def __init__(self, clpModel, bucketSize=1):
self.dim = clpModel.nConstraints + clpModel.nVariables
self.clpModel = clpModel
# Require extra check after leaving variable is chosen
clpModel.useCustomPrimal(True)
#self.banList = np.array([])
self.orgBan = np.array(self.dim * [True], np.bool)
self.notBanned = self.orgBan.copy()
self.complementarityList = np.arange(self.dim)
#Positive-edge-related attributes
self.isDegenerate = False
# Create some numpy arrays here ONCE to prevent memory
# allocation at each iteration
self.aColumn = CyCoinIndexedVector()
self.aColumn.reserve(self.dim)
self.w = CyCoinIndexedVector()
self.w.reserve(self.clpModel.nRows)
self.rhs = np.empty(self.clpModel.nRows, dtype=np.double)
self.EPSILON = 10**-7
self.lastUpdateIteration = 0
self.compCount = 0
self.nonCompCount = 0
self.compRej = 0
self.numberOfIncompSinceLastUpdate = 0
self.last_p_count = 0
self.iCounter = 0
self.iInterval = 100
def __init__(self, clpModel, bucketSize=1):
self.dim = clpModel.nConstraints + clpModel.nVariables
self.clpModel = clpModel
# Require extra check after leaving variable is chosen
clpModel.useCustomPrimal(True)
#self.banList = np.array([])
self.orgBan = np.array(self.dim * [True], np.bool)
self.notBanned = self.orgBan.copy()
self.complementarityList = np.arange(self.dim)
#Positive-edge-related attributes
self.isDegenerate = False
# Create some numpy arrays here ONCE to prevent memory
# allocation at each iteration
self.aColumn = CyCoinIndexedVector()
self.aColumn.reserve(self.dim)
self.w = CyCoinIndexedVector()
self.w.reserve(self.clpModel.nRows)
self.rhs = np.empty(self.clpModel.nRows, dtype=np.double)
self.EPSILON = 10**-7
self.lastUpdateIteration = 0
self.compCount = 0
self.nonCompCount = 0
self.compRej = 0
self.numberOfIncompSinceLastUpdate = 0
self.last_p_count = 0
self.iCounter = 0
self.iInterval = 100
def __init__(self, clpModel, EPSILON=10 ** (-7)):
self.clpModel = clpModel
self.dim = self.clpModel.nRows + self.clpModel.nCols
self.isDegenerate = False
# Create some numpy arrays here ONCE to prevent memory
# allocation at each iteration
self.aColumn = CyCoinIndexedVector()
self.aColumn.reserve(self.dim)
self.w = CyCoinIndexedVector()
self.w.reserve(self.clpModel.nRows)
self.rhs = np.empty(self.clpModel.nRows, dtype=np.double)
self.EPSILON = EPSILON
self.lastUpdateIteration = 0
def __init__(self, clpModel, EPSILON=10 ** (-7)):
self.clpModel = clpModel
self.dim = self.clpModel.nRows + self.clpModel.nCols
self.isDegenerate = False
# Create some numpy arrays here ONCE to prevent memory
# allocation at each iteration
self.aColumn = CyCoinIndexedVector()
self.aColumn.reserve(self.dim)
self.w = CyCoinIndexedVector()
self.w.reserve(self.clpModel.nRows)
self.rhs = np.empty(self.clpModel.nRows, dtype=np.double)
self.EPSILON = EPSILON
self.lastUpdateIteration = 0
class WolfePivotPE(PivotPythonBase):
def __init__(self, clpModel, bucketSize=1):
self.dim = clpModel.nConstraints + clpModel.nVariables
self.clpModel = clpModel
# Require extra check after leaving variable is chosen
clpModel.useCustomPrimal(True)
#self.banList = np.array([])
self.orgBan = np.array(self.dim * [True], np.bool)
self.notBanned = self.orgBan.copy()
self.complementarityList = np.arange(self.dim)
#Positive-edge-related attributes
self.isDegenerate = False
# Create some numpy arrays here ONCE to prevent memory
# allocation at each iteration
self.aColumn = CyCoinIndexedVector()
self.aColumn.reserve(self.dim)
self.w = CyCoinIndexedVector()
self.w.reserve(self.clpModel.nRows)
self.rhs = np.empty(self.clpModel.nRows, dtype=np.double)
self.EPSILON = 10**-7
self.lastUpdateIteration = 0
self.compCount = 0
self.nonCompCount = 0
self.compRej = 0
self.numberOfIncompSinceLastUpdate = 0
self.last_p_count = 0
self.iCounter = 0
self.iInterval = 100
# Begining of Positive-Edge-related attributes
def updateP(self):
'''Finds constraints with abs(rhs) <= epsilon and put
their indices in "z"
'''
s = self.clpModel
nRows = s.nRows
rhs = self.rhs
s.getRightHandSide(rhs)
#self.p = np.where(np.abs(rhs) > self.EPSILON)[0]
self.z = np.where(np.abs(rhs) <= self.EPSILON)[0]
print 'degeneracy level : ', (len(self.z)) / float(nRows)
#self.isDegenerate = (len(self.p) != nRows)
self.isDegenerate = (len(self.z) > 0)
def updateW(self):
'''Sets "w" to be a vector of random vars with "0"
at indices defined in "p"
Note that vectorTimesB_1 changes "w"
'''
self.updateP()
self.w.clear()
self.w[self.z] = np.random.random(len(self.z))
s = self.clpModel
s.vectorTimesB_1(self.w)
self.lastUpdateIteration = s.iteration
def random(self):
'Defines how random vector "w" components are generated'
return random.random()
def isCompatible(self, varInd):
if not self.isDegenerate:
return False
s = self.clpModel
s.getACol(varInd, self.aColumn)
return abs(cydot(self.aColumn, self.w)) < self.EPSILON
# End of Positive-Edge-related attributes
def pivotColumn(self, updates, spareRow1, spareRow2, spareCol1, spareCol2):
self.updateReducedCosts(updates, spareRow1, spareRow2, spareCol1, spareCol2)
s = self.clpModel
self.CompIter = True
rc = s.reducedCosts
#tol = s.dualTolerance
tol = 0
indicesToConsider = np.where(s.varNotFlagged & s.varNotFixed &
s.varNotBasic &
(((rc > tol) & s.varIsAtUpperBound) |
((rc < -tol) & s.varIsAtLowerBound) |
s.varIsFree) &
self.notBanned)[0]
rc2 = abs(rc[indicesToConsider])
checkFree = False
maxRc = maxCompRc = maxInd = maxCompInd = -1
if self.isDegenerate:
w = self.w.elements
compatibility = np.zeros(s.nCols + s.nRows, dtype=np.double)
if len(indicesToConsider) > 0:
s.transposeTimesSubsetAll(indicesToConsider,
w, compatibility)
comp_varInds = indicesToConsider[np.where(abs(
compatibility[indicesToConsider]) <
self.EPSILON)[0]]
comp_rc = abs(rc[comp_varInds])
if len(comp_rc) > 0:
if True:
s = self.clpModel
cl = self.complementarityList
maxCompRc = 0
count = 0
#randinds = np.random.randint(len(comp_varInds), size=10)
nn = 15
# for i in xrange(random.randint(0,
# len(comp_varInds)),
# min(nn, len(comp_varInds))):
for i in xrange(min(nn, len(comp_varInds))):
#for i in randinds:
ind = comp_varInds[i]
if (s.CLP_getVarStatus(cl[ind]) != 1 and
comp_rc[i] > maxCompRc):
maxCompInd = ind
maxCompRc = rc[maxCompInd]
else:
maxCompInd = comp_varInds[np.argmax(comp_rc)]
maxCompRc = rc[maxCompInd]
del compatibility
del comp_rc
del comp_varInds
if len(rc2) > 0:
maxInd = indicesToConsider[np.argmax(rc2)]
maxRc = rc[maxInd]
del rc2
if maxCompInd != -1 and abs(maxCompRc) > 0.00001 * abs(maxRc):
self.compCount += 1
#print s.getVarNameByIndex(maxCompInd)
return maxCompInd
if self.iCounter % self.iInterval == 0:
rhs = self.rhs
s.getRightHandSide(rhs)
p_count = len(np.where(np.abs(rhs) > self.EPSILON)[0])
if abs(p_count - self.last_p_count) > 10:
#print s.iteration
self.iCounter = 0
self.updateW()
self.last_p_count = p_count
self.iInterval = max(50, self.iInterval - 50)
else:
self.iInterval = min(300, self.iInterval + 50)
self.iCounter += 1
else:
self.iCounter += 1
self.nonCompCount += 1
self.CompIter = False
#if self.numberOfIncompSinceLastUpdate > 5:
# self.updateW()
# self.numberOfIncompSinceLastUpdate = 0
#else:
# self.numberOfIncompSinceLastUpdate += 1
#
# for i in xrange(s.nConstraints):
# print s.getVarNameByIndex(s.getPivotVariable()[i]),
# print
return maxInd
#
#
# if rc2.shape[0] > 0:
# if checkFree:
# w = np.where(s.varIsFree)[0]
# if w.shape[0] > 0:
# ind = s.argWeightedMax(rc2, indicesToConsider, 1, w)
# else:
# ind = np.argmax(rc2)
# else:
# ind = np.argmax(rc2)
# #print 'incomming var: %d' % indicesToConsider[ind]
# ret = indicesToConsider[ind]
# del indicesToConsider # not sure if this is necessary
# del rc2 # HUGE memory leak otherwise
# return ret
# return -1
# return self.pivotColumnFirst()
def saveWeights(self, model, mode):
self.clpModel = model
def isPivotAcceptable(self):
#import pdb; pdb.set_trace()
# return True
# #TODO ComplementarityList can be defined in the current class
s = self.clpModel
#cl = s.getComplementarityList()
cl = self.complementarityList
pivotRow = s.pivotRow()
if pivotRow < 0:
colInd = s.sequenceIn()
#print 'entering: ', colInd, ' comp: ', cl[colInd]
return 1
pivotVariable = s.getPivotVariable()
leavingVarIndex = pivotVariable[pivotRow]
colInd = s.sequenceIn()
# print 'Basis:'
# print s.getPivotVariable()
# print 'leave: ', leavingVarIndex
# print 'entering: ', colInd, ' comp: ', cl[colInd]
if s.CLP_getVarStatus(cl[colInd]) == 1 and \
cl[colInd] != leavingVarIndex:
#self.banList = np.concatenate((self.banList, [colInd]))
self.notBanned[colInd] = False
#s.setFlagged(colInd)
if self.CompIter:
self.compRej += 1
return 0
#self.banList = np.zeros(self.dim, np.int)
del self.notBanned
self.notBanned = self.orgBan.copy()
#self.notBanned = np.array(self.dim * [True])
return 1
def setComplement(self, model, v1, v2):
v1n = v1.name
v2n = v2.name
listv1 = np.array(model.inds.varIndex[v1n])[v1.indices]
listv2 = np.array(model.inds.varIndex[v2n])[v2.indices]
for i, j in izip(listv1, listv2):
(self.complementarityList[i], self.complementarityList[j]) = \
(self.complementarityList[j], self.complementarityList[i])
class WolfePivotPE(PivotPythonBase):
def __init__(self, clpModel, bucketSize=1):
self.dim = clpModel.nConstraints + clpModel.nVariables
self.clpModel = clpModel
# Require extra check after leaving variable is chosen
clpModel.useCustomPrimal(True)
#self.banList = np.array([])
self.orgBan = np.array(self.dim * [True], np.bool)
self.notBanned = self.orgBan.copy()
self.complementarityList = np.arange(self.dim)
#Positive-edge-related attributes
self.isDegenerate = False
# Create some numpy arrays here ONCE to prevent memory
# allocation at each iteration
self.aColumn = CyCoinIndexedVector()
self.aColumn.reserve(self.dim)
self.w = CyCoinIndexedVector()
self.w.reserve(self.clpModel.nRows)
self.rhs = np.empty(self.clpModel.nRows, dtype=np.double)
self.EPSILON = 10**-7
self.lastUpdateIteration = 0
self.compCount = 0
self.nonCompCount = 0
self.compRej = 0
self.numberOfIncompSinceLastUpdate = 0
self.last_p_count = 0
self.iCounter = 0
self.iInterval = 100
# Begining of Positive-Edge-related attributes
def updateP(self):
'''Finds constraints with abs(rhs) <= epsilon and put
their indices in "z"
'''
s = self.clpModel
nRows = s.nRows
rhs = self.rhs
s.getRightHandSide(rhs)
#self.p = np.where(np.abs(rhs) > self.EPSILON)[0]
self.z = np.where(np.abs(rhs) <= self.EPSILON)[0]
print 'degeneracy level : ', (len(self.z)) / float(nRows)
#self.isDegenerate = (len(self.p) != nRows)
self.isDegenerate = (len(self.z) > 0)
def updateW(self):
'''Sets "w" to be a vector of random vars with "0"
at indices defined in "p"
Note that vectorTimesB_1 changes "w"
'''
self.updateP()
self.w.clear()
self.w[self.z] = np.random.random(len(self.z))
s = self.clpModel
s.vectorTimesB_1(self.w)
self.lastUpdateIteration = s.iteration
def random(self):
'Defines how random vector "w" components are generated'
return random.random()
def isCompatible(self, varInd):
if not self.isDegenerate:
return False
s = self.clpModel
s.getACol(varInd, self.aColumn)
return abs(cydot(self.aColumn, self.w)) < self.EPSILON
# End of Positive-Edge-related attributes
def pivotColumn(self, updates, spareRow1, spareRow2, spareCol1, spareCol2):
self.updateReducedCosts(updates, spareRow1, spareRow2, spareCol1,
spareCol2)
s = self.clpModel
self.CompIter = True
rc = s.reducedCosts
#tol = s.dualTolerance
tol = 0
indicesToConsider = np.where(s.varNotFlagged & s.varNotFixed
& s.varNotBasic
& (((rc > tol) & s.varIsAtUpperBound) | (
(rc < -tol) & s.varIsAtLowerBound)
| s.varIsFree) & self.notBanned)[0]
rc2 = abs(rc[indicesToConsider])
checkFree = False
maxRc = maxCompRc = maxInd = maxCompInd = -1
if self.isDegenerate:
w = self.w.elements
compatibility = np.zeros(s.nCols + s.nRows, dtype=np.double)
if len(indicesToConsider) > 0:
s.transposeTimesSubsetAll(indicesToConsider, w, compatibility)
comp_varInds = indicesToConsider[np.where(
abs(compatibility[indicesToConsider]) < self.EPSILON)[0]]
comp_rc = abs(rc[comp_varInds])
if len(comp_rc) > 0:
if True:
s = self.clpModel
cl = self.complementarityList
maxCompRc = 0
count = 0
#randinds = np.random.randint(len(comp_varInds), size=10)
nn = 15
# for i in xrange(random.randint(0,
# len(comp_varInds)),
# min(nn, len(comp_varInds))):
for i in xrange(min(nn, len(comp_varInds))):
#for i in randinds:
ind = comp_varInds[i]
if (s.CLP_getVarStatus(cl[ind]) != 1
and comp_rc[i] > maxCompRc):
maxCompInd = ind
maxCompRc = rc[maxCompInd]
else:
maxCompInd = comp_varInds[np.argmax(comp_rc)]
maxCompRc = rc[maxCompInd]
del compatibility
del comp_rc
del comp_varInds
if len(rc2) > 0:
maxInd = indicesToConsider[np.argmax(rc2)]
maxRc = rc[maxInd]
del rc2
if maxCompInd != -1 and abs(maxCompRc) > 0.00001 * abs(maxRc):
self.compCount += 1
#print s.getVarNameByIndex(maxCompInd)
return maxCompInd
if self.iCounter % self.iInterval == 0:
rhs = self.rhs
s.getRightHandSide(rhs)
p_count = len(np.where(np.abs(rhs) > self.EPSILON)[0])
if abs(p_count - self.last_p_count) > 10:
#print s.iteration
self.iCounter = 0
self.updateW()
self.last_p_count = p_count
self.iInterval = max(50, self.iInterval - 50)
else:
self.iInterval = min(300, self.iInterval + 50)
self.iCounter += 1
else:
self.iCounter += 1
self.nonCompCount += 1
self.CompIter = False
#if self.numberOfIncompSinceLastUpdate > 5:
# self.updateW()
# self.numberOfIncompSinceLastUpdate = 0
#else:
# self.numberOfIncompSinceLastUpdate += 1
#
# for i in xrange(s.nConstraints):
# print s.getVarNameByIndex(s.getPivotVariable()[i]),
# print
return maxInd
#
#
# if rc2.shape[0] > 0:
# if checkFree:
# w = np.where(s.varIsFree)[0]
# if w.shape[0] > 0:
# ind = s.argWeightedMax(rc2, indicesToConsider, 1, w)
# else:
# ind = np.argmax(rc2)
# else:
# ind = np.argmax(rc2)
# #print 'incomming var: %d' % indicesToConsider[ind]
# ret = indicesToConsider[ind]
# del indicesToConsider # not sure if this is necessary
# del rc2 # HUGE memory leak otherwise
# return ret
# return -1
# return self.pivotColumnFirst()
def saveWeights(self, model, mode):
self.clpModel = model
def isPivotAcceptable(self):
#import pdb; pdb.set_trace()
# return True
# #TODO ComplementarityList can be defined in the current class
s = self.clpModel
#cl = s.getComplementarityList()
cl = self.complementarityList
pivotRow = s.pivotRow()
if pivotRow < 0:
colInd = s.sequenceIn()
#print 'entering: ', colInd, ' comp: ', cl[colInd]
return 1
pivotVariable = s.getPivotVariable()
leavingVarIndex = pivotVariable[pivotRow]
colInd = s.sequenceIn()
# print 'Basis:'
# print s.getPivotVariable()
# print 'leave: ', leavingVarIndex
# print 'entering: ', colInd, ' comp: ', cl[colInd]
if s.CLP_getVarStatus(cl[colInd]) == 1 and \
cl[colInd] != leavingVarIndex:
#self.banList = np.concatenate((self.banList, [colInd]))
self.notBanned[colInd] = False
#s.setFlagged(colInd)
if self.CompIter:
self.compRej += 1
return 0
#self.banList = np.zeros(self.dim, np.int)
del self.notBanned
self.notBanned = self.orgBan.copy()
#self.notBanned = np.array(self.dim * [True])
return 1
def setComplement(self, model, v1, v2):
v1n = v1.name
v2n = v2.name
listv1 = np.array(model.inds.varIndex[v1n])[v1.indices]
listv2 = np.array(model.inds.varIndex[v2n])[v2.indices]
for i, j in izip(listv1, listv2):
(self.complementarityList[i], self.complementarityList[j]) = \
(self.complementarityList[j], self.complementarityList[i])
class PositiveEdgePivot(PivotPythonBase):
'''
Positive Edge pivot rule implementation.
.. _custom-pivot-usage:
**Usage**
>>> from cylp.cy import CyClpSimplex
>>> from cylp.py.pivots import PositiveEdgePivot
>>> from cylp.py.pivots.PositiveEdgePivot import getMpsExample
>>> # Get the path to a sample mps file
>>> f = getMpsExample()
>>> s = CyClpSimplex()
>>> s.readMps(f) # Returns 0 if OK
0
>>> pivot = PositiveEdgePivot(s)
>>> s.setPivotMethod(pivot)
>>> s.primal()
'optimal'
>>> round(s.objectiveValue, 5)
2520.57174
'''
def __init__(self, clpModel, EPSILON=10 ** (-7)):
self.clpModel = clpModel
self.dim = self.clpModel.nRows + self.clpModel.nCols
self.isDegenerate = False
# Create some numpy arrays here ONCE to prevent memory
# allocation at each iteration
self.aColumn = CyCoinIndexedVector()
self.aColumn.reserve(self.dim)
self.w = CyCoinIndexedVector()
self.w.reserve(self.clpModel.nRows)
self.rhs = np.empty(self.clpModel.nRows, dtype=np.double)
self.EPSILON = EPSILON
self.lastUpdateIteration = 0
def updateP(self):
'''Finds constraints with abs(rhs) <= epsilon and put
their indices in "z"
'''
s = self.clpModel
nRows = s.nRows
rhs = self.rhs
s.getRightHandSide(rhs)
#self.p = np.where(np.abs(rhs) > self.EPSILON)[0]
self.z = np.where(np.abs(rhs) <= self.EPSILON)[0]
#print('degeneracy level : ', (len(self.z)) / float(nRows)))
self.isDegenerate = (len(self.z) > 0)
def updateW(self):
'''Sets "w" to be a vector of random vars with "0"
at indices defined in "p"
Note that vectorTimesB_1 changes "w"
'''
self.updateP()
self.w.clear()
self.w[self.z] = np.random.random(len(self.z))
s = self.clpModel
s.vectorTimesB_1(self.w)
self.lastUpdateIteration = s.iteration
def random(self):
'Defines how random vector "w" components are generated'
return random.random()
def isCompatible(self, varInd):
if not self.isDegenerate:
return False
s = self.clpModel
s.getACol(varInd, self.aColumn)
return abs(cydot(self.aColumn, self.w)) < self.EPSILON
def checkVar(self, i):
return self.isCompatible(i)
def pivotColumn(self, updates, spareRow1, spareRow2, spareCol1, spareCol2):
self.updateReducedCosts(updates, spareRow1, spareRow2, spareCol1, spareCol2)
s = self.clpModel
rc = s.reducedCosts
tol = s.dualTolerance
indicesToConsider = np.where(s.varNotFlagged & s.varNotFixed &
s.varNotBasic &
(((rc > tol) & s.varIsAtUpperBound) |
((rc < -tol) & s.varIsAtLowerBound) |
s.varIsFree))[0]
rc2 = abs(rc[indicesToConsider])
maxRc = maxCompRc = maxInd = maxCompInd = -1
if self.isDegenerate:
w = self.w.elements
compatibility = np.zeros(s.nCols + s.nRows, dtype=np.double)
if len(indicesToConsider) > 0:
s.transposeTimesSubsetAll(indicesToConsider.astype(np.int64),
w, compatibility)
comp_varInds = indicesToConsider[np.where(abs(
compatibility[indicesToConsider]) <
self.EPSILON)[0]]
comp_rc = abs(rc[comp_varInds])
if len(comp_rc) > 0:
maxCompInd = comp_varInds[np.argmax(comp_rc)]
maxCompRc = rc[maxCompInd]
if len(rc2) > 0:
maxInd = indicesToConsider[np.argmax(rc2)]
maxRc = rc[maxInd]
del rc2
if maxCompInd != -1 and abs(maxCompRc) > 0.1 * abs(maxRc):
return maxCompInd
self.updateW()
return maxInd
def saveWeights(self, model, mode):
self.clpModel = model
def isPivotAcceptable(self):
return True
class PositiveEdgePivot(PivotPythonBase):
'''
Positive Edge pivot rule implementation.
.. _custom-pivot-usage:
**Usage**
>>> from cylp.cy import CyClpSimplex
>>> from cylp.py.pivots import PositiveEdgePivot
>>> from cylp.py.pivots.PositiveEdgePivot import getMpsExample
>>> # Get the path to a sample mps file
>>> f = getMpsExample()
>>> s = CyClpSimplex()
>>> s.readMps(f) # Returns 0 if OK
0
>>> pivot = PositiveEdgePivot(s)
>>> s.setPivotMethod(pivot)
>>> s.primal()
'optimal'
>>> round(s.objectiveValue, 5)
2520.57174
'''
def __init__(self, clpModel, EPSILON=10 ** (-7)):
self.clpModel = clpModel
self.dim = self.clpModel.nRows + self.clpModel.nCols
self.isDegenerate = False
# Create some numpy arrays here ONCE to prevent memory
# allocation at each iteration
self.aColumn = CyCoinIndexedVector()
self.aColumn.reserve(self.dim)
self.w = CyCoinIndexedVector()
self.w.reserve(self.clpModel.nRows)
self.rhs = np.empty(self.clpModel.nRows, dtype=np.double)
self.EPSILON = EPSILON
self.lastUpdateIteration = 0
def updateP(self):
'''Finds constraints with abs(rhs) <= epsilon and put
their indices in "z"
'''
s = self.clpModel
nRows = s.nRows
rhs = self.rhs
s.getRightHandSide(rhs)
#self.p = np.where(np.abs(rhs) > self.EPSILON)[0]
self.z = np.where(np.abs(rhs) <= self.EPSILON)[0]
#print 'degeneracy level : ', (len(self.z)) / float(nRows)
self.isDegenerate = (len(self.z) > 0)
def updateW(self):
'''Sets "w" to be a vector of random vars with "0"
at indices defined in "p"
Note that vectorTimesB_1 changes "w"
'''
self.updateP()
self.w.clear()
self.w[self.z] = np.random.random(len(self.z))
s = self.clpModel
s.vectorTimesB_1(self.w)
self.lastUpdateIteration = s.iteration
def random(self):
'Defines how random vector "w" components are generated'
return random.random()
def isCompatible(self, varInd):
if not self.isDegenerate:
return False
s = self.clpModel
s.getACol(varInd, self.aColumn)
return abs(cydot(self.aColumn, self.w)) < self.EPSILON
def checkVar(self, i):
return self.isCompatible(i)
def pivotColumn(self, updates, spareRow1, spareRow2, spareCol1, spareCol2):
self.updateReducedCosts(updates, spareRow1, spareRow2, spareCol1, spareCol2)
s = self.clpModel
rc = s.reducedCosts
tol = s.dualTolerance
indicesToConsider = np.where(s.varNotFlagged & s.varNotFixed &
s.varNotBasic &
(((rc > tol) & s.varIsAtUpperBound) |
((rc < -tol) & s.varIsAtLowerBound) |
s.varIsFree))[0]
rc2 = abs(rc[indicesToConsider])
maxRc = maxCompRc = maxInd = maxCompInd = -1
if self.isDegenerate:
w = self.w.elements
compatibility = np.zeros(s.nCols + s.nRows, dtype=np.double)
if len(indicesToConsider) > 0:
s.transposeTimesSubsetAll(indicesToConsider.astype(np.int64),
w, compatibility)
comp_varInds = indicesToConsider[np.where(abs(
compatibility[indicesToConsider]) <
self.EPSILON)[0]]
comp_rc = abs(rc[comp_varInds])
if len(comp_rc) > 0:
maxCompInd = comp_varInds[np.argmax(comp_rc)]
maxCompRc = rc[maxCompInd]
if len(rc2) > 0:
maxInd = indicesToConsider[np.argmax(rc2)]
maxRc = rc[maxInd]
del rc2
if maxCompInd != -1 and abs(maxCompRc) > 0.1 * abs(maxRc):
return maxCompInd
self.updateW()
return maxInd
def saveWeights(self, model, mode):
self.clpModel = model
def isPivotAcceptable(self):
return True
def setUp(self):
self.c = CyCoinIndexedVector()
self.c.reserve(5)
class TestCyCoinIndexedVector(unittest.TestCase):
def setUp(self):
self.c = CyCoinIndexedVector()
self.c.reserve(5)
def test_basic(self):
c = self.c
c[1] = 3.1
c[4] = -2
self.assertTrue((abs(c.elements - np.array([0., 3.1, 0., 0., -2.])) <
10 ** -8).all())
self.assertTrue((c.indices - np.array([1,4]) < 10 ** -8).all())
self.assertTrue(c.nElements == 2)
def test_number_number(self):
self.c.clear()
self.c[2] = -1.2
self.assertEqual(self.c[1], 0)
self.assertEqual(self.c[2], -1.2)
self.c.clear()
self.assertEqual(self.c[2], 0)
self.c[2] = 1.5
self.assertEqual(self.c[2], 1.5)
def test_slice_list_number(self):
l = [1, 3, 4]
self.c[l] = 5.5
self.assertEqual(self.c[1], 5.5)
self.assertEqual(self.c[3], 5.5)
self.assertEqual(self.c[4], 5.5)
def test_slice_list_list(self):
l = [1, 3, 4]
val = [1.1, 2.2, 3.3]
self.c[l] = val
self.assertEqual(self.c[1], 1.1)
self.assertEqual(self.c[3], 2.2)
self.assertEqual(self.c[4], 3.3)
def test_slice_list_array(self):
l = [1, 3, 4]
val = np.array([1.1, 2.2, 3.3])
self.c[l] = val
self.assertEqual(self.c[1], 1.1)
self.assertEqual(self.c[3], 2.2)
self.assertEqual(self.c[4], 3.3)
def test_slice_array_number(self):
l = np.array([1, 3, 4])
val = 5.5
self.c[l] = val
self.assertEqual(self.c[1], 5.5)
self.assertEqual(self.c[3], 5.5)
self.assertEqual(self.c[4], 5.5)
def test_slice_array_list(self):
l = np.array([1, 3, 4])
val = [1.1, 2.2, 3.3]
self.c[l] = val
self.assertEqual(self.c[1], 1.1)
self.assertEqual(self.c[3], 2.2)
self.assertEqual(self.c[4], 3.3)
def test_slice_array_array(self):
l = np.array([1, 3, 4])
val = np.array([1.1, 2.2, 3.3])
self.c[l] = val
self.assertEqual(self.c[1], 1.1)
self.assertEqual(self.c[3], 2.2)
self.assertEqual(self.c[4], 3.3)
def test_reAssign(self):
c = self.c
c[3] = 5
l = [1, 3, 4]
c[l] = [1, 1, 1]
self.assertEqual(self.c[0], 0)
self.assertEqual(self.c[1], 1)
self.assertEqual(self.c[3], 1)
def setUp(self):
self.c = CyCoinIndexedVector()
self.c.reserve(5)
class TestCyCoinIndexedVector(unittest.TestCase):
def setUp(self):
self.c = CyCoinIndexedVector()
self.c.reserve(5)
def test_basic(self):
c = self.c
c[1] = 3.1
c[4] = -2
self.assertTrue((abs(c.elements - np.array([0., 3.1, 0., 0., -2.])) <
10**-8).all())
self.assertTrue((c.indices - np.array([1, 4]) < 10**-8).all())
self.assertTrue(c.nElements == 2)
def test_number_number(self):
self.c.clear()
self.c[2] = -1.2
self.assertEqual(self.c[1], 0)
self.assertEqual(self.c[2], -1.2)
self.c.clear()
self.assertEqual(self.c[2], 0)
self.c[2] = 1.5
self.assertEqual(self.c[2], 1.5)
def test_slice_list_number(self):
l = [1, 3, 4]
self.c[l] = 5.5
self.assertEqual(self.c[1], 5.5)
self.assertEqual(self.c[3], 5.5)
self.assertEqual(self.c[4], 5.5)
def test_slice_list_list(self):
l = [1, 3, 4]
val = [1.1, 2.2, 3.3]
self.c[l] = val
self.assertEqual(self.c[1], 1.1)
self.assertEqual(self.c[3], 2.2)
self.assertEqual(self.c[4], 3.3)
def test_slice_list_array(self):
l = [1, 3, 4]
val = np.array([1.1, 2.2, 3.3])
self.c[l] = val
self.assertEqual(self.c[1], 1.1)
self.assertEqual(self.c[3], 2.2)
self.assertEqual(self.c[4], 3.3)
def test_slice_array_number(self):
l = np.array([1, 3, 4])
val = 5.5
self.c[l] = val
self.assertEqual(self.c[1], 5.5)
self.assertEqual(self.c[3], 5.5)
self.assertEqual(self.c[4], 5.5)
def test_slice_array_list(self):
l = np.array([1, 3, 4])
val = [1.1, 2.2, 3.3]
self.c[l] = val
self.assertEqual(self.c[1], 1.1)
self.assertEqual(self.c[3], 2.2)
self.assertEqual(self.c[4], 3.3)
def test_slice_array_array(self):
l = np.array([1, 3, 4])
val = np.array([1.1, 2.2, 3.3])
self.c[l] = val
self.assertEqual(self.c[1], 1.1)
self.assertEqual(self.c[3], 2.2)
self.assertEqual(self.c[4], 3.3)
def test_reAssign(self):
c = self.c
c[3] = 5
l = [1, 3, 4]
c[l] = [1, 1, 1]
self.assertEqual(self.c[0], 0)
self.assertEqual(self.c[1], 1)
self.assertEqual(self.c[3], 1)
