polyListOptim)
pyOptimUtil.direct.plotDirectPolygon(polyListOptim, potentialIndex, lb - 0.5,
ub + 0.5)
## Testing the triangulation
lb = numpy.array([-2., -2.], float)
ub = numpy.array([2., 2.], float)
A, b = polyOperation.addBoxToInequalityLBUB(lb, ub)
polyObj = polyOperation.PolygonObj(optimTestFun.rosen, A, b)
polyObj.getMaxDistanceToVertices()
V, dualHull, G, h, x0 = polyOperation.constraintToVertices(A,
b,
full_output=True)
tri = scipy.spatial.Delaunay(V)
points = V
plt.triplot(points[:, 0], points[:, 1], tri.simplices.copy())
plt.plot(points[:, 0], points[:, 1], 'o')
plt.show()
for simplex in tri.simplices:
plt.plot(points[simplex, 0], points[simplex, 1], 'k-')
plt.ylim([-2.5, 2.5])
plt.xlim([-2.5, 2.5])
plt.show()
primalD = mul(matrix(primalDistance)[:, [0, 0]] ** -1, matrix(newA))
primalHull = scipy.spatial.ConvexHull(primalD)
primalPoints = numpy.array(primalD)
plt.plot(primalPoints[:, 0], primalPoints[:, 1], "o")
# plt.plot(x[0], x[1], 'ro')
for simplex in primalHull.simplices:
plt.plot(primalPoints[simplex, 0], primalPoints[simplex, 1], "k-")
plt.show()
## testing inequality <=> verticies operations
V, hull, A, b, x0 = polyOperation.constraintToVertices(G[0:5, :], h[0:5], full_output=True)
points = hull.points
plt.plot(points[:, 0], points[:, 1], "o")
plt.plot(x[0], x[1], "ro")
for simplex in hull.simplices:
plt.plot(points[simplex, 0], points[simplex, 1], "k-")
plt.show()
ATest, bTest = polyOperation.verticesToConstraint(V)
polyOperation.constraintToVertices(ATest, bTest)
polyOperation.findAnalyticCenter(ATest, bTest)
primalD = mul(matrix(primalDistance)[:, [0, 0]]**-1, matrix(newA))
primalHull = scipy.spatial.ConvexHull(primalD)
primalPoints = numpy.array(primalD)
plt.plot(primalPoints[:, 0], primalPoints[:, 1], 'o')
#plt.plot(x[0], x[1], 'ro')
for simplex in primalHull.simplices:
plt.plot(primalPoints[simplex, 0], primalPoints[simplex, 1], 'k-')
plt.show()
## testing inequality <=> verticies operations
V, hull, A, b, x0 = polyOperation.constraintToVertices(G[0:5, :],
h[0:5],
full_output=True)
points = hull.points
plt.plot(points[:, 0], points[:, 1], 'o')
plt.plot(x[0], x[1], 'ro')
for simplex in hull.simplices:
plt.plot(points[simplex, 0], points[simplex, 1], 'k-')
plt.show()
ATest, bTest = polyOperation.verticesToConstraint(V)
polyOperation.constraintToVertices(ATest, bTest)
polyOperation.findAnalyticCenter(ATest, bTest)
potentialIndex = polyOperation.identifyPotentialOptimalPolygonPareto(polyListOptim)
pyOptimUtil.direct.plotDirectPolygon(polyListOptim,potentialIndex,lb-0.5,ub+0.5)
## Testing the triangulation
lb = numpy.array([-2.,-2.],float)
ub = numpy.array([2.,2.],float)
A,b = polyOperation.addBoxToInequalityLBUB(lb,ub)
polyObj = polyOperation.PolygonObj(optimTestFun.rosen,A,b)
polyObj.getMaxDistanceToVertices()
V, dualHull, G, h, x0 = polyOperation.constraintToVertices(A,b,full_output=True)
tri = scipy.spatial.Delaunay(V)
points = V
plt.triplot(points[:,0], points[:,1], tri.simplices.copy())
plt.plot(points[:,0], points[:,1], 'o')
plt.show()
for simplex in tri.simplices:
plt.plot(points[simplex,0], points[simplex,1], 'k-')
plt.ylim([-2.5,2.5])
plt.xlim([-2.5,2.5])
plt.show()
