def wlinear_svd(X, y, w, work=None):
"""
Similar to wlinear, except that this function returns
"""
if work == None:
work = linear_workspace(X.shape[0], X.shape[1])
return _callback.gsl_multifit_wlinear(X, y, w, work._ptr)
def wlinear_svd(X, y, w, work=None):
"""
Similar to wlinear, except that this function returns
"""
if work == None:
work = linear_workspace(X.shape[0], X.shape[1])
return _callback.gsl_multifit_wlinear(X, y, w, work._ptr)
def wlinear(X, w, y, work=None):
"""
This function computes the best-fit parameters C of the model y =
X c for the observations Y and the matrix of predictor variables
X. The covariance matrix of the model parameters COV is estimated
from the weighted data. The weighted sum of squares of the
residuals from the best-fit, \chi^2, is returned in CHISQ.
The best-fit is found by singular value decomposition of the matrix
X using the preallocated workspace provided in WORK. Any
components which have zero singular value (to machine precision)
are discarded from the fit.
If work is None, the approbriate workspace will be allocated
automatically
"""
if work == None:
work = linear_workspace(X.shape[0], X.shape[1])
return _callback.gsl_multifit_wlinear(X, w, y, work._ptr)
def wlinear(X, w, y, work=None):
"""
This function computes the best-fit parameters C of the model y =
X c for the observations Y and the matrix of predictor variables
X. The covariance matrix of the model parameters COV is estimated
from the weighted data. The weighted sum of squares of the
residuals from the best-fit, \chi^2, is returned in CHISQ.
The best-fit is found by singular value decomposition of the matrix
X using the preallocated workspace provided in WORK. Any
components which have zero singular value (to machine precision)
are discarded from the fit.
If work is None, the approbriate workspace will be allocated
automatically
"""
if work == None:
work = linear_workspace(X.shape[0], X.shape[1])
return _callback.gsl_multifit_wlinear(X, w, y, work._ptr)
