def divideGivenPolygon(func, polyObj):
'''
Divide a polygon object given objective function
Parameters
----------
func: callable
objective function
polyObj: :class:`PolygonObj`
the polygon which we want to split
Returns
-------
list:
the set of new polygons as well as the origin
'''
# TODO: think about the case where we only divide a single
# dimension.
# each polyObj carries it's own little polygon
# defined by the inequalities
A, b = polyObj.getInequality()
polyList = list()
###
#
# HERE, we divide the polygon into little triangles
#
###
# We obtain our convex hull and the points at the vertices
V, dualHull, G, h, x0 = constraintToVertices(A,
b,
x0=None,
full_output=True)
# and the convex hull in the primal space
hull = scipy.spatial.ConvexHull(V, False)
# random information
numFace = len(b)
numSimplices = len(hull.simplices[:, 0])
numDimension = len(V[0, :])
numVertices = len(V[:, 0])
###
#
# Find the center simplex
#
###
# we are going to find the distance between the vertices and the
# hyperplanes defined by the inequalities
dToPlane = distanceToPlane(V, A, b)
#distanceToPlane = abs(A.dot(V.T) - numpy.reshape(b,(numFace,1)))
indexList = numpy.linspace(0, numVertices - 1, numVertices).astype(int)
# holder for the new vertices
X = numpy.zeros((numFace, numDimension))
for i in range(0, numFace):
# building our new triangle/tetrahedron
# find points not on the plane, subject to machine precision
onPlane = abs(dToPlane[i]) <= 1e-8
indexOnPlane = indexList[onPlane]
newV = V[indexOnPlane, :]
# take the average
X[i] = numpy.mean(newV, axis=0)
# we are going to create our new simplex that sits in the middle
# of the original simplex
newPolyObj = PolygonObj(func, None, None, X)
centerA, centerB = newPolyObj.getInequality()
###
#
# Find the corner simplex
#
###
# we need to find out the closest plane from the middle simplex to each vertex
# and also the planes that connects to the vertex
## getting the transpose because we want to go down the list of vertices
centerDToPlane = distanceToPlane(X, centerA, centerB)
# the number of faces doesn't change for a n+1-simplex
centerX = numpy.zeros((numFace, numDimension))
verticesList = list()
for i in range(0, numFace):
# building our new triangle/tetrahedron
# find points not on the plane, subject to machine precision
onPlane = abs(centerDToPlane[i]) <= 1e-8
indexOnPlane = indexList[onPlane]
newV = X[indexOnPlane, :]
# take the average
centerX[i] = numpy.mean(newV, axis=0)
verticesList.append(indexOnPlane)
# the distance from the middle of the hyperplane that defines the center
# simplex and the hyperplanes. only used for identifying which point
# sits on which plane
centerDToCenterPlane = (centerA.dot(centerX.T) - centerB).T
# create the set of index for our hyperplanes
planeIndexList = numpy.linspace(0, numFace - 1, numFace).astype(int)
# holders
simplexFromCenter = numpy.zeros((numFace, numDimension))
for i in range(0, numVertices):
# going to find the planes where vertex i sits on
planeMeetVertices = abs(dToPlane.T[i]) <= 1e-8
# then the index of those planes
planesMeetVertex = planeIndexList[planeMeetVertices]
Atemp = A[planesMeetVertex, :]
btemp = b[planesMeetVertex]
# then the new plane that completes the convex hull/simplex
# centerPlaneIndex = numpy.argmin(dVToS[i])
# print "Vertex " +str(i)
# print Atemp
# print btemp
# Atemp = numpy.append(Atemp,numpy.reshape(centerA[centerPlaneIndex],(1,numDimension)),axis=0)
# btemp = numpy.append(btemp,centerB[centerPlaneIndex])
# the set of distance between vertex i and the points lying on the hyperplane
# of the center simplex
dVToCenterX = V[i] - centerX
# index with the minimum distance
index = numpy.argmin(numpy.linalg.norm(dVToCenterX, axis=1))
# index of center simplex that completes the corner simplex
index = numpy.argmin(abs(centerDToCenterPlane[index]))
# print "Location of V[i]"
# print V[i]
# print "dVToCenterX"
# print dVToCenterX
# print "Actual distance from vertices to the center simplex mid point"
# print numpy.linalg.norm(dVToCenterX,axis=1)
# print "index of center simplex that completes the corner simplex"
# print index
# print "and the location of that is "
# print centerX[index]
# print "vertices of center simplex"
# print X[verticesList[index]]
XTemp = numpy.append(X[verticesList[index]],
numpy.reshape(V[i], (1, numDimension)),
axis=0)
# Atemp = numpy.append(Atemp,numpy.reshape(centerA[index],(1,numDimension)),axis=0)
# btemp = numpy.append(btemp,centerB[index])
# print V[i]
# print planeMeetVertices
# print A
# print b
# add the object to the list
# print centerPlaneIndex
polyList.append(PolygonObj(func, None, None, XTemp))
# find the location for simplex i
simplexFromCenter[i] = polyList[i].getLocation()
###
#
# Consolidate information
#
###
# add in the original and declare it has already been split
#originalPolyObj = polyObj.splitThis()
#polyList.append(copy.deepcopy(originalPolyObj))
# this should be a deepcopy because we remove the original
# object later using their hash
#TODO: add in closest polygon
#polyObj = polyObj.splitThis(polyList[1::])
newPolyObj = newPolyObj.splitThis(polyList)
y, r, j = closestVector(simplexFromCenter, newPolyObj.getGrad())
polyList[j].setDirectionFromParent()
polyList.append(newPolyObj)
polyList.append(copy.deepcopy(polyObj.splitThis()))
return polyList
def divideGivenPolygon(func,polyObj):
'''
Divide a polygon object given objective function
Parameters
----------
func: callable
objective function
polyObj: :class:`PolygonObj`
the polygon which we want to split
Returns
-------
list:
the set of new polygons as well as the origin
'''
# TODO: think about the case where we only divide a single
# dimension.
# each polyObj carries it's own little polygon
# defined by the inequalities
A,b = polyObj.getInequality()
polyList = list()
###
#
# HERE, we divide the polygon into little triangles
#
###
# We obtain our convex hull and the points at the vertices
V, dualHull, G, h, x0 = constraintToVertices(A,b,x0=None,full_output=True)
# and the convex hull in the primal space
hull = scipy.spatial.ConvexHull(V,False)
# random information
numFace = len(b)
numSimplices = len(hull.simplices[:,0])
numDimension = len(V[0,:])
numVertices = len(V[:,0])
###
#
# Find the center simplex
#
###
# we are going to find the distance between the vertices and the
# hyperplanes defined by the inequalities
dToPlane = distanceToPlane(V,A,b)
#distanceToPlane = abs(A.dot(V.T) - numpy.reshape(b,(numFace,1)))
indexList = numpy.linspace(0,numVertices-1,numVertices).astype(int)
# holder for the new vertices
X = numpy.zeros((numFace,numDimension))
for i in range(0,numFace):
# building our new triangle/tetrahedron
# find points not on the plane, subject to machine precision
onPlane = abs(dToPlane[i])<=1e-8
indexOnPlane = indexList[onPlane]
newV = V[indexOnPlane,:]
# take the average
X[i] = numpy.mean(newV,axis=0)
# we are going to create our new simplex that sits in the middle
# of the original simplex
newPolyObj = PolygonObj(func,None,None,X)
centerA,centerB = newPolyObj.getInequality()
###
#
# Find the corner simplex
#
###
# we need to find out the closest plane from the middle simplex to each vertex
# and also the planes that connects to the vertex
## getting the transpose because we want to go down the list of vertices
centerDToPlane = distanceToPlane(X,centerA,centerB)
# the number of faces doesn't change for a n+1-simplex
centerX = numpy.zeros((numFace,numDimension))
verticesList = list()
for i in range(0,numFace):
# building our new triangle/tetrahedron
# find points not on the plane, subject to machine precision
onPlane = abs(centerDToPlane[i])<=1e-8
indexOnPlane = indexList[onPlane]
newV = X[indexOnPlane,:]
# take the average
centerX[i] = numpy.mean(newV,axis=0)
verticesList.append(indexOnPlane)
# the distance from the middle of the hyperplane that defines the center
# simplex and the hyperplanes. only used for identifying which point
# sits on which plane
centerDToCenterPlane = (centerA.dot(centerX.T) - centerB).T
# create the set of index for our hyperplanes
planeIndexList = numpy.linspace(0,numFace-1,numFace).astype(int)
# holders
simplexFromCenter = numpy.zeros((numFace,numDimension))
for i in range(0,numVertices):
# going to find the planes where vertex i sits on
planeMeetVertices = abs(dToPlane.T[i])<=1e-8
# then the index of those planes
planesMeetVertex = planeIndexList[planeMeetVertices]
Atemp = A[planesMeetVertex,:]
btemp = b[planesMeetVertex]
# then the new plane that completes the convex hull/simplex
# centerPlaneIndex = numpy.argmin(dVToS[i])
# print "Vertex " +str(i)
# print Atemp
# print btemp
# Atemp = numpy.append(Atemp,numpy.reshape(centerA[centerPlaneIndex],(1,numDimension)),axis=0)
# btemp = numpy.append(btemp,centerB[centerPlaneIndex])
# the set of distance between vertex i and the points lying on the hyperplane
# of the center simplex
dVToCenterX = V[i] - centerX
# index with the minimum distance
index = numpy.argmin(numpy.linalg.norm(dVToCenterX,axis=1))
# index of center simplex that completes the corner simplex
index = numpy.argmin(abs(centerDToCenterPlane[index]))
# print "Location of V[i]"
# print V[i]
# print "dVToCenterX"
# print dVToCenterX
# print "Actual distance from vertices to the center simplex mid point"
# print numpy.linalg.norm(dVToCenterX,axis=1)
# print "index of center simplex that completes the corner simplex"
# print index
# print "and the location of that is "
# print centerX[index]
# print "vertices of center simplex"
# print X[verticesList[index]]
XTemp = numpy.append(X[verticesList[index]],numpy.reshape(V[i],(1,numDimension)),axis=0)
# Atemp = numpy.append(Atemp,numpy.reshape(centerA[index],(1,numDimension)),axis=0)
# btemp = numpy.append(btemp,centerB[index])
# print V[i]
# print planeMeetVertices
# print A
# print b
# add the object to the list
# print centerPlaneIndex
polyList.append(PolygonObj(func,None,None,XTemp))
# find the location for simplex i
simplexFromCenter[i] = polyList[i].getLocation()
###
#
# Consolidate information
#
###
# add in the original and declare it has already been split
#originalPolyObj = polyObj.splitThis()
#polyList.append(copy.deepcopy(originalPolyObj))
# this should be a deepcopy because we remove the original
# object later using their hash
#TODO: add in closest polygon
#polyObj = polyObj.splitThis(polyList[1::])
newPolyObj = newPolyObj.splitThis(polyList)
y,r,j = closestVector(simplexFromCenter,newPolyObj.getGrad())
polyList[j].setDirectionFromParent()
polyList.append(newPolyObj)
polyList.append(copy.deepcopy(polyObj.splitThis()))
return polyList
def triangulatePolygon(func, polyObj):
'''
Triangulate a polygon object given objective function
Parameters
----------
func: callable
objective function
polyObj: :class:`PolygonObj`
the polygon which we want to split
Returns
-------
list:
the set of new simplex as well as the origin
'''
# TODO: think about the case where we only divide a single
# dimension.
# each polyObj carries it's own little polygon
# defined by the inequalities
A, b = polyObj.getInequality()
polyList = list()
###
#
# HERE, we divide the polygon into little triangles
#
###
# We obtain our convex hull and the points at the vertices
V, dualHull, G, h, x0 = constraintToVertices(A,
b,
x0=None,
full_output=True)
# and the convex hull in the primal space
# print "Reached Here"
# t0 = time.time()
hull = scipy.spatial.ConvexHull(V, False)
# t1 = time.time()
# print t1 - t0
numFace = len(b)
numSimplices = len(hull.simplices[:, 0])
numDimension = len(V[0, :])
numVertices = len(V[:, 0])
# now we split it up given the center
# we are going to find the distance between the vertices and the
# hyperplanes defined by the inequalities
dToPlane = distanceToPlane(V, A, b)
#distanceToPlane = abs(A.dot(V.T) - numpy.reshape(b,(numFace,1)))
indexList = numpy.linspace(0, numVertices - 1, numVertices).astype(int)
# print indexList
X = numpy.zeros((numFace, numDimension))
fx = numpy.zeros(numFace)
for i in range(0, numFace):
# print "face number " +str(i)
# building our new triangle/tetrahedron
# print distanceToPlane[i]
# find points not on the plane, subject to machine precision
# notOnPlane = abs(dToPlane[i])>=1e-8
onPlane = abs(dToPlane[i]) <= 1e-8
# print "\n Face number " +str(i)
# print notOnPlane
#indexNotOnPlane = indexList[notOnPlane==False]
indexOnPlane = indexList[onPlane]
#print indexList[indexNotOnPlane]
newV = V[indexList[indexOnPlane], :]
newV = numpy.append(newV,
numpy.reshape(numpy.array(x0), (1, numDimension)),
axis=0)
# newA,newb = verticesToConstraint(newV)
# print "Object " +str(i)
# print newV
# add our new object
# polyList.append(copy.deepcopy(PolygonObj(func,newA,newb)))
polyList.append(copy.deepcopy(PolygonObj(func, None, None, newV)))
X[i] = polyList[i].getLocation()
fx[i] = polyList[i].getFx()
#TODO: add in closest polygon
newPolyObj = polyObj.splitThis(polyList)
y, r, j = closestVector(X, newPolyObj.getGrad())
polyList[j].setDirectionFromParent()
# add in the original and declare it has already been split
polyList.append(copy.deepcopy(newPolyObj))
return polyList
def triangulatePolygon(func,polyObj):
'''
Triangulate a polygon object given objective function
Parameters
----------
func: callable
objective function
polyObj: :class:`PolygonObj`
the polygon which we want to split
Returns
-------
list:
the set of new simplex as well as the origin
'''
# TODO: think about the case where we only divide a single
# dimension.
# each polyObj carries it's own little polygon
# defined by the inequalities
A,b = polyObj.getInequality()
polyList = list()
###
#
# HERE, we divide the polygon into little triangles
#
###
# We obtain our convex hull and the points at the vertices
V, dualHull, G, h, x0 = constraintToVertices(A,b,x0=None,full_output=True)
# and the convex hull in the primal space
# print "Reached Here"
# t0 = time.time()
hull = scipy.spatial.ConvexHull(V,False)
# t1 = time.time()
# print t1 - t0
numFace = len(b)
numSimplices = len(hull.simplices[:,0])
numDimension = len(V[0,:])
numVertices = len(V[:,0])
# now we split it up given the center
# we are going to find the distance between the vertices and the
# hyperplanes defined by the inequalities
dToPlane = distanceToPlane(V,A,b)
#distanceToPlane = abs(A.dot(V.T) - numpy.reshape(b,(numFace,1)))
indexList = numpy.linspace(0,numVertices-1,numVertices).astype(int)
# print indexList
X = numpy.zeros((numFace,numDimension))
fx = numpy.zeros(numFace)
for i in range(0,numFace):
# print "face number " +str(i)
# building our new triangle/tetrahedron
# print distanceToPlane[i]
# find points not on the plane, subject to machine precision
# notOnPlane = abs(dToPlane[i])>=1e-8
onPlane = abs(dToPlane[i])<=1e-8
# print "\n Face number " +str(i)
# print notOnPlane
#indexNotOnPlane = indexList[notOnPlane==False]
indexOnPlane = indexList[onPlane]
#print indexList[indexNotOnPlane]
newV = V[indexList[indexOnPlane],:]
newV = numpy.append(newV,numpy.reshape(numpy.array(x0),(1,numDimension)),axis=0)
# newA,newb = verticesToConstraint(newV)
# print "Object " +str(i)
# print newV
# add our new object
# polyList.append(copy.deepcopy(PolygonObj(func,newA,newb)))
polyList.append(copy.deepcopy(PolygonObj(func,None,None,newV)))
X[i] = polyList[i].getLocation()
fx[i] = polyList[i].getFx()
#TODO: add in closest polygon
newPolyObj = polyObj.splitThis(polyList)
y,r,j = closestVector(X,newPolyObj.getGrad())
polyList[j].setDirectionFromParent()
# add in the original and declare it has already been split
polyList.append(copy.deepcopy(newPolyObj))
return polyList
