def __init__(self, *args, **kwargs):
# TODO: add the field to ALL classes
self.err = ooerr
self.warn = oowarn
self.info = ooinfo
self.hint = oohint
self.pWarn = ooPWarn
self.disp = oodisp
self.data4TextOutput = ['objFunVal', 'log10(maxResidual)']
self.nEvals = {}
if hasattr(self, 'expectedArgs'):
if len(self.expectedArgs) < len(args):
self.err('Too much arguments for ' + self.probType + ': ' +
str(len(args)) + ' are got, at most ' +
str(len(self.expectedArgs)) + ' were expected')
for i, arg in enumerate(args):
setattr(self, self.expectedArgs[i], arg)
self.norm = norm
self.denyingStopFuncs = denyingStopFuncs()
self.iterfcn = lambda *args, **kwargs: ooIter(
self, *args, **kwargs
) # this parameter is only for OpenOpt developers, not common users
self.graphics = Graphics()
self.user = user()
self.F = lambda x: self.objFuncMultiple2Single(self.objFunc(
x)) # TODO: should be changes for LP, MILP, QP classes!
self.point = lambda *args, **kwargs: Point(self, *args, **kwargs)
self.timeElapsedForPlotting = [0.]
self.cpuTimeElapsedForPlotting = [0.]
#user can redirect these ones, as well as debugmsg
self.debugmsg = lambda msg: oodebugmsg(self, msg)
self.constraints = [] # used in isFDmodel
self.callback = [] # user-defined callback function(s)
self.solverParams = autocreate()
self.userProvided = autocreate()
self.special = autocreate()
self.intVars = [] # for problems like MILP
self.binVars = [] # for problems like MILP
self.optionalData = [
] #string names of optional data like 'c', 'h', 'Aeq' etc
if self.allowedGoals is not None: # None in EIG
if 'min' in self.allowedGoals:
self.minimize = lambda *args, **kwargs: minimize(
self, *args, **kwargs)
if 'max' in self.allowedGoals:
self.maximize = lambda *args, **kwargs: maximize(
self, *args, **kwargs)
assignScript(self, kwargs)
def __init__(self, *args, **kwargs):
NonLinProblem.__init__(self, *args, **kwargs)
assignScript(self, kwargs)
self.fff = self.f
def ff(x):
r = []
for i in range(self.Y.shape[0]):
r.append(asfarray(self.fff(x, self.X[i])-self.Y[i])**2)
return r
self.f = ff
def __init__(self, *args, **kwargs):
# TODO: add the field to ALL classes
self.err = ooerr
self.warn = oowarn
self.info = ooinfo
self.hint = oohint
self.pWarn = ooPWarn
self.disp = oodisp
self.data4TextOutput = ['objFunVal', 'log10(maxResidual)']
self.nEvals = {}
if hasattr(self, 'expectedArgs'):
if len(self.expectedArgs)<len(args):
self.err('Too much arguments for '+self.probType +': '+ str(len(args)) +' are got, at most '+ str(len(self.expectedArgs)) + ' were expected')
for i, arg in enumerate(args):
setattr(self, self.expectedArgs[i], arg)
self.norm = norm
self.denyingStopFuncs = denyingStopFuncs()
self.iterfcn = lambda *args, **kwargs: ooIter(self, *args, **kwargs)# this parameter is only for OpenOpt developers, not common users
self.graphics = Graphics()
self.user = user()
self.F = lambda x: self.objFuncMultiple2Single(self.objFunc(x)) # TODO: should be changes for LP, MILP, QP classes!
self.point = lambda *args, **kwargs: Point(self, *args, **kwargs)
self.timeElapsedForPlotting = [0.]
self.cpuTimeElapsedForPlotting = [0.]
#user can redirect these ones, as well as debugmsg
self.debugmsg = lambda msg: oodebugmsg(self, msg)
self.constraints = [] # used in isFDmodel
self.callback = [] # user-defined callback function(s)
self.solverParams = autocreate()
self.userProvided = autocreate()
self.special = autocreate()
self.intVars = [] # for problems like MILP
self.binVars = [] # for problems like MILP
self.optionalData = []#string names of optional data like 'c', 'h', 'Aeq' etc
if self.allowedGoals is not None: # None in EIG
if 'min' in self.allowedGoals:
self.minimize = lambda *args, **kwargs: minimize(self, *args, **kwargs)
if 'max' in self.allowedGoals:
self.maximize = lambda *args, **kwargs: maximize(self, *args, **kwargs)
assignScript(self, kwargs)
def llavp_init(prob, kwargs):
prob.probType = 'LLAVP'
prob.goal = 'minimum'
prob.allowedGoals = ['minimum', 'min']
prob.showGoal = False
kwargs['C'] = asfarray(kwargs['C'])
prob.n = kwargs['C'].shape[1]
prob.lb = -inf * ones(prob.n)
prob.ub = inf * ones(prob.n)
if 'damp' not in kwargs.keys(): kwargs['damp'] = None
if 'X' not in kwargs.keys(): kwargs['X'] = nan*ones(prob.n)
if prob.x0 is None: prob.x0 = zeros(prob.n)
return assignScript(prob, kwargs)
def llavp_init(prob, kwargs):
prob.probType = 'LLAVP'
prob.goal = 'minimum'
prob.allowedGoals = ['minimum', 'min']
prob.showGoal = False
kwargs['C'] = asfarray(kwargs['C'])
prob.n = kwargs['C'].shape[1]
prob.lb = -inf * ones(prob.n)
prob.ub = inf * ones(prob.n)
if 'damp' not in kwargs.keys(): kwargs['damp'] = None
if 'X' not in kwargs.keys(): kwargs['X'] = nan * ones(prob.n)
if prob.x0 is None: prob.x0 = zeros(prob.n)
return assignScript(prob, kwargs)
