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| author | Peter Carbonetto | 2017-06-04 12:06:36 -0500 |
|---|---|---|
| committer | Peter Carbonetto | 2017-06-04 12:06:36 -0500 |
| commit | c1132606169875be6d07b54b30e8ae9446341bc2 (patch) | |
| tree | 13019a8101d2278ab1a928481979cca9c7ee6009 /src/logistic.h | |
| parent | 079d7deb888936fe174746d1efd7cd7ed6a511dd (diff) | |
| download | pangemma-c1132606169875be6d07b54b30e8ae9446341bc2.tar.gz | |
Removed FORCE_FLOAT from prdt.h/prdt.cpp.
Diffstat (limited to 'src/logistic.h')
| -rw-r--r-- | src/logistic.h | 93 |
1 files changed, 47 insertions, 46 deletions
diff --git a/src/logistic.h b/src/logistic.h index a68ee09..e951935 100644 --- a/src/logistic.h +++ b/src/logistic.h @@ -1,52 +1,54 @@ #ifndef LOGISTIC_H_ /* Include guard */
#define LOGISTIC_H_
-/* Mixed interface */
-void logistic_mixed_pred(gsl_vector *beta // Vector of parameters length = 1 + Sum_k(C_k - 1) + Kc
- ,gsl_matrix_int *X //Matrix Nobs x K
- ,gsl_vector_int *nlev // Vector with number categories
- ,gsl_matrix *Xc // continuous covariates Matrix Nobs x Kc
- ,gsl_vector *yhat //Vector of prob. predicted by the logistic
- );
+// Mixed interface.
+void logistic_mixed_pred(gsl_vector *beta, // Vector of parameters
+ // length = 1+Sum_k(C_k-1)+Kc.
+ gsl_matrix_int *X, // Matrix Nobs x K.
+ gsl_vector_int *nlev, // Vector with num. categories.
+ gsl_matrix *Xc, // Continuous covariates matrix
+ // Nobs x Kc
+ gsl_vector *yhat); // Vector of prob. predicted by
+ // the logistic.
-int logistic_mixed_fit(gsl_vector *beta // Vector of parameters length = 1 + Sum_k(C_k - 1) + Kc
- ,gsl_matrix_int *X //Matrix Nobs x K
- ,gsl_vector_int *nlev // Vector with number categories
- ,gsl_matrix *Xc // continuous covariates Matrix Nobs x Kc
- ,gsl_vector *y //Vector of prob. to predict
- ,double lambdaL1 // Regularization L1 0.0 if not used
- ,double lambdaL2); // Regularization L2 0.0 if not used
+int logistic_mixed_fit(gsl_vector *beta, // Vector of parameters
+ // length = 1+Sum_k(C_k-1)+Kc
+ gsl_matrix_int *X, // Matrix Nobs x K.
+ gsl_vector_int *nlev, // Vector with number categories.
+ gsl_matrix *Xc, // Continuous covariates
+ // matrix Nobs x Kc
+ gsl_vector *y, // Vector of prob. to predict.
+ double lambdaL1, // Reg. L1 0.0 if not used.
+ double lambdaL2); // Reg. L2 0.0 if not used.
-double fLogit_mixed(gsl_vector *beta
- ,gsl_matrix_int *X
- ,gsl_vector_int *nlev
- ,gsl_matrix *Xc // continuous covariates Matrix Nobs x Kc
- ,gsl_vector *y
- ,double lambdaL1
- ,double lambdaL2);
+double fLogit_mixed(gsl_vector *beta,
+ gsl_matrix_int *X,
+ gsl_vector_int *nlev,
+ gsl_matrix *Xc, // continuous covariates matrix Nobs x Kc
+ gsl_vector *y,
+ double lambdaL1,
+ double lambdaL2);
-/* Categorical only interface */
-void logistic_cat_pred(gsl_vector *beta // Vector of parameters length = 1 + Sum_k(C_k - 1) + Kc
- ,gsl_matrix_int *X //Matrix Nobs x K
- ,gsl_vector_int *nlev // Vector with number categories
- ,gsl_vector *yhat //Vector of prob. predicted by the logistic
- );
+// Categorical-only interface.
+void logistic_cat_pred(gsl_vector *beta, // Vector of parameters length = 1 + Sum_k(C_k - 1) + Kc
+ gsl_matrix_int *X, //Matrix Nobs x K
+ gsl_vector_int *nlev, // Vector with number categories
+ gsl_vector *yhat); //Vector of prob. predicted by the logistic
-int logistic_cat_fit(gsl_vector *beta // Vector of parameters length = 1 + Sum_k(C_k - 1) + Kc
- ,gsl_matrix_int *X //Matrix Nobs x K
- ,gsl_vector_int *nlev // Vector with number categories
- ,gsl_vector *y //Vector of prob. to predict
- ,double lambdaL1 // Regularization L1 0.0 if not used
- ,double lambdaL2); // Regularization L2 0.0 if not used
-
-double fLogit_cat(gsl_vector *beta
- ,gsl_matrix_int *X
- ,gsl_vector_int *nlev
- ,gsl_vector *y
- ,double lambdaL1
- ,double lambdaL2);
+int logistic_cat_fit(gsl_vector *beta, // Vector of parameters length = 1 + Sum_k(C_k - 1) + Kc
+ gsl_matrix_int *X, //Matrix Nobs x K
+ gsl_vector_int *nlev, // Vector with number categories
+ gsl_vector *y, //Vector of prob. to predict
+ double lambdaL1, // Regularization L1 0.0 if not used
+ double lambdaL2); // Regularization L2 0.0 if not used
+double fLogit_cat(gsl_vector *beta,
+ gsl_matrix_int *X,
+ gsl_vector_int *nlev,
+ gsl_vector *y,
+ double lambdaL1,
+ double lambdaL2);
/* Continuous only interface */
void logistic_cont_pred(gsl_vector *beta // Vector of parameters length = 1 + Sum_k(C_k - 1) + Kc
@@ -60,11 +62,10 @@ int logistic_cont_fit(gsl_vector *beta // Vector of parameters length = 1 + Sum ,double lambdaL1 // Regularization L1 0.0 if not used
,double lambdaL2); // Regularization L2 0.0 if not used
-double fLogit_cont(gsl_vector *beta
- ,gsl_matrix *Xc // continuous covariates Matrix Nobs x Kc
- ,gsl_vector *y
- ,double lambdaL1
- ,double lambdaL2);
-
+double fLogit_cont(gsl_vector *beta,
+ gsl_matrix *Xc, // Continuous covariates matrix Nobs x Kc .
+ gsl_vector *y,
+ double lambdaL1,
+ double lambdaL2);
#endif // LOGISTIC_H_
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