diff2]).copyTrans()
def coneVolumeDescendBFGSApprox(cD, params_new, params_prev, crease,
efficiency, beta_1, beta_2):
A_k = M.idMatrix(2)
n = 0
while (cV.phi(params_new, cD) > eps_descend):
while (True):
try:
A_k = BFGS.getBFGSApprox(cD, params_new, params_prev, A_k)
break
except ZeroDivisionError:
print('es geht nicht besser mit BFGSApprox')
return params_new
break
antiGrad = M.scaleVek(-1, cV.gradPhiApprox(params_new, cD, 0.0001))
d = A_k.lgsSolve(antiGrad)
d = M.scaleVek(1.0 / M.norm(d), d)
alpha = sS.stepSizeArmijo(cD, params_new, d, crease, efficiency,
beta_1, beta_2)
d = M.scaleVek(alpha, d)
params_prev = [params_new[0], params_new[1]]
params_new = M.addVek(params_new, d)
if (M.dist(params_new, params_prev) < minimalStep_BFGSApprox):
print(' distance is lower than minimalStep of BFGSApprox ')
break
n = n + 1
return params_new
def coneVolumeDescendArmijo(cD, start, crease, efficiency, beta_1, beta_2):
result = start
n = 0
previous = [-1, -1]
print('gehe in while schleife ')
while (cV.phi(result, cD) > eps_descend):
if M.dist(previous, result) > minimalStep_descendArmijo:
previous[0] = result[0]
previous[1] = result[1]
d = M.scaleVek(-1, cV.gradPhi(result, cD))
#d = M.scaleVek( 1 / M.norm(d) , d )
alpha = sS.stepSizeArmijo(cD, result, d, crease, efficiency,
beta_1, beta_2)
result = M.addVek(result, M.scaleVek(alpha, d))
else:
print('versuche es mit BFGS und BFGSApprox')
result_1 = coneVolumeDescendBFGSApprox(cD, result, previous,
crease, efficiency, beta_1,
beta_2)
result_2 = coneVolumeDescendBFGS(cD, result, previous, crease,
efficiency, beta_1, beta_2)
if cV.phi(result_1, cD) < cV.phi(result_2, cD):
return result_1
else:
return result_2
n = n + 1
return result
def gamma(coneData, params):
# d_0 und d_1 verdrehen?
d_0 = [-coneData[0][1], coneData[0][0]]
d_1 = [coneData[1][1], -coneData[1][0]]
v = M.scaleVek(params[0], d_0)
w = M.scaleVek(params[1], d_1)
return M.addVek(v, w)
def hMethodConeVolIteratorCheck(cD, point, eps, h):
while (True):
try:
vektor = cV.coneVolIterator(cD, point)
break
except ZeroDivisionError:
return True
break
diff = cV.diffConeVolumeIterator(cD, point)
e_1 = [1, 0]
e_2 = [0, 1]
diff_1 = diff.image(e_1)
diff_2 = diff.image(e_2)
point_diff11 = M.addVek(point, M.scaleVek(h, e_1))
point_diff12 = M.subVek(point, M.scaleVek(h, e_1))
quotient_diff1 = M.subVek(
M.scaleVek(1 / (2 * h), cV.coneVolIterator(cD, point_diff11)),
M.scaleVek(1 / (2 * h), cV.coneVolIterator(cD, point_diff12)))
point_diff21 = M.addVek(point, M.scaleVek(h, e_2))
point_diff22 = M.subVek(point, M.scaleVek(h, e_2))
quotient_diff2 = M.subVek(
M.scaleVek(1 / (2 * h), cV.coneVolIterator(cD, point_diff21)),
M.scaleVek(1 / (2 * h), cV.coneVolIterator(cD, point_diff22)))
difference_1 = M.dist(quotient_diff1, diff_1)
difference_2 = M.dist(quotient_diff2, diff_2)
print(' the differences equals')
print(diff_1)
print(quotient_diff1)
print(diff_2)
print(quotient_diff2)
if difference_1 >= eps or difference_2 >= eps:
print(' the differences equals')
print(difference_1)
print(difference_2)
return False
return True
def hMethodPolyFunctionalCheck(cD, point, eps, h):
while (True):
try:
value = cV.polyFunctional(cD, point)
break
except ZeroDivisionError:
return True
break
e_1 = [1, 0]
e_2 = [0, 1]
grad_point = cV.gradPolyFunctional(cD, point)
# diff is a row, grad is a column...
diff = M.Matrix([grad_point])
diff_1 = diff.image(e_1)[0]
diff_2 = diff.image(e_2)[0]
pointTrans_1 = M.addVek(point, M.scaleVek(h, e_1))
pointTrans_2 = M.addVek(point, M.scaleVek(h, e_2))
diffQuotient_1 = cV.polyFunctional(cD, pointTrans_1) / h
substraction_1 = cV.polyFunctional(cD, point) / h
diffQuotient_1 = diffQuotient_1 - substraction_1
diffQuotient_2 = cV.polyFunctional(cD, pointTrans_2) / h
substraction_2 = cV.polyFunctional(cD, point) / h
diffQuotient_2 = diffQuotient_2 - substraction_2
dist_1 = math.fabs(diff_1 - diffQuotient_1)
dist_2 = math.fabs(diff_2 - diffQuotient_2)
if dist_1 > eps or dist_2 > eps:
print(' difference equals ')
print(dist_1)
print(dist_2)
print(' calculated diff and approximated diff:')
diff.list()
print([diffQuotient_1, diffQuotient_2])
return False
return True
def vizNiveauGradOnGrid( size , stepSize , cD , param , grad , eps):
grid = []
value_param = cV.phi( param , cD )
n = math.floor(size * 1.0/stepSize)
for i in range(n):
for j in range(n):
grid.append( [ i * stepSize - size / 2.0 + param[0], j *stepSize - size / 2.0 + param[1]])
print('grid ist fertig')
x_1 = []
y_1 = []
for point in grid:
while(True):
try:
value= cV.phi(point , cD )
if math.fabs(value - value_param) < eps:
x_1.append(point[0])
y_1.append([point[1]])
break
except ZeroDivisionError:
break
d= M.scaleVek( 1.0 / M.norm(grad) , grad )
x_d = []
y_d = []
stepBound = 0.71 * size
step = - stepBound
while( step <= stepBound ):
point = M.addVek( param , M.scaleVek( step , d ) )
x_d.append( point[0] )
y_d.append( point[1] )
step = step + stepSize
a_x = param[0] - size / 2.0
b_x = param[0] + size / 2.0 + 5
a_y = param[1] - size / 2.0
b_y = param[1] + size / 2.0 + 5
mp.plot( x_1 , y_1 , 'go')
mp.plot( x_d , y_d , 'yo')
mp.plot([param[0]], [param[1]], 'wo')
mp.axes( [a_x , b_x , a_y , b_y])
#print('here comes minimal point, gamma , value, gradient:')
#print( point_min )
#print( cV.gamma( cD , point_min ))
#print( cV.phi( point_min , cD ))
#print( cV.gradPhi( point_min , cD))
mp.show()
def nextVertex(u, volume, point):
orth = []
# gegen uhrzeigersinn um 90 grad drehen
orth.append(-u[1])
orth.append(u[0])
# assumes that 0 lies in K (i.e. point is not orthogonal)
d = M.scaleVek(2 * volume / M.scal(point, u), orth)
temp = M.addVek(point, d)
point[0] = temp[0]
point[1] = temp[1]
def getDistOrderedGrid( steps , stepSize ):
grid = []
n = math.floor( steps / stepSize )
#range( n ) = [ 0 , 1 , ... , n - 1 ], so ...
for k in range( n + 1):
diagonalStart = [ k * stepSize , 0 ]
grid.append(diagonalStart)
for l in range(k):
diagonalStart = M.addVek( diagonalStart , [ - stepSize , stepSize ] )
grid.append( diagonalStart )
return grid
def hMethodNextVertexCheck(u, V, point, eps, h):
nextVektor = cV.getNextVertex(u, V, point)
while (True):
try:
nextVektor = cV.getNextVertex(u, V, point)
break
except ZeroDivisionError:
return True
break
diff = cV.diffGetNextVertex(u, V, point)
e_1 = [1, 0]
e_2 = [0, 1]
diff_1 = diff.image(e_1)
diff_2 = diff.image(e_2)
point_diff1 = M.addVek(point, M.scaleVek(h, e_1))
quotient_diff1 = M.subVek(
M.scaleVek(1 / h, cV.getNextVertex(u, V, point_diff1)),
M.scaleVek(1 / h, cV.getNextVertex(u, V, point)))
point_diff2 = M.addVek(point, M.scaleVek(h, e_2))
quotient_diff2 = M.subVek(
M.scaleVek(1 / h, cV.getNextVertex(u, V, point_diff2)),
M.scaleVek(1 / h, cV.getNextVertex(u, V, point)))
difference_1 = M.dist(quotient_diff1, diff_1)
difference_2 = M.dist(quotient_diff2, diff_2)
print(' the differences equals')
print(difference_1)
print(difference_2)
if difference_1 >= eps or difference_2 >= eps:
print(' the differences euqal')
print(difference_1)
print(difference_2)
return False
return True
def gradPolyFunctionalTest_random(repeats, edgeNumber, eps, h, delta,
nearby_option):
n = 1
if not nearby_option:
while n <= repeats:
P = rP.getRandomPolygon(edgeNumber)
cD_test = cV.getConeVol(P)
point_test = [random.random() * 10, random.random() * 10]
# could be improved by making delta smaller if check is false ... orby setting point_test in the near of an vertex...
check = hMethodPolyFunctionalCheck(cD_test, point_test, eps, h)
if not check:
print(
' gradPolyFunctionalTest failed with polytop , point_test , value : '
)
print(P)
print(point_test)
print(cV.polyFunctional(cD_test, point_test))
n = n + 1
else:
while n <= repeats:
P = rP.getRandomPolygon(edgeNumber)
cD_test = cV.getConeVol(P)
point_test = [random.random(), random.random()]
norm = M.norm(point_test)
point_test = M.scaleVek(delta / norm, point_test)
print(' here is the translation')
print(point_test)
point_test = M.addVek(point_test, P[0])
# could be improved by making delta smaller if check is false ... orby setting point_test in the near of an vertex...
check = hMethodPolyFunctionalCheck(cD_test, point_test, eps, h)
if not check:
print(
' gradPolyFunctionalTest failed with polytop , point_test , value : '
)
print(P)
print(point_test)
print(cV.polyFunctional(cD_test, point_test))
n = n + 1
def transGridMid( orderedGrid , newMidPoint):
length = len(orderedGrid)
start = orderedGrid[0]
end_leftup = orderedGrid[ length -1 ]
end_rightdown = [ end_leftup[1] , start[1] ]
midOfGrid = M.getMidPoint( end_leftup , end_rightdown )
translation = M.subVek( newMidPoint , midOfGrid )
i = 0
# delete every point in orderedGrid, that is not in first (postive) quadrant. Because of 'index out of bounce' error, i and length may have to be reduced
while i < length:
orderedGrid[i] = M.addVek( orderedGrid[i] , translation )
if orderedGrid[i][0] < 0 or orderedGrid[i][1] < 0:
orderedGrid.pop(i)
i = i - 1
length = length - 1
i = i + 1
def coneVolumeDescend(cD, start):
result = start
previous = [-1, -1]
n = 0
print('gehe in while schleife ')
while (cV.phi(result, cD) > eps_descend):
if M.dist(previous, result) > 0.1:
previous[0] = result[0]
previous[1] = result[1]
d = M.scaleVek(-1, cV.gradPhiApprox(result, cD, 0.0001))
# d = M.scaleVek( 1 / M.norm(d) , d )
alpha = sS.stepSize_posGrad(cD, result, d, n)
result = M.addVek(result, M.scaleVek(alpha, d))
n = n + 1
else:
print('versuche es mit BFGS und BFGSApprox, Start bei:')
print(cV.gamma(cD, result))
result_2 = coneVolumeDescendBFGS(cD, result, previous, crease_test,
efficiency_test, beta_1test,
beta_2test)
print('BFGS ist fertig mit:')
print(cV.gamma(cD, result_2))
result_1 = coneVolumeDescendBFGSApprox(cD, result, previous,
crease_test,
efficiency_test, beta_1test,
beta_2test)
print('BFGS Approx ist fertig!')
if cV.phi(result_1, cD) < cV.phi(result_2, cD):
return result_1
else:
return result_2
return result
def coneVolumeDescendBFGS(cD, params_new, params_prev, crease, efficiency,
beta_1, beta_2):
A_k = M.idMatrix(2)
n = 0
while (cV.phi(params_new, cD) > eps_descend):
# ICH VERÄNDERE HIER MEIN CREASE...
crease = 0.000001 #cV.phi( params_new , cD) * 0.00000001
while (True):
try:
A_k = BFGS.getBFGS(cD, params_new, params_prev, A_k)
break
except ZeroDivisionError:
print('es geht nicht besser')
return params_new
break
antiGrad = M.scaleVek(-1, cV.gradPhi(params_new, cD))
d = A_k.lgsSolve(antiGrad)
d = M.scaleVek(1.0 / M.norm(d), d)
alpha = sS.stepSize_posGrad(
cD, params_new, d, n) # crease , efficiency , beta_1 , beta_2 )
d = M.scaleVek(alpha, d)
params_prev = [params_new[0], params_new[1]]
params_new = M.addVek(params_new, d)
if (M.dist(params_new, params_prev) < minimalStep_BFGS):
print(' distance is lower than minimalStep of BFGS ')
break
n = n + 1
return params_new
[-1.1761317014903958, -0.022433820601322707],
[-3.4952312190856785, -4.881491593765966],
[0.789349380758395, -2.4687243187640626]]
cD = cV.getConeVol(polygon_test2)
params_now = [4.7, 1.6152821997297826]
print(cV.gamma(cD, params_now))
print(cV.phi(params_now, cD))
d = [
-0.2083940408151545, -0.9780449497608644
] # should come from a BFGS algorithm to test the efficiency of BFGS directions
grad = M.scaleVek(1.0 / M.norm(cV.gradPhi(params_now, cD)),
cV.gradPhi(params_now, cD))
grad_approx = cV.gradPhiApprox(params_now, cD, 0.00001)
stepSize = 1
params_next = M.addVek(params_now, M.scaleVek(stepSize, d))
v.visiualizeLowValueOnGrid(0.001, 0.00001, cD, params_now, 0.02405, 0.024059,
0.02406)
v.vizNiveauGradOnGrid(0.001, 0.00001, cD, params_now, d, 0.000001)
v.vizNiveauGradOnGrid(0.001, 0.00001, cD, params_now, grad, 0.000001)
v.vizNiveauGradOnGrid(0.001, 0.00001, cD, params_now, grad_approx, 0.000001)
n = 0
diff = (1 * cV.phi(params_now, cD)) - (1 * cV.phi(params_next, cD))
d = [-1, 2]
while (diff < 0):
stepSize = stepSize * 0.5
params_next = M.addVek(params_now, M.scaleVek(stepSize, d))
diff = cV.phi(params_now, cD) - cV.phi(params_next, cD)
if (n + 1) % 10 == 0:
print(diff)
def notEfficient(cD, point, d, stepSize, crease):
v = M.addVek(point, M.scaleVek(stepSize, d))
upper_bound = cV.phi(point, cD) + crease * stepSize * M.scal(
cV.gradPhi(point, cD), d) / M.norm(d)
return cV.phi(v, cD) > upper_bound