chemometrics.mcr.McrAR¶
Multivariate Curve Resolution  Alternating Regression
Multivariate curve resolution factorizes data such that the factors
maximize the explained data. Additional constraints are given for the
scores and loadings, such as nonnegativeness, baseline restrictions,
unimodality, etc. Due to its origin in the analysis of chemical
information, a different nomenclature is widely used in literature and also
in this implementation. Scores are interpreted as concentration matrices
C_
, loadings are the transposed spectral matrices ST_
.
 param c_regr
Instantiated regression class (or string, see Notes) for calculating the C matrix
 type c_regr
str, class
 param st_regr
Instantiated regression class (or string, see Notes) for calculating the S^T matrix
 type st_regr
str, class
 param fit_kwargs
kwargs sent to fit and fit_transform methods
 type fit_kwargs
dict
 param c_fit_kwargs
kwargs sent to c_regr.fit method
 type c_fit_kwargs
dict
 param st_fit_kwargs
kwargs sent to``st_regr.fit`` method
 type st_fit_kwargs
dict
 param c_constraints
List of constraints applied to calculation of C matrix
 type c_constraints
list
 param st_constraints
List of constraints applied to calculation of S^T matrix
 type st_constraints
list
 param max_iter
Maximum number of iterations. One iteration calculates both C and S^T
 type max_iter
int
 param err_fcn
Function to calculate error/differences after each least squares calculation (ie twice per iteration). Outputs to err attribute.
 type err_fcn
function
 param tol_increase
Factor increase to allow in err attribute. Set to 0 for no increase allowed. E.g., setting to 1.0 means the err can double per iteration.
 type tol_increase
float
 param tol_n_increase
Number of consecutive iterations for which the err attribute can increase
 type tol_n_increase
int
 param tol_err_change
If err changes less than tol_err_change, per iteration, break.
 type tol_err_change
float
 param tol_n_above_min
Number of halfiterations that can be performed without reaching a new errorminimum
 type tol_n_above_min
int
 chemometrics.mcr.McrAR.err¶
List of calculated errors (from err_fcn) after each least squares (ie twice per iteration)
 Type
list
 chemometrics.mcr.McrAR.C_¶
Most recently calculated C matrix (that did not cause a tolerance failure)
 Type
ndarray [n_samples, n_targets]
 chemometrics.mcr.McrAR.ST_¶
Most recently calculated S^T matrix (that did not cause a tolerance failure)
 Type
ndarray [n_targets, n_features]
 chemometrics.mcr.McrAR.components_¶
Synonym for
ST_
, providing sklearn like compatibility Type
ndarray [n_targets, n_features]
 chemometrics.mcr.McrAR.C_opt_¶
[Optimal] C matrix for lowest err attribute
 Type
ndarray [n_samples, n_targets]
 chemometrics.mcr.McrAR.ST_opt_¶
[Optimal] ST matrix for lowest err attribute
 Type
ndarray [n_targets, n_features]
 chemometrics.mcr.McrAR.n_iter¶
Total number of iterations performed
 Type
int
 chemometrics.mcr.McrAR.n_features¶
Total number of features, e.g. spectral frequencies.
 Type
int
 chemometrics.mcr.McrAR.n_samples¶
Total number of samples (e.g., pixels)
 Type
int
 chemometrics.mcr.McrAR.n_targets¶
Total number of targets (e.g., pure analytes)
 Type
int
 chemometrics.mcr.McrAR.n_iter_opt¶
Iteration when optimal C and ST calculated
 Type
int
 chemometrics.mcr.McrAR.exit_max_iter_reached¶
Exited iterations due to maximum number of iteration reached (max_iter parameter)
 Type
bool
 chemometrics.mcr.McrAR.exit_tol_increase¶
Exited iterations due to maximum fractional increase in error metric (via err_fcn)
 Type
bool
 chemometrics.mcr.McrAR.exit_tol_n_increase¶
Exited iterations due to maximum number of consecutive increases in error metric (via err fcn)
 Type
bool
 chemometrics.mcr.McrAR.exit_tol_err_change¶
Exited iterations due to error metric change that is smaller than tol_err_change
 Type
bool
 chemometrics.mcr.McrAR.exit_tol_n_above_min¶
Exited iterations due to maximum number of halfiterations for which the error metric increased above the minimum error
 Type
bool
Notes
Builtin regressor classes (str can be used): OLS (ordinary least squares), NNLS (nonnegatively constrained least squares). See mcr.regressors.
Builtin regressor methods can be given as a string to
c_regr
,st_regr
; though instantiating an imported class gives more flexibility.Setting any tolerance to None turns that check off
Module Attributes
noindex: 
