def __init__(self, parent, function, **kwargs):
if isinstance(function, list):
from printing import repr_polynomial
self.repr = lambda: repr_polynomial(function, parent.variable_name())
kwargs['parenthesis_level'] = 0
LazyApproximation.__init__(self, parent, function, **kwargs)
if self._length >= 0:
self._expected_degree = self._length - 1
else:
self._expected_degree = Infinity
def __init__(self, parent, function, starting_workprec=None, **kwargs):
if isinstance(function, list):
nrows = parent.nrows()
ncols = parent.ncols()
if len(function) > nrows*ncols:
raise ValueError("list too long")
coeffs = function + ([0] * (nrows*ncols-len(function)))
coeffs = [ coeffs[i*ncols:(i+1)*ncols] for i in range(nrows) ]
from printing import repr_matrix
self.repr = lambda: repr_matrix(coeffs)
LazyApproximation.__init__(self, parent, function, starting_workprec, **kwargs)
def _pow_(self,exp,*args,**kwargs):
res = LazyApproximation._pow_(self,exp,*args,**kwargs)
res._expected_degree = self._expected_degree * exp
return res
def _mul_(self,other,*args,**kwargs):
res = LazyApproximation._mul_(self,other,*args,**kwargs)
res._expected_degree = self._expected_degree + other._expected_degree
return res
def _lmul_(self,*args,**kwargs):
res = LazyApproximation._lmul_(self,*args,**kwargs)
res._expected_degree = self._expected_degree
return res
def _sub_(self,other,*args,**kwargs):
res = LazyApproximation._sub_(self,other,*args,**kwargs)
res._expected_degree = max(self._expected_degree, other._expected_degree)
return res
def __init__(self, *args, **kwargs):
LazyApproximation.__init__(self, *args, **kwargs)
self._length = -1
if not hasattr(self, 'expected_valuation'):
self.expected_valuation = 0