parameters: (self,x,u)
return_statement: return dx
docstring: Returns the estimate A*x+B*u+c
docstring_summary: Returns the estimate A*x+B*u+c
docstring_tokens: Returns the estimate A * x + B * u + c
function: def getOutput(self,x,u):
          """Returns the estimate A*x+B*u+c"""
          assert(len(x)==self.m)
          assert(len(u)==self.n)
          if isinstance(x,np.ndarray): x = x.tolist()
          if isinstance(u,np.ndarray): u = u.tolist()
          dx = []
          if self.coeffPattern == [None,None,None]:
              xuc = np.array(x + u + [1.0])
              for e in self.estimators:
                  dx.append(np.dot(e.x,xuc))
          else:
              for i,e in enumerate(self.estimators):
                  (xuc,constOffset) = self._toEstimator(i,x,u)
                  dx.append(np.dot(e.x,xuc)+constOffset)
          return dx
function_tokens: def getOutput ( self , x , u ) : assert ( len ( x ) == self . m ) assert ( len ( u ) == self . n ) if isinstance ( x , np . ndarray ) : x = x . tolist ( ) if isinstance ( u , np . ndarray ) : u = u . tolist ( ) dx = [ ] if self . coeffPattern == [ None , None , None ] : xuc = np . array ( x + u + [ 1.0 ] ) for e in self . estimators : dx . append ( np . dot ( e . x , xuc ) ) else : for i , e in enumerate ( self . estimators ) : ( xuc , constOffset ) = self . _toEstimator ( i , x , u ) dx . append ( np . dot ( e . x , xuc ) + constOffset ) return dx