data: :
instance of the Data class
model: :
instance of the Model class
beta0: :
a rank1 sequence of initial parameter values. Optional if
model provides an “estimate” function to estimate these values.
delta0: optional :
a (doubleprecision) float array to hold the initial values of
the errors in the input variables. Must be same shape as data.x
ifixb: optional :
sequence of integers with the same length as beta0 that determines
which parameters are held fixed. A value of 0 fixes the parameter,
a value > 0 makes the parameter free.
ifixx: optional :
an array of integers with the same shape as data.x that determines
which input observations are treated as fixed. One can use a sequence
of length m (the dimensionality of the input observations) to fix some
dimensions for all observations. A value of 0 fixes the observation,
a value > 0 makes it free.
job: optional :
an integer telling ODRPACK what tasks to perform. See p. 31 of the
ODRPACK User’s Guide if you absolutely must set the value here. Use the
method set_job postinitialization for a more readable interface.
iprint: optional :
an integer telling ODRPACK what to print. See pp. 3334 of the
ODRPACK User’s Guide if you absolutely must set the value here. Use the
method set_iprint postinitialization for a more readable interface.
errfile: optional :
string with the filename to print ODRPACK errors to. Do Not Open
This File Yourself!
rptfile: optional :
string with the filename to print ODRPACK summaries to. Do Not
Open This File Yourself!
ndigit: optional :
integer specifying the number of reliable digits in the computation
of the function.
taufac: optional :
float specifying the initial trust region. The default value is 1.
The initial trust region is equal to taufac times the length of the
first computed GaussNewton step. taufac must be less than 1.
sstol: optional :
float specifying the tolerance for convergence based on the relative
change in the sumofsquares. The default value is eps**(1/2) where eps
is the smallest value such that 1 + eps > 1 for double precision
computation on the machine. sstol must be less than 1.
partol: optional :
float specifying the tolerance for convergence based on the relative
change in the estimated parameters. The default value is eps**(2/3) for
explicit models and eps**(1/3) for implicit models. partol must be less
than 1.
maxit: optional :
integer specifying the maximum number of iterations to perform. For
first runs, maxit is the total number of iterations performed and
defaults to 50. For restarts, maxit is the number of additional
iterations to perform and defaults to 10.
stpb: optional :
sequence (len(stpb) == len(beta0)) of relative step sizes to compute
finite difference derivatives wrt the parameters.
stpd: optional :
array (stpd.shape == data.x.shape or stpd.shape == (m,)) of relative
step sizes to compute finite difference derivatives wrt the input
variable errors. If stpd is a rank1 array with length m (the
dimensionality of the input variable), then the values are broadcast to
all observations.
sclb: optional :
sequence (len(stpb) == len(beta0)) of scaling factors for the
parameters. The purpose of these scaling factors are to scale all of
the parameters to around unity. Normally appropriate scaling factors
are computed if this argument is not specified. Specify them yourself
if the automatic procedure goes awry.
scld: optional :
array (scld.shape == data.x.shape or scld.shape == (m,)) of scaling
factors for the errors in the input variables. Again, these factors
are automatically computed if you do not provide them. If scld.shape ==
(m,), then the scaling factors are broadcast to all observations.
work: optional :
array to hold the doublevalued working data for ODRPACK. When
restarting, takes the value of self.output.work.
iwork: optional :
array to hold the integervalued working data for ODRPACK. When
restarting, takes the value of self.output.iwork.
output: :
an instance if the Output class containing all of the returned
data from an invocation of ODR.run() or ODR.restart()
