def __init__(self, ts, data=0, weights=1.0):
self.ts = ts
self.weights = weights
self.data = data
self.calc = calc #DAECalc.DAECalc("__PC12_MA__","__dPC12_MA__","__d2PC12_MA__")
BaseModel.__init__(self,
len(ts) * 15, 91,
"PC12_MA") #15 of 54 dVars, 91 Parameters
self.calc.kwargs['max_steps'] = 5000
def __init__(self, N):
BaseModel.__init__(self, N, N, "%i Parameter Exponential" % N)
def __init__(self, weights):
self.weights = weights
BaseModel.__init__(self, 22, 70, "MMPrior")
def __init__(self, x0=1, weights=25.0):
self.x0 = x0
self.weights = weights
BaseModel.__init__(self, 70, 70, "LinearPrior")
def __init__(self, x0):
self.x0 = x0 # x0 contains the default values for the experiment
BaseModel.__init__(self, 91, 70, "Expt")
def __init__(self, ts, weights = 1.0):
self.ts = ts
self.weights = weights
self.calc = calc #DAECalc.DAECalc("__PC12_MA__","__dPC12_MA__","__d2PC12_MA__")
BaseModel.__init__(self,len(ts) * 54, 91, "PC12_MA") #54 (not 51) dVars, 91(21+70, not 21+64=85) Parameters
self.calc.kwargs['max_steps']=5000