## another triangle
V = numpy.array([[0, 0], [0, 1], [1, 0]], float)
## Tetrahegron
V = numpy.array([[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0]], float)
## our routine
hull = scipy.spatial.ConvexHull(V)
b = numpy.zeros((len(hull.simplices[:, 0]), len(hull.points[0, :])))
for i in range(0, len(hull.simplices[:, 0])):
b[i] = numpy.mean(hull.points[hull.simplices[i, :], :], axis=0)
hull.points[hull.simplices[0, :], :]
polyOperation.verticesToConstraint(hull.points[hull.simplices[0, :], :])
polyOperation.PolygonObj(optimTestFun.rosen, None, None,
hull.points[hull.simplices[0, :], :])
polyObj = polyOperation.PolygonObj(optimTestFun.rosen, None, None, V)
## TODO: Currently facing with a rank deficiency problem where the face of a polygon is
## actually a hyperplane
lb = numpy.array([-2., -2.], float)
ub = numpy.array([2., 2.], float)
A, b = polyOperation.addBoxToInequalityLBUB(lb, ub)
V, dualHull, G, h, x0 = polyOperation.constraintToVertices(A,
b,
full_output=True)
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)
polyOperation.findAnalyticCenter(G[0:5, :], h[0:5])
hull.simplices
hull.points
## splitting
newV = V[hull.simplices[0, :], :]
newV = numpy.append(newV, numpy.reshape(numpy.array(x0), (1, 2)), axis=0)
ATest, bTest = polyOperation.verticesToConstraint(newV)
## 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)
polyOperation.findAnalyticCenter(G[0:5, :], h[0:5])
hull.simplices
hull.points
## splitting
newV = V[hull.simplices[0, :], :]
newV = numpy.append(newV, numpy.reshape(numpy.array(x0), (1, 2)), axis=0)
ATest, bTest = polyOperation.verticesToConstraint(newV)
V = numpy.array([[0,0],[0,1],[1,0]],float)
## Tetrahegron
V = numpy.array([[0,0,0],[0,0,1],[0,1,0],[1,0,0]],float)
## our routine
hull = scipy.spatial.ConvexHull(V)
b = numpy.zeros((len(hull.simplices[:,0]),len(hull.points[0,:])))
for i in range(0,len(hull.simplices[:,0])):
b[i] = numpy.mean(hull.points[hull.simplices[i,:],:],axis=0)
hull.points[hull.simplices[0,:],:]
polyOperation.verticesToConstraint(hull.points[hull.simplices[0,:],:])
polyOperation.PolygonObj(optimTestFun.rosen,None,None,hull.points[hull.simplices[0,:],:])
polyObj = polyOperation.PolygonObj(optimTestFun.rosen,None,None,V)
## TODO: Currently facing with a rank deficiency problem where the face of a polygon is
## actually a hyperplane
lb = numpy.array([-2.,-2.],float)
ub = numpy.array([2.,2.],float)
A,b = polyOperation.addBoxToInequalityLBUB(lb,ub)
V, dualHull, G, h, x0 = polyOperation.constraintToVertices(A,b,full_output=True)
V = numpy.array([[0,0],[0,1],[1,0]],float)
