def _Prepare(self):
if hasattr(self, 'prepared') and self.prepared == True:
return
NonLinProblem._Prepare(self)
self.prepared = True
if self.isFDmodel:
r = {}
for iv in self.freeVars:
if iv.domain is None: continue
ind1, ind2 = self._oovarsIndDict[iv]
assert ind2 - ind1 == 1
r[ind1] = iv.domain
self.discreteVars = r
# TODO: use something else instead of dict.keys()
new_discr = {}
for key, fv in self.discreteVars.items():
if type(fv) not in [list, tuple, ndarray
] and fv not in ('bool', bool):
self.err(
'each element from discreteVars dictionary should be list or tuple of allowed values'
)
if fv is not bool and fv is not 'bool':
new_discr[key] = sort(fv)
lowest = 0 if fv is bool or fv is 'bool' else fv[0]
biggest = 1 if fv is bool or fv is 'bool' else fv[-1]
if lowest > self.ub[key]:
self.err('variable ' + str(key) +
': smallest allowed discrete value ' + str(fv[0]) +
' exeeds imposed upper bound ' + str(self.ub[key]))
if biggest < self.lb[key]:
self.err('variable ' + str(key) +
': biggest allowed discrete value ' + str(fv[-1]) +
' is less than imposed lower bound ' +
str(self.lb[key]))
self.discreteVars.update(new_discr)
def _Prepare(self):
if hasattr(self, 'prepared') and self.prepared == True:
return
NonLinProblem._Prepare(self)
if self.isFDmodel:
r = {}
for iv in self.freeVars:
if iv.domain is None: continue
ind1, ind2 = self._oovarsIndDict[iv]
assert ind2-ind1 == 1
r[ind1] = iv.domain
self.discreteVars = r
# TODO: use something else instead of dict.keys()
for key in self.discreteVars.keys():
fv = self.discreteVars[key]
if type(fv) not in [list, tuple, ndarray] and fv not in ('bool', bool):
self.err('each element from discreteVars dictionary should be list or tuple of allowed values')
if fv is not bool and fv is not 'bool': fv = sort(fv)
lowest = 0 if fv is bool or fv is 'bool' else fv[0]
biggest = 1 if fv is bool or fv is 'bool' else fv[-1]
if lowest > self.ub[key]:
self.err('variable '+ str(key)+ ': smallest allowed discrete value ' + str(fv[0]) + ' exeeds imposed upper bound '+ str(self.ub[key]))
if biggest < self.lb[key]:
self.err('variable '+ str(key)+ ': biggest allowed discrete value ' + str(fv[-1]) + ' is less than imposed lower bound '+ str(self.lb[key]))
self.discreteVars[key] = fv
def _Prepare(self):
self.X = atleast_2d(self.X)
self.Y = array(self.Y, float)
if self.X.shape[0] != self.Y.shape[0]:
if self.X.shape[1] != self.Y.shape[0]:
self.err('incorrect shape of input data')
else:
self.X = self.X.T
NonLinProblem._Prepare(self)
#if self.Y.ndim
if self.userProvided.df:
assert len(self.user.df) == 1
self.dfff = self.user.df[0]
def dff(x):
r = zeros(self.n)
for i in range(self.Y.shape[0]):
#print asfarray(self.fff(x, self.X[i])-self.Y[i]), asfarray(self.dfff(x, self.X[i]))
r += dot(
2.0 * asfarray(self.fff(x, self.X[i]) - self.Y[i]),
asfarray(self.dfff(x, self.X[i])))
return r
self.df = self.user.df = dff
def _Prepare(self):
self.X = atleast_2d(self.X)
self.Y = array(self.Y, float)
if self.X.shape[0] != self.Y.shape[0]:
if self.X.shape[1] != self.Y.shape[0]:
self.err('incorrect shape of input data')
else:
self.X = self.X.T
NonLinProblem._Prepare(self)
#if self.Y.ndim
if self.userProvided.df:
assert len(self.user.df) == 1
self.dfff = self.user.df[0]
def dff(x):
r = zeros(self.n)
for i in range(self.Y.shape[0]):
#print asfarray(self.fff(x, self.X[i])-self.Y[i]), asfarray(self.dfff(x, self.X[i]))
r += dot(2.0 * asfarray(self.fff(x, self.X[i])-self.Y[i]), asfarray(self.dfff(x, self.X[i])))
return r
self.df = self.user.df = dff
