Some three compile time fixes for the GNU GCC 7.1.0 compiler on Linux.

This patch is a mess because we use different line endings. I propose
to convert to standard Unix mode (as was the original GEMMA code).

To convert with vim one can use

  set fileformat=unix

Do not merge this pull request, we can handle te fixes later.

1 files changed, 724 insertions, 725 deletions

diff --git a/src/logistic.cpp b/src/logistic.cpp
index c1ddac1..f9edc68 100644
--- a/src/logistic.cpp
+++ b/src/logistic.cpp
@@ -1,725 +1,724 @@
-#include <stdio.h>

-#include <math.h>

-#include <gsl/gsl_matrix.h>

-#include <gsl/gsl_rng.h>

-#include <gsl/gsl_multimin.h>

-#include <gsl/gsl_sf.h>

-#include <gsl/gsl_linalg.h>

-#include "logistic.h"

-

-// I need to bundle all the data that goes to the function to optimze

-// together.

-typedef struct{

-  gsl_matrix_int *X;

-  gsl_vector_int *nlev;

-  gsl_vector *y;

-  gsl_matrix *Xc; // Continuous covariates matrix Nobs x Kc (NULL if not used).

-  double lambdaL1;

-  double lambdaL2;

-} fix_parm_mixed_T;

-

-double fLogit_mixed(gsl_vector *beta,

-		    gsl_matrix_int *X,

-		    gsl_vector_int *nlev,

-		    gsl_matrix *Xc,

-		    gsl_vector *y,

-		    double lambdaL1,

-		    double lambdaL2) {

-  int n = y->size; 

-  int npar = beta->size; 

-  double total = 0;

-  double aux = 0;

-  

-  // Changed loop start at 1 instead of 0 to avoid regularization of

-  // beta_0*\/

-  // #pragma omp parallel for reduction (+:total)

-  for(int i = 1; i < npar; ++i)

-    total += beta->data[i]*beta->data[i];

-  total = (-total*lambdaL2/2);

-  // #pragma omp parallel for reduction (+:aux)

-  for(int i = 1; i < npar; ++i)

-    aux += (beta->data[i]>0 ? beta->data[i] : -beta->data[i]);

-  total = total-aux*lambdaL1;

-  // #pragma omp parallel for schedule(static) shared(n,beta,X,nlev,y)

-  // #reduction (+:total)

-  for(int i = 0; i < n; ++i) {

-    double Xbetai=beta->data[0];

-    int iParm=1;

-    for(int k = 0; k < X->size2; ++k) {

-      if(gsl_matrix_int_get(X,i,k)>0)

-	Xbetai+=beta->data[gsl_matrix_int_get(X,i,k)-1+iParm];

-      iParm+=nlev->data[k]-1;

-    }

-    for(int k = 0; k < (Xc->size2); ++k) 

-      Xbetai+= gsl_matrix_get(Xc,i,k)*beta->data[iParm++];

-    total += y->data[i]*Xbetai-gsl_sf_log_1plusx(gsl_sf_exp(Xbetai));

-  }

-  return -total;

-} 

-

-void logistic_mixed_pred(gsl_vector *beta,     // Vector of parameters

-					       // length = 1 + Sum_k(C_k -1)

-			 gsl_matrix_int *X,    // Matrix Nobs x K 

-			 gsl_vector_int *nlev, // Vector with number categories

-			 gsl_matrix *Xc,       // Continuous covariates matrix:

-			                       // obs x Kc (NULL if not used).

-			 gsl_vector *yhat){    // Vector of prob. predicted by

-					       // the logistic

-  for(int i = 0; i < X->size1; ++i) {

-    double Xbetai=beta->data[0];

-    int iParm=1;

-    for(int k = 0; k < X->size2; ++k) {

-      if(gsl_matrix_int_get(X,i,k)>0)

-	Xbetai+=beta->data[gsl_matrix_int_get(X,i,k)-1+iParm];

-      iParm+=nlev->data[k]-1;

-    }

-    // Adding the continuous.

-    for(int k = 0; k < (Xc->size2); ++k) 

-      Xbetai+= gsl_matrix_get(Xc,i,k)*beta->data[iParm++];

-    yhat->data[i]=1/(1 + gsl_sf_exp(-Xbetai));

-  }

-}

-

-

-// The gradient of f, df = (df/dx, df/dy).

-void wgsl_mixed_optim_df (const gsl_vector *beta, void *params, 

-			  gsl_vector *out) {

-  fix_parm_mixed_T *p = (fix_parm_mixed_T *)params;

-  int n = p->y->size; 

-  int K = p->X->size2; 

-  int Kc = p->Xc->size2; 

-  int npar = beta->size;

-  

-  // Intitialize gradient out necessary?

-  for(int i = 0; i < npar; ++i) 

-    out->data[i]= 0;

-  

-  // Changed loop start at 1 instead of 0 to avoid regularization of beta 0.

-  for(int i = 1; i < npar; ++i)

-    out->data[i]= p->lambdaL2*beta->data[i]; 

-  for(int i = 1; i < npar; ++i)

-    out->data[i]+= p->lambdaL1*((beta->data[i]>0)-(beta->data[i]<0));

-  

-  for(int i = 0; i < n; ++i) {

-    double pn=0;

-    double Xbetai=beta->data[0];

-    int iParm=1;

-    for(int k = 0; k < K; ++k) {

-      if(gsl_matrix_int_get(p->X,i,k)>0)

-	Xbetai+=beta->data[gsl_matrix_int_get(p->X,i,k)-1+iParm];

-      iParm+=p->nlev->data[k]-1;

-    }

-    

-    // Adding the continuous.

-    for(int k = 0; k < Kc; ++k) 

-      Xbetai+= gsl_matrix_get(p->Xc,i,k)*beta->data[iParm++];

-

-    pn= -( p->y->data[i] - 1/(1 + gsl_sf_exp(-Xbetai)) );

-

-    out->data[0]+= pn;

-    iParm=1;

-    for(int k = 0; k < K; ++k) {

-      if(gsl_matrix_int_get(p->X,i,k)>0)

-	out->data[gsl_matrix_int_get(p->X,i,k)-1+iParm]+=pn;

-      iParm+=p->nlev->data[k]-1;

-    }

-    

-    // Adding the continuous.

-    for(int k = 0; k < Kc; ++k) {

-      out->data[iParm++] += gsl_matrix_get(p->Xc,i,k)*pn;

-    }

-  }

-

-}

-

-// The Hessian of f.

-void  wgsl_mixed_optim_hessian (const gsl_vector *beta, void *params, 

-				gsl_matrix *out) {

-  fix_parm_mixed_T *p = (fix_parm_mixed_T *)params;

-  int n = p->y->size; 

-  int K = p->X->size2; 

-  int Kc = p->Xc->size2; 

-  int npar = beta->size; 

-  gsl_vector *gn = gsl_vector_alloc(npar); // gn

-  

-  // Intitialize Hessian out necessary ???

-  gsl_matrix_set_zero(out);

-  

-  /* Changed loop start at 1 instead of 0 to avoid regularization of beta 0*/

-  for(int i = 1; i < npar; ++i)

-    gsl_matrix_set(out,i,i,(p->lambdaL2)); // Double-check this.

-  

-  // L1 penalty not working yet, as not differentiable, I may need to

-  // do coordinate descent (as in glm_net)

-  for(int i = 0; i < n; ++i) {

-    double pn=0;

-    double aux=0;

-    double Xbetai=beta->data[0];

-    int iParm1=1;

-    for(int k = 0; k < K; ++k) {

-      if(gsl_matrix_int_get(p->X,i,k)>0)

-	Xbetai+=beta->data[gsl_matrix_int_get(p->X,i,k)-1+iParm1];

-      iParm1+=p->nlev->data[k]-1;  //-1?

-    }

-    

-    // Adding the continuous.

-    for(int k = 0; k < Kc; ++k) 

-      Xbetai+= gsl_matrix_get(p->Xc,i,k)*beta->data[iParm1++];

-

-    pn= 1/(1 + gsl_sf_exp(-Xbetai));

-    

-    // Add a protection for pn very close to 0 or 1?

-    aux=pn*(1-pn);

-

-    // Calculate sub-gradient vector gn.

-    gsl_vector_set_zero(gn);

-    gn->data[0]= 1;

-    iParm1=1;

-    for(int k = 0; k < K; ++k) {

-      if(gsl_matrix_int_get(p->X,i,k)>0)

-	gn->data[gsl_matrix_int_get(p->X,i,k)-1+iParm1]=1;

-      iParm1+=p->nlev->data[k]-1;

-    }

-    

-    // Adding the continuous.

-    for(int k = 0; k < Kc; ++k) {

-      gn->data[iParm1++] = gsl_matrix_get(p->Xc,i,k);

-    }

-

-    for(int k1=0;k1<npar; ++k1)

-      if(gn->data[k1]!=0)

-	for(int k2=0;k2<npar; ++k2)

-	  if(gn->data[k2]!=0)

-	    *gsl_matrix_ptr(out,k1,k2) += (aux * gn->data[k1] * gn->data[k2]);

-  }

-  gsl_vector_free(gn);

-}

-

-double wgsl_mixed_optim_f(gsl_vector *v, void *params) {

-  double mLogLik=0;

-  fix_parm_mixed_T *p = (fix_parm_mixed_T *)params;

-  mLogLik = fLogit_mixed(v,p->X,p->nlev,p->Xc,p->y,p->lambdaL1,p->lambdaL2);

-  return mLogLik; 

-}

-

-// Compute both f and df together.

-void 

-wgsl_mixed_optim_fdf (gsl_vector *x, void *params, double *f, gsl_vector *df) {

-  *f = wgsl_mixed_optim_f(x, params); 

-  wgsl_mixed_optim_df(x, params, df);

-}

-

-// Xc is the matrix of continuous covariates, Nobs x Kc (NULL if not used).

-int logistic_mixed_fit(gsl_vector *beta, gsl_matrix_int *X,

-		       gsl_vector_int *nlev, gsl_matrix *Xc,

-		       gsl_vector *y, double lambdaL1, double lambdaL2) {

-  double mLogLik=0;

-  fix_parm_mixed_T p;

-  int npar = beta->size; 

-  int iter=0;

-  double maxchange=0;

-

-  // Intializing fix parameters.

-  p.X=X;

-  p.Xc=Xc;

-  p.nlev=nlev;

-  p.y=y;

-  p.lambdaL1=lambdaL1;

-  p.lambdaL2=lambdaL2;

-  

-  // Initial fit.

-  mLogLik = wgsl_mixed_optim_f(beta,&p);

-

-  gsl_matrix *myH = gsl_matrix_alloc(npar,npar); // Hessian matrix.

-  gsl_vector *stBeta = gsl_vector_alloc(npar);   // Direction to move.

-

-  gsl_vector *myG = gsl_vector_alloc(npar);      // Gradient.

-  gsl_vector *tau = gsl_vector_alloc(npar);      // tau for QR.

-

-  for(iter=0;iter<100;iter++){ 

-    wgsl_mixed_optim_hessian(beta,&p,myH);       // Calculate Hessian.

-    wgsl_mixed_optim_df(beta,&p,myG);            // Calculate Gradient.

-    gsl_linalg_QR_decomp(myH,tau);               // Calculate next beta.

-    gsl_linalg_QR_solve(myH,tau,myG,stBeta);

-    gsl_vector_sub(beta,stBeta);

-    

-    // Monitor convergence.

-    maxchange=0;

-    for(int i=0;i<npar; i++)

-      if(maxchange<fabs(stBeta->data[i]))

-	maxchange=fabs(stBeta->data[i]);

-

-    if(maxchange<1E-4)

-      break;

-  }

-

-  // Final fit.

-  mLogLik = wgsl_mixed_optim_f(beta,&p);

-  

-  gsl_vector_free (tau);

-  gsl_vector_free (stBeta);

-  gsl_vector_free (myG);

-  gsl_matrix_free (myH);

-

-  return 0;

-}

-

-/***************/

-/* Categorical */

-/***************/

-

-// I need to bundle all the data that goes to the function to optimze

-// together.

-typedef struct {

-  gsl_matrix_int *X;

-  gsl_vector_int *nlev;

-  gsl_vector *y;

-  double lambdaL1;

-  double lambdaL2;

-} fix_parm_cat_T;

-

-double fLogit_cat (gsl_vector *beta, gsl_matrix_int *X, gsl_vector_int *nlev,

-		   gsl_vector *y, double lambdaL1, double lambdaL2) {

-  int n = y->size; 

-  int npar = beta->size; 

-  double total = 0;

-  double aux = 0;

-  

-  // omp_set_num_threads(ompthr); /\* Changed loop start at 1 instead

-  // of 0 to avoid regularization of beta 0*\/ /\*#pragma omp parallel

-  // for reduction (+:total)*\/

-  for(int i = 1; i < npar; ++i)

-    total += beta->data[i]*beta->data[i];

-  total = (-total*lambdaL2/2);

-  

-  // /\*#pragma omp parallel for reduction (+:aux)*\/

-  for(int i = 1; i < npar; ++i)

-    aux += (beta->data[i]>0 ? beta->data[i] : -beta->data[i]);

-  total = total-aux*lambdaL1;

-  

-  // #pragma omp parallel for schedule(static) shared(n,beta,X,nlev,y)

-  // #reduction (+:total)

-  for(int i = 0; i < n; ++i) {

-    double Xbetai=beta->data[0];

-    int iParm=1;

-    for(int k = 0; k < X->size2; ++k) {

-      if(gsl_matrix_int_get(X,i,k)>0)

-	Xbetai+=beta->data[gsl_matrix_int_get(X,i,k)-1+iParm];

-      iParm+=nlev->data[k]-1;

-    }

-    total += y->data[i]*Xbetai-gsl_sf_log_1plusx(gsl_sf_exp(Xbetai));

-  }

-  return -total;

-} 

-

-void logistic_cat_pred (gsl_vector *beta,     // Vector of parameters

-					      // length = 1 + Sum_k(C_k-1).

-		        gsl_matrix_int *X,    // Matrix Nobs x K 

-		        gsl_vector_int *nlev, // Vector with #categories

-		        gsl_vector *yhat){    // Vector of prob. predicted by

-					      // the logistic.

-  for(int i = 0; i < X->size1; ++i) {

-    double Xbetai=beta->data[0];

-    int iParm=1;

-    for(int k = 0; k < X->size2; ++k) {

-      if(gsl_matrix_int_get(X,i,k)>0)

-	Xbetai+=beta->data[gsl_matrix_int_get(X,i,k)-1+iParm];

-      iParm+=nlev->data[k]-1;

-    }

-    yhat->data[i]=1/(1 + gsl_sf_exp(-Xbetai));

-  }

-}

-

-// The gradient of f, df = (df/dx, df/dy).

-void  wgsl_cat_optim_df (const gsl_vector *beta, void *params, 

-		   gsl_vector *out) {

-  fix_parm_cat_T *p = (fix_parm_cat_T *)params;

-  int n = p->y->size; 

-  int K = p->X->size2; 

-  int npar = beta->size;

-  

-  // Intitialize gradient out necessary?

-  for(int i = 0; i < npar; ++i) 

-    out->data[i]= 0;

-  

-  // Changed loop start at 1 instead of 0 to avoid regularization of beta 0.

-  for(int i = 1; i < npar; ++i)

-    out->data[i]= p->lambdaL2*beta->data[i]; 

-  for(int i = 1; i < npar; ++i)

-    out->data[i]+= p->lambdaL1*((beta->data[i]>0)-(beta->data[i]<0));

-  

-  for(int i = 0; i < n; ++i) {

-    double pn=0;

-    double Xbetai=beta->data[0];

-    int iParm=1;

-    for(int k = 0; k < K; ++k) {

-      if(gsl_matrix_int_get(p->X,i,k)>0)

-	Xbetai+=beta->data[gsl_matrix_int_get(p->X,i,k)-1+iParm];

-      iParm+=p->nlev->data[k]-1;

-    }

-    

-    pn= -( p->y->data[i] - 1/(1 + gsl_sf_exp(-Xbetai)) );

-

-    out->data[0]+= pn;

-    iParm=1;

-    for(int k = 0; k < K; ++k) {

-      if(gsl_matrix_int_get(p->X,i,k)>0)

-	out->data[gsl_matrix_int_get(p->X,i,k)-1+iParm]+=pn;

-      iParm+=p->nlev->data[k]-1;

-    }

-  }

-}

-

-// The Hessian of f.

-void  wgsl_cat_optim_hessian (const gsl_vector *beta, void *params, 

-			      gsl_matrix *out) {

-  fix_parm_cat_T *p = (fix_parm_cat_T *)params;

-  int n = p->y->size; 

-  int K = p->X->size2; 

-  int npar = beta->size;

-  

-  // Intitialize Hessian out necessary.

-  gsl_matrix_set_zero(out);

-  

-  // Changed loop start at 1 instead of 0 to avoid regularization of beta.

-  for(int i = 1; i < npar; ++i)

-    gsl_matrix_set(out,i,i,(p->lambdaL2)); // Double-check this.

-  

-  // L1 penalty not working yet, as not differentiable, I may need to

-  // do coordinate descent (as in glm_net).

-  for(int i = 0; i < n; ++i) {

-    double pn=0;

-    double aux=0;

-    double Xbetai=beta->data[0];

-    int iParm2=1;

-    int iParm1=1;

-    for(int k = 0; k < K; ++k) {

-      if(gsl_matrix_int_get(p->X,i,k)>0)

-	Xbetai+=beta->data[gsl_matrix_int_get(p->X,i,k)-1+iParm1];

-      iParm1+=p->nlev->data[k]-1;  //-1?

-    }

-

-    pn= 1/(1 + gsl_sf_exp(-Xbetai));

-    

-    // Add a protection for pn very close to 0 or 1?

-    aux=pn*(1-pn);

-    *gsl_matrix_ptr(out,0,0)+=aux;

-    iParm2=1;

-    for(int k2 = 0; k2 < K; ++k2) {

-      if(gsl_matrix_int_get(p->X,i,k2)>0)

-	*gsl_matrix_ptr(out,0,gsl_matrix_int_get(p->X,i,k2)-1+iParm2)+=aux;

-      iParm2+=p->nlev->data[k2]-1;   //-1?

-    }

-    iParm1=1;

-    for(int k1 = 0; k1 < K; ++k1) {

-      if(gsl_matrix_int_get(p->X,i,k1)>0)

-	*gsl_matrix_ptr(out,gsl_matrix_int_get(p->X,i,k1)-1+iParm1,0)+=aux;

-      iParm2=1;

-      for(int k2 = 0; k2 < K; ++k2) {

-	if((gsl_matrix_int_get(p->X,i,k1)>0) &&

-	   (gsl_matrix_int_get(p->X,i,k2)>0))

-	  *gsl_matrix_ptr(out

-			  ,gsl_matrix_int_get(p->X,i,k1)-1+iParm1

-			  ,gsl_matrix_int_get(p->X,i,k2)-1+iParm2

-			  )+=aux;

-	iParm2+=p->nlev->data[k2]-1;  //-1?

-      }

-      iParm1+=p->nlev->data[k1]-1; //-1?

-    }

-  }

-}

-

-double wgsl_cat_optim_f(gsl_vector *v, void *params) {

-  double mLogLik=0;

-  fix_parm_cat_T *p = (fix_parm_cat_T *)params;

-  mLogLik = fLogit_cat(v,p->X,p->nlev,p->y,p->lambdaL1,p->lambdaL2);

-  return mLogLik; 

-}

-

-// Compute both f and df together.

-void wgsl_cat_optim_fdf (gsl_vector *x, void *params, double *f,

-			 gsl_vector *df) {

-  *f = wgsl_cat_optim_f(x, params); 

-  wgsl_cat_optim_df(x, params, df);

-}

-

-int logistic_cat_fit(gsl_vector *beta,

-		     gsl_matrix_int *X,

-		     gsl_vector_int *nlev,

-		     gsl_vector *y,

-		     double lambdaL1,

-		     double lambdaL2) {

-  double mLogLik=0;

-  fix_parm_cat_T p;

-  int npar = beta->size; 

-  int iter=0;

-  double maxchange=0;

-

-  // Intializing fix parameters.

-  p.X=X;

-  p.nlev=nlev;

-  p.y=y;

-  p.lambdaL1=lambdaL1;

-  p.lambdaL2=lambdaL2;

-  

-  // Initial fit.

-  mLogLik = wgsl_cat_optim_f(beta,&p);

-

-  gsl_matrix *myH = gsl_matrix_alloc(npar,npar); // Hessian matrix.

-  gsl_vector *stBeta = gsl_vector_alloc(npar);   // Direction to move.

-

-  gsl_vector *myG = gsl_vector_alloc(npar);      // Gradient.

-  gsl_vector *tau = gsl_vector_alloc(npar);      // tau for QR.

-

-  for(iter=0;iter<100;iter++){ 

-    wgsl_cat_optim_hessian(beta,&p,myH); // Calculate Hessian.

-    wgsl_cat_optim_df(beta,&p,myG);      // Calculate Gradient.

-    gsl_linalg_QR_decomp(myH,tau);       // Calculate next beta.

-    gsl_linalg_QR_solve(myH,tau,myG,stBeta);

-    gsl_vector_sub(beta,stBeta);

-    

-    // Monitor convergence.

-    maxchange=0;

-    for(int i=0;i<npar; i++)

-      if(maxchange<fabs(stBeta->data[i]))

-	maxchange=fabs(stBeta->data[i]);

-

-#ifdef _RPR_DEBUG_

-    mLogLik = wgsl_cat_optim_f(beta,&p);

-#endif

-

-    if(maxchange<1E-4)

-      break;

-  }

-

-  // Final fit.

-  mLogLik = wgsl_cat_optim_f(beta,&p);

-

-  gsl_vector_free (tau);

-  gsl_vector_free (stBeta);

-  gsl_vector_free (myG);

-  gsl_matrix_free (myH);

-

-  return 0;

-}

-

-/***************/

-/* Continuous  */

-/***************/

-

-// I need to bundle all the data that goes to the function to optimze

-// together.

-typedef struct{

-  gsl_matrix *Xc;   // continuous covariates; Matrix Nobs x Kc 

-  gsl_vector *y;

-  double lambdaL1;

-  double lambdaL2;

-}fix_parm_cont_T;

-

-double fLogit_cont(gsl_vector *beta, gsl_matrix *Xc, gsl_vector *y,

-		   double lambdaL1, double lambdaL2) {

-  int n = y->size; 

-  int npar = beta->size; 

-  double total = 0;

-  double aux = 0;

-  

-  // omp_set_num_threads(ompthr); /\* Changed loop start at 1 instead

-  // of 0 to avoid regularization of beta_0*\/ /\*#pragma omp parallel

-  // for reduction (+:total)*\/

-  for(int i = 1; i < npar; ++i)

-    total += beta->data[i]*beta->data[i];

-  total = (-total*lambdaL2/2);

-  

-  // /\*#pragma omp parallel for reduction (+:aux)*\/

-  for(int i = 1; i < npar; ++i)

-    aux += (beta->data[i]>0 ? beta->data[i] : -beta->data[i]);

-  total = total-aux*lambdaL1;

-  

-  // #pragma omp parallel for schedule(static) shared(n,beta,X,nlev,y)

-  // #reduction (+:total)

-  for(int i = 0; i < n; ++i) {

-    double Xbetai=beta->data[0];

-    int iParm=1;

-    for(int k = 0; k < (Xc->size2); ++k) 

-      Xbetai+= gsl_matrix_get(Xc,i,k)*beta->data[iParm++];

-    total += y->data[i]*Xbetai-gsl_sf_log_1plusx(gsl_sf_exp(Xbetai));

-  }

-  return -total;

-} 

-

-void logistic_cont_pred(gsl_vector *beta,    // Vector of parameters

-					     // length = 1 + Sum_k(C_k-1).

-			gsl_matrix *Xc,      // Continuous covariates matrix,

-			                     // Nobs x Kc (NULL if not used).

-			,gsl_vector *yhat) { // Vector of prob. predicted by

-                                             // the logistic.

-  for(int i = 0; i < Xc->size1; ++i) {

-    double Xbetai=beta->data[0];

-    int iParm=1;

-    for(int k = 0; k < (Xc->size2); ++k) 

-      Xbetai+= gsl_matrix_get(Xc,i,k)*beta->data[iParm++];

-    yhat->data[i]=1/(1 + gsl_sf_exp(-Xbetai));

-  }

-}

-

-// The gradient of f, df = (df/dx, df/dy).

-void wgsl_cont_optim_df (const gsl_vector *beta, void *params, 

-			 gsl_vector *out) {

-  fix_parm_cont_T *p = (fix_parm_cont_T *)params;

-  int n = p->y->size; 

-  int Kc = p->Xc->size2; 

-  int npar = beta->size;

-  

-  // Intitialize gradient out necessary?

-  for(int i = 0; i < npar; ++i) 

-    out->data[i]= 0;

-  

-  // Changed loop start at 1 instead of 0 to avoid regularization of beta 0.

-  for(int i = 1; i < npar; ++i)

-    out->data[i]= p->lambdaL2*beta->data[i]; 

-  for(int i = 1; i < npar; ++i)

-    out->data[i]+= p->lambdaL1*((beta->data[i]>0)-(beta->data[i]<0));

-  

-  for(int i = 0; i < n; ++i) {

-    double pn=0;

-    double Xbetai=beta->data[0];

-    int iParm=1;

-    for(int k = 0; k < Kc; ++k) 

-      Xbetai+= gsl_matrix_get(p->Xc,i,k)*beta->data[iParm++];

-

-    pn= -( p->y->data[i] - 1/(1 + gsl_sf_exp(-Xbetai)) );

-

-    out->data[0]+= pn;

-    iParm=1;

-    

-    // Adding the continuous.

-    for(int k = 0; k < Kc; ++k) {

-      out->data[iParm++] += gsl_matrix_get(p->Xc,i,k)*pn;

-    }

-  }

-}

-

-// The Hessian of f.

-void wgsl_cont_optim_hessian (const gsl_vector *beta, void *params, 

-			      gsl_matrix *out) {

-  fix_parm_cont_T *p = (fix_parm_cont_T *)params;

-  int n = p->y->size; 

-  int Kc = p->Xc->size2; 

-  int npar = beta->size; 

-  gsl_vector *gn = gsl_vector_alloc(npar); // gn.

-  

-  // Intitialize Hessian out necessary ???

-  

-  gsl_matrix_set_zero(out);

-  

-  // Changed loop start at 1 instead of 0 to avoid regularization of

-  // beta 0.

-  for(int i = 1; i < npar; ++i)

-    gsl_matrix_set(out,i,i,(p->lambdaL2));  // Double-check this.

-  

-  // L1 penalty not working yet, as not differentiable, I may need to

-  // do coordinate descent (as in glm_net).

-  for(int i = 0; i < n; ++i) {

-    double pn=0;

-    double aux=0;

-    double Xbetai=beta->data[0];

-    int iParm1=1;

-    for(int k = 0; k < Kc; ++k) 

-      Xbetai+= gsl_matrix_get(p->Xc,i,k)*beta->data[iParm1++];

-

-    pn= 1/(1 + gsl_sf_exp(-Xbetai));

-    

-    // Add a protection for pn very close to 0 or 1?

-    aux=pn*(1-pn);

-

-    // Calculate sub-gradient vector gn.

-    gsl_vector_set_zero(gn);

-    gn->data[0]= 1;

-    iParm1=1;

-    for(int k = 0; k < Kc; ++k) {

-      gn->data[iParm1++] = gsl_matrix_get(p->Xc,i,k);

-    }

-

-    for(int k1=0;k1<npar; ++k1)

-      if(gn->data[k1]!=0)

-	for(int k2=0;k2<npar; ++k2)

-	  if(gn->data[k2]!=0)

-	    *gsl_matrix_ptr(out,k1,k2) += (aux * gn->data[k1] * gn->data[k2]);

-  }

-  gsl_vector_free(gn);

-}

-

-double wgsl_cont_optim_f(gsl_vector *v, void *params) {

-  double mLogLik=0;

-  fix_parm_cont_T *p = (fix_parm_cont_T *)params;

-  mLogLik = fLogit_cont(v,p->Xc,p->y,p->lambdaL1,p->lambdaL2);

-  return mLogLik; 

-}

-

-// Compute both f and df together.

-void wgsl_cont_optim_fdf (gsl_vector *x, void *params, 

-			  double *f, gsl_vector *df) {

-  *f = wgsl_cont_optim_f(x, params); 

-  wgsl_cont_optim_df(x, params, df);

-}

-

-int logistic_cont_fit (gsl_vector *beta,

-		       gsl_matrix *Xc,   // Continuous covariates matrix,

-		 		         // Nobs x Kc (NULL if not used).

-		       gsl_vector *y,

-		       double lambdaL1,

-		       double lambdaL2) {

-

-  double mLogLik=0;

-  fix_parm_cont_T p;

-  int npar = beta->size; 

-  int iter=0;

-  double maxchange=0;

-

-  // Initializing fix parameters.

-  p.Xc=Xc;

-  p.y=y;

-  p.lambdaL1=lambdaL1;

-  p.lambdaL2=lambdaL2;

-  

-  // Initial fit.

-  mLogLik = wgsl_cont_optim_f(beta,&p);

-

-  gsl_matrix *myH = gsl_matrix_alloc(npar,npar); // Hessian matrix.

-  gsl_vector *stBeta = gsl_vector_alloc(npar);   // Direction to move.

-

-  gsl_vector *myG = gsl_vector_alloc(npar);      // Gradient.

-  gsl_vector *tau = gsl_vector_alloc(npar);      // tau for QR.

-

-  for(iter=0;iter<100;iter++){ 

-    wgsl_cont_optim_hessian(beta,&p,myH); // Calculate Hessian.

-    wgsl_cont_optim_df(beta,&p,myG);      // Calculate Gradient.

-    gsl_linalg_QR_decomp(myH,tau);        // Calculate next beta.

-    gsl_linalg_QR_solve(myH,tau,myG,stBeta);

-    gsl_vector_sub(beta,stBeta);

-    

-    // Monitor convergence.

-    maxchange=0;

-    for(int i=0;i<npar; i++)

-      if(maxchange<fabs(stBeta->data[i]))

-	maxchange=fabs(stBeta->data[i]);

-

-#ifdef _RPR_DEBUG_

-    mLogLik = wgsl_cont_optim_f(beta,&p);

-#endif

-

-    if(maxchange<1E-4)

-      break;

-  }

-

-  // Final fit.

-  mLogLik = wgsl_cont_optim_f(beta,&p);

-  

-  gsl_vector_free (tau);

-  gsl_vector_free (stBeta);

-  gsl_vector_free (myG);

-  gsl_matrix_free (myH);

-

-  return 0;

-}

-

+#include <stdio.h>
+#include <math.h>
+#include <gsl/gsl_matrix.h>
+#include <gsl/gsl_rng.h>
+#include <gsl/gsl_multimin.h>
+#include <gsl/gsl_sf.h>
+#include <gsl/gsl_linalg.h>
+#include "logistic.h"
+
+// I need to bundle all the data that goes to the function to optimze
+// together.
+typedef struct{
+  gsl_matrix_int *X;
+  gsl_vector_int *nlev;
+  gsl_vector *y;
+  gsl_matrix *Xc; // Continuous covariates matrix Nobs x Kc (NULL if not used).
+  double lambdaL1;
+  double lambdaL2;
+} fix_parm_mixed_T;
+
+double fLogit_mixed(gsl_vector *beta,
+		    gsl_matrix_int *X,
+		    gsl_vector_int *nlev,
+		    gsl_matrix *Xc,
+		    gsl_vector *y,
+		    double lambdaL1,
+		    double lambdaL2) {
+  int n = y->size;
+  int npar = beta->size;
+  double total = 0;
+  double aux = 0;
+
+  // Changed loop start at 1 instead of 0 to avoid regularization of
+  // beta_0*\/
+  // #pragma omp parallel for reduction (+:total)
+  for(int i = 1; i < npar; ++i)
+    total += beta->data[i]*beta->data[i];
+  total = (-total*lambdaL2/2);
+  // #pragma omp parallel for reduction (+:aux)
+  for(int i = 1; i < npar; ++i)
+    aux += (beta->data[i]>0 ? beta->data[i] : -beta->data[i]);
+  total = total-aux*lambdaL1;
+  // #pragma omp parallel for schedule(static) shared(n,beta,X,nlev,y)
+  // #reduction (+:total)
+  for(int i = 0; i < n; ++i) {
+    double Xbetai=beta->data[0];
+    int iParm=1;
+    for(int k = 0; k < X->size2; ++k) {
+      if(gsl_matrix_int_get(X,i,k)>0)
+	Xbetai+=beta->data[gsl_matrix_int_get(X,i,k)-1+iParm];
+      iParm+=nlev->data[k]-1;
+    }
+    for(int k = 0; k < (Xc->size2); ++k)
+      Xbetai+= gsl_matrix_get(Xc,i,k)*beta->data[iParm++];
+    total += y->data[i]*Xbetai-gsl_sf_log_1plusx(gsl_sf_exp(Xbetai));
+  }
+  return -total;
+}
+
+void logistic_mixed_pred(gsl_vector *beta,     // Vector of parameters
+					       // length = 1 + Sum_k(C_k -1)
+			 gsl_matrix_int *X,    // Matrix Nobs x K
+			 gsl_vector_int *nlev, // Vector with number categories
+			 gsl_matrix *Xc,       // Continuous covariates matrix:
+			                       // obs x Kc (NULL if not used).
+			 gsl_vector *yhat){    // Vector of prob. predicted by
+					       // the logistic
+  for(int i = 0; i < X->size1; ++i) {
+    double Xbetai=beta->data[0];
+    int iParm=1;
+    for(int k = 0; k < X->size2; ++k) {
+      if(gsl_matrix_int_get(X,i,k)>0)
+	Xbetai+=beta->data[gsl_matrix_int_get(X,i,k)-1+iParm];
+      iParm+=nlev->data[k]-1;
+    }
+    // Adding the continuous.
+    for(int k = 0; k < (Xc->size2); ++k)
+      Xbetai+= gsl_matrix_get(Xc,i,k)*beta->data[iParm++];
+    yhat->data[i]=1/(1 + gsl_sf_exp(-Xbetai));
+  }
+}
+
+
+// The gradient of f, df = (df/dx, df/dy).
+void wgsl_mixed_optim_df (const gsl_vector *beta, void *params,
+			  gsl_vector *out) {
+  fix_parm_mixed_T *p = (fix_parm_mixed_T *)params;
+  int n = p->y->size;
+  int K = p->X->size2;
+  int Kc = p->Xc->size2;
+  int npar = beta->size;
+
+  // Intitialize gradient out necessary?
+  for(int i = 0; i < npar; ++i)
+    out->data[i]= 0;
+
+  // Changed loop start at 1 instead of 0 to avoid regularization of beta 0.
+  for(int i = 1; i < npar; ++i)
+    out->data[i]= p->lambdaL2*beta->data[i];
+  for(int i = 1; i < npar; ++i)
+    out->data[i]+= p->lambdaL1*((beta->data[i]>0)-(beta->data[i]<0));
+
+  for(int i = 0; i < n; ++i) {
+    double pn=0;
+    double Xbetai=beta->data[0];
+    int iParm=1;
+    for(int k = 0; k < K; ++k) {
+      if(gsl_matrix_int_get(p->X,i,k)>0)
+	Xbetai+=beta->data[gsl_matrix_int_get(p->X,i,k)-1+iParm];
+      iParm+=p->nlev->data[k]-1;
+    }
+
+    // Adding the continuous.
+    for(int k = 0; k < Kc; ++k)
+      Xbetai+= gsl_matrix_get(p->Xc,i,k)*beta->data[iParm++];
+
+    pn= -( p->y->data[i] - 1/(1 + gsl_sf_exp(-Xbetai)) );
+
+    out->data[0]+= pn;
+    iParm=1;
+    for(int k = 0; k < K; ++k) {
+      if(gsl_matrix_int_get(p->X,i,k)>0)
+	out->data[gsl_matrix_int_get(p->X,i,k)-1+iParm]+=pn;
+      iParm+=p->nlev->data[k]-1;
+    }
+
+    // Adding the continuous.
+    for(int k = 0; k < Kc; ++k) {
+      out->data[iParm++] += gsl_matrix_get(p->Xc,i,k)*pn;
+    }
+  }
+
+}
+
+// The Hessian of f.
+void  wgsl_mixed_optim_hessian (const gsl_vector *beta, void *params,
+				gsl_matrix *out) {
+  fix_parm_mixed_T *p = (fix_parm_mixed_T *)params;
+  int n = p->y->size;
+  int K = p->X->size2;
+  int Kc = p->Xc->size2;
+  int npar = beta->size;
+  gsl_vector *gn = gsl_vector_alloc(npar); // gn
+
+  // Intitialize Hessian out necessary ???
+  gsl_matrix_set_zero(out);
+
+  /* Changed loop start at 1 instead of 0 to avoid regularization of beta 0*/
+  for(int i = 1; i < npar; ++i)
+    gsl_matrix_set(out,i,i,(p->lambdaL2)); // Double-check this.
+
+  // L1 penalty not working yet, as not differentiable, I may need to
+  // do coordinate descent (as in glm_net)
+  for(int i = 0; i < n; ++i) {
+    double pn=0;
+    double aux=0;
+    double Xbetai=beta->data[0];
+    int iParm1=1;
+    for(int k = 0; k < K; ++k) {
+      if(gsl_matrix_int_get(p->X,i,k)>0)
+	Xbetai+=beta->data[gsl_matrix_int_get(p->X,i,k)-1+iParm1];
+      iParm1+=p->nlev->data[k]-1;  //-1?
+    }
+
+    // Adding the continuous.
+    for(int k = 0; k < Kc; ++k)
+      Xbetai+= gsl_matrix_get(p->Xc,i,k)*beta->data[iParm1++];
+
+    pn= 1/(1 + gsl_sf_exp(-Xbetai));
+
+    // Add a protection for pn very close to 0 or 1?
+    aux=pn*(1-pn);
+
+    // Calculate sub-gradient vector gn.
+    gsl_vector_set_zero(gn);
+    gn->data[0]= 1;
+    iParm1=1;
+    for(int k = 0; k < K; ++k) {
+      if(gsl_matrix_int_get(p->X,i,k)>0)
+	gn->data[gsl_matrix_int_get(p->X,i,k)-1+iParm1]=1;
+      iParm1+=p->nlev->data[k]-1;
+    }
+
+    // Adding the continuous.
+    for(int k = 0; k < Kc; ++k) {
+      gn->data[iParm1++] = gsl_matrix_get(p->Xc,i,k);
+    }
+
+    for(int k1=0;k1<npar; ++k1)
+      if(gn->data[k1]!=0)
+	for(int k2=0;k2<npar; ++k2)
+	  if(gn->data[k2]!=0)
+	    *gsl_matrix_ptr(out,k1,k2) += (aux * gn->data[k1] * gn->data[k2]);
+  }
+  gsl_vector_free(gn);
+}
+
+double wgsl_mixed_optim_f(gsl_vector *v, void *params) {
+  double mLogLik=0;
+  fix_parm_mixed_T *p = (fix_parm_mixed_T *)params;
+  mLogLik = fLogit_mixed(v,p->X,p->nlev,p->Xc,p->y,p->lambdaL1,p->lambdaL2);
+  return mLogLik;
+}
+
+// Compute both f and df together.
+void
+wgsl_mixed_optim_fdf (gsl_vector *x, void *params, double *f, gsl_vector *df) {
+  *f = wgsl_mixed_optim_f(x, params);
+  wgsl_mixed_optim_df(x, params, df);
+}
+
+// Xc is the matrix of continuous covariates, Nobs x Kc (NULL if not used).
+int logistic_mixed_fit(gsl_vector *beta, gsl_matrix_int *X,
+		       gsl_vector_int *nlev, gsl_matrix *Xc,
+		       gsl_vector *y, double lambdaL1, double lambdaL2) {
+  double mLogLik=0;
+  fix_parm_mixed_T p;
+  int npar = beta->size;
+  int iter=0;
+  double maxchange=0;
+
+  // Intializing fix parameters.
+  p.X=X;
+  p.Xc=Xc;
+  p.nlev=nlev;
+  p.y=y;
+  p.lambdaL1=lambdaL1;
+  p.lambdaL2=lambdaL2;
+
+  // Initial fit.
+  mLogLik = wgsl_mixed_optim_f(beta,&p);
+
+  gsl_matrix *myH = gsl_matrix_alloc(npar,npar); // Hessian matrix.
+  gsl_vector *stBeta = gsl_vector_alloc(npar);   // Direction to move.
+
+  gsl_vector *myG = gsl_vector_alloc(npar);      // Gradient.
+  gsl_vector *tau = gsl_vector_alloc(npar);      // tau for QR.
+
+  for(iter=0;iter<100;iter++){
+    wgsl_mixed_optim_hessian(beta,&p,myH);       // Calculate Hessian.
+    wgsl_mixed_optim_df(beta,&p,myG);            // Calculate Gradient.
+    gsl_linalg_QR_decomp(myH,tau);               // Calculate next beta.
+    gsl_linalg_QR_solve(myH,tau,myG,stBeta);
+    gsl_vector_sub(beta,stBeta);
+
+    // Monitor convergence.
+    maxchange=0;
+    for(int i=0;i<npar; i++)
+      if(maxchange<fabs(stBeta->data[i]))
+	maxchange=fabs(stBeta->data[i]);
+
+    if(maxchange<1E-4)
+      break;
+  }
+
+  // Final fit.
+  mLogLik = wgsl_mixed_optim_f(beta,&p);
+
+  gsl_vector_free (tau);
+  gsl_vector_free (stBeta);
+  gsl_vector_free (myG);
+  gsl_matrix_free (myH);
+
+  return 0;
+}
+
+/***************/
+/* Categorical */
+/***************/
+
+// I need to bundle all the data that goes to the function to optimze
+// together.
+typedef struct {
+  gsl_matrix_int *X;
+  gsl_vector_int *nlev;
+  gsl_vector *y;
+  double lambdaL1;
+  double lambdaL2;
+} fix_parm_cat_T;
+
+double fLogit_cat (gsl_vector *beta, gsl_matrix_int *X, gsl_vector_int *nlev,
+		   gsl_vector *y, double lambdaL1, double lambdaL2) {
+  int n = y->size;
+  int npar = beta->size;
+  double total = 0;
+  double aux = 0;
+
+  // omp_set_num_threads(ompthr); /\* Changed loop start at 1 instead
+  // of 0 to avoid regularization of beta 0*\/ /\*#pragma omp parallel
+  // for reduction (+:total)*\/
+  for(int i = 1; i < npar; ++i)
+    total += beta->data[i]*beta->data[i];
+  total = (-total*lambdaL2/2);
+
+  // /\*#pragma omp parallel for reduction (+:aux)*\/
+  for(int i = 1; i < npar; ++i)
+    aux += (beta->data[i]>0 ? beta->data[i] : -beta->data[i]);
+  total = total-aux*lambdaL1;
+
+  // #pragma omp parallel for schedule(static) shared(n,beta,X,nlev,y)
+  // #reduction (+:total)
+  for(int i = 0; i < n; ++i) {
+    double Xbetai=beta->data[0];
+    int iParm=1;
+    for(int k = 0; k < X->size2; ++k) {
+      if(gsl_matrix_int_get(X,i,k)>0)
+	Xbetai+=beta->data[gsl_matrix_int_get(X,i,k)-1+iParm];
+      iParm+=nlev->data[k]-1;
+    }
+    total += y->data[i]*Xbetai-gsl_sf_log_1plusx(gsl_sf_exp(Xbetai));
+  }
+  return -total;
+}
+
+void logistic_cat_pred (gsl_vector *beta,     // Vector of parameters
+					      // length = 1 + Sum_k(C_k-1).
+		        gsl_matrix_int *X,    // Matrix Nobs x K
+		        gsl_vector_int *nlev, // Vector with #categories
+		        gsl_vector *yhat){    // Vector of prob. predicted by
+					      // the logistic.
+  for(int i = 0; i < X->size1; ++i) {
+    double Xbetai=beta->data[0];
+    int iParm=1;
+    for(int k = 0; k < X->size2; ++k) {
+      if(gsl_matrix_int_get(X,i,k)>0)
+	Xbetai+=beta->data[gsl_matrix_int_get(X,i,k)-1+iParm];
+      iParm+=nlev->data[k]-1;
+    }
+    yhat->data[i]=1/(1 + gsl_sf_exp(-Xbetai));
+  }
+}
+
+// The gradient of f, df = (df/dx, df/dy).
+void  wgsl_cat_optim_df (const gsl_vector *beta, void *params,
+		   gsl_vector *out) {
+  fix_parm_cat_T *p = (fix_parm_cat_T *)params;
+  int n = p->y->size;
+  int K = p->X->size2;
+  int npar = beta->size;
+
+  // Intitialize gradient out necessary?
+  for(int i = 0; i < npar; ++i)
+    out->data[i]= 0;
+
+  // Changed loop start at 1 instead of 0 to avoid regularization of beta 0.
+  for(int i = 1; i < npar; ++i)
+    out->data[i]= p->lambdaL2*beta->data[i];
+  for(int i = 1; i < npar; ++i)
+    out->data[i]+= p->lambdaL1*((beta->data[i]>0)-(beta->data[i]<0));
+
+  for(int i = 0; i < n; ++i) {
+    double pn=0;
+    double Xbetai=beta->data[0];
+    int iParm=1;
+    for(int k = 0; k < K; ++k) {
+      if(gsl_matrix_int_get(p->X,i,k)>0)
+	Xbetai+=beta->data[gsl_matrix_int_get(p->X,i,k)-1+iParm];
+      iParm+=p->nlev->data[k]-1;
+    }
+
+    pn= -( p->y->data[i] - 1/(1 + gsl_sf_exp(-Xbetai)) );
+
+    out->data[0]+= pn;
+    iParm=1;
+    for(int k = 0; k < K; ++k) {
+      if(gsl_matrix_int_get(p->X,i,k)>0)
+	out->data[gsl_matrix_int_get(p->X,i,k)-1+iParm]+=pn;
+      iParm+=p->nlev->data[k]-1;
+    }
+  }
+}
+
+// The Hessian of f.
+void  wgsl_cat_optim_hessian (const gsl_vector *beta, void *params,
+			      gsl_matrix *out) {
+  fix_parm_cat_T *p = (fix_parm_cat_T *)params;
+  int n = p->y->size;
+  int K = p->X->size2;
+  int npar = beta->size;
+
+  // Intitialize Hessian out necessary.
+  gsl_matrix_set_zero(out);
+
+  // Changed loop start at 1 instead of 0 to avoid regularization of beta.
+  for(int i = 1; i < npar; ++i)
+    gsl_matrix_set(out,i,i,(p->lambdaL2)); // Double-check this.
+
+  // L1 penalty not working yet, as not differentiable, I may need to
+  // do coordinate descent (as in glm_net).
+  for(int i = 0; i < n; ++i) {
+    double pn=0;
+    double aux=0;
+    double Xbetai=beta->data[0];
+    int iParm2=1;
+    int iParm1=1;
+    for(int k = 0; k < K; ++k) {
+      if(gsl_matrix_int_get(p->X,i,k)>0)
+	Xbetai+=beta->data[gsl_matrix_int_get(p->X,i,k)-1+iParm1];
+      iParm1+=p->nlev->data[k]-1;  //-1?
+    }
+
+    pn= 1/(1 + gsl_sf_exp(-Xbetai));
+
+    // Add a protection for pn very close to 0 or 1?
+    aux=pn*(1-pn);
+    *gsl_matrix_ptr(out,0,0)+=aux;
+    iParm2=1;
+    for(int k2 = 0; k2 < K; ++k2) {
+      if(gsl_matrix_int_get(p->X,i,k2)>0)
+	*gsl_matrix_ptr(out,0,gsl_matrix_int_get(p->X,i,k2)-1+iParm2)+=aux;
+      iParm2+=p->nlev->data[k2]-1;   //-1?
+    }
+    iParm1=1;
+    for(int k1 = 0; k1 < K; ++k1) {
+      if(gsl_matrix_int_get(p->X,i,k1)>0)
+	*gsl_matrix_ptr(out,gsl_matrix_int_get(p->X,i,k1)-1+iParm1,0)+=aux;
+      iParm2=1;
+      for(int k2 = 0; k2 < K; ++k2) {
+	if((gsl_matrix_int_get(p->X,i,k1)>0) &&
+	   (gsl_matrix_int_get(p->X,i,k2)>0))
+	  *gsl_matrix_ptr(out
+			  ,gsl_matrix_int_get(p->X,i,k1)-1+iParm1
+			  ,gsl_matrix_int_get(p->X,i,k2)-1+iParm2
+			  )+=aux;
+	iParm2+=p->nlev->data[k2]-1;  //-1?
+      }
+      iParm1+=p->nlev->data[k1]-1; //-1?
+    }
+  }
+}
+
+double wgsl_cat_optim_f(gsl_vector *v, void *params) {
+  double mLogLik=0;
+  fix_parm_cat_T *p = (fix_parm_cat_T *)params;
+  mLogLik = fLogit_cat(v,p->X,p->nlev,p->y,p->lambdaL1,p->lambdaL2);
+  return mLogLik;
+}
+
+// Compute both f and df together.
+void wgsl_cat_optim_fdf (gsl_vector *x, void *params, double *f,
+			 gsl_vector *df) {
+  *f = wgsl_cat_optim_f(x, params);
+  wgsl_cat_optim_df(x, params, df);
+}
+
+int logistic_cat_fit(gsl_vector *beta,
+		     gsl_matrix_int *X,
+		     gsl_vector_int *nlev,
+		     gsl_vector *y,
+		     double lambdaL1,
+		     double lambdaL2) {
+  double mLogLik=0;
+  fix_parm_cat_T p;
+  int npar = beta->size;
+  int iter=0;
+  double maxchange=0;
+
+  // Intializing fix parameters.
+  p.X=X;
+  p.nlev=nlev;
+  p.y=y;
+  p.lambdaL1=lambdaL1;
+  p.lambdaL2=lambdaL2;
+
+  // Initial fit.
+  mLogLik = wgsl_cat_optim_f(beta,&p);
+
+  gsl_matrix *myH = gsl_matrix_alloc(npar,npar); // Hessian matrix.
+  gsl_vector *stBeta = gsl_vector_alloc(npar);   // Direction to move.
+
+  gsl_vector *myG = gsl_vector_alloc(npar);      // Gradient.
+  gsl_vector *tau = gsl_vector_alloc(npar);      // tau for QR.
+
+  for(iter=0;iter<100;iter++){
+    wgsl_cat_optim_hessian(beta,&p,myH); // Calculate Hessian.
+    wgsl_cat_optim_df(beta,&p,myG);      // Calculate Gradient.
+    gsl_linalg_QR_decomp(myH,tau);       // Calculate next beta.
+    gsl_linalg_QR_solve(myH,tau,myG,stBeta);
+    gsl_vector_sub(beta,stBeta);
+
+    // Monitor convergence.
+    maxchange=0;
+    for(int i=0;i<npar; i++)
+      if(maxchange<fabs(stBeta->data[i]))
+	maxchange=fabs(stBeta->data[i]);
+
+#ifdef _RPR_DEBUG_
+    mLogLik = wgsl_cat_optim_f(beta,&p);
+#endif
+
+    if(maxchange<1E-4)
+      break;
+  }
+
+  // Final fit.
+  mLogLik = wgsl_cat_optim_f(beta,&p);
+
+  gsl_vector_free (tau);
+  gsl_vector_free (stBeta);
+  gsl_vector_free (myG);
+  gsl_matrix_free (myH);
+
+  return 0;
+}
+
+/***************/
+/* Continuous  */
+/***************/
+
+// I need to bundle all the data that goes to the function to optimze
+// together.
+typedef struct{
+  gsl_matrix *Xc;   // continuous covariates; Matrix Nobs x Kc
+  gsl_vector *y;
+  double lambdaL1;
+  double lambdaL2;
+}fix_parm_cont_T;
+
+double fLogit_cont(gsl_vector *beta, gsl_matrix *Xc, gsl_vector *y,
+		   double lambdaL1, double lambdaL2) {
+  int n = y->size;
+  int npar = beta->size;
+  double total = 0;
+  double aux = 0;
+
+  // omp_set_num_threads(ompthr); /\* Changed loop start at 1 instead
+  // of 0 to avoid regularization of beta_0*\/ /\*#pragma omp parallel
+  // for reduction (+:total)*\/
+  for(int i = 1; i < npar; ++i)
+    total += beta->data[i]*beta->data[i];
+  total = (-total*lambdaL2/2);
+
+  // /\*#pragma omp parallel for reduction (+:aux)*\/
+  for(int i = 1; i < npar; ++i)
+    aux += (beta->data[i]>0 ? beta->data[i] : -beta->data[i]);
+  total = total-aux*lambdaL1;
+
+  // #pragma omp parallel for schedule(static) shared(n,beta,X,nlev,y)
+  // #reduction (+:total)
+  for(int i = 0; i < n; ++i) {
+    double Xbetai=beta->data[0];
+    int iParm=1;
+    for(int k = 0; k < (Xc->size2); ++k)
+      Xbetai+= gsl_matrix_get(Xc,i,k)*beta->data[iParm++];
+    total += y->data[i]*Xbetai-gsl_sf_log_1plusx(gsl_sf_exp(Xbetai));
+  }
+  return -total;
+}
+
+void logistic_cont_pred(gsl_vector *beta,    // Vector of parameters
+					     // length = 1 + Sum_k(C_k-1).
+			gsl_matrix *Xc,      // Continuous covariates matrix,
+			                     // Nobs x Kc (NULL if not used).
+			gsl_vector *yhat) {  // Vector of prob. predicted by
+                                             // the logistic.
+  for(int i = 0; i < Xc->size1; ++i) {
+    double Xbetai=beta->data[0];
+    int iParm=1;
+    for(int k = 0; k < (Xc->size2); ++k)
+      Xbetai+= gsl_matrix_get(Xc,i,k)*beta->data[iParm++];
+    yhat->data[i]=1/(1 + gsl_sf_exp(-Xbetai));
+  }
+}
+
+// The gradient of f, df = (df/dx, df/dy).
+void wgsl_cont_optim_df (const gsl_vector *beta, void *params,
+			 gsl_vector *out) {
+  fix_parm_cont_T *p = (fix_parm_cont_T *)params;
+  int n = p->y->size;
+  int Kc = p->Xc->size2;
+  int npar = beta->size;
+
+  // Intitialize gradient out necessary?
+  for(int i = 0; i < npar; ++i)
+    out->data[i]= 0;
+
+  // Changed loop start at 1 instead of 0 to avoid regularization of beta 0.
+  for(int i = 1; i < npar; ++i)
+    out->data[i]= p->lambdaL2*beta->data[i];
+  for(int i = 1; i < npar; ++i)
+    out->data[i]+= p->lambdaL1*((beta->data[i]>0)-(beta->data[i]<0));
+
+  for(int i = 0; i < n; ++i) {
+    double pn=0;
+    double Xbetai=beta->data[0];
+    int iParm=1;
+    for(int k = 0; k < Kc; ++k)
+      Xbetai+= gsl_matrix_get(p->Xc,i,k)*beta->data[iParm++];
+
+    pn= -( p->y->data[i] - 1/(1 + gsl_sf_exp(-Xbetai)) );
+
+    out->data[0]+= pn;
+    iParm=1;
+
+    // Adding the continuous.
+    for(int k = 0; k < Kc; ++k) {
+      out->data[iParm++] += gsl_matrix_get(p->Xc,i,k)*pn;
+    }
+  }
+}
+
+// The Hessian of f.
+void wgsl_cont_optim_hessian (const gsl_vector *beta, void *params,
+			      gsl_matrix *out) {
+  fix_parm_cont_T *p = (fix_parm_cont_T *)params;
+  int n = p->y->size;
+  int Kc = p->Xc->size2;
+  int npar = beta->size;
+  gsl_vector *gn = gsl_vector_alloc(npar); // gn.
+
+  // Intitialize Hessian out necessary ???
+
+  gsl_matrix_set_zero(out);
+
+  // Changed loop start at 1 instead of 0 to avoid regularization of
+  // beta 0.
+  for(int i = 1; i < npar; ++i)
+    gsl_matrix_set(out,i,i,(p->lambdaL2));  // Double-check this.
+
+  // L1 penalty not working yet, as not differentiable, I may need to
+  // do coordinate descent (as in glm_net).
+  for(int i = 0; i < n; ++i) {
+    double pn=0;
+    double aux=0;
+    double Xbetai=beta->data[0];
+    int iParm1=1;
+    for(int k = 0; k < Kc; ++k)
+      Xbetai+= gsl_matrix_get(p->Xc,i,k)*beta->data[iParm1++];
+
+    pn= 1/(1 + gsl_sf_exp(-Xbetai));
+
+    // Add a protection for pn very close to 0 or 1?
+    aux=pn*(1-pn);
+
+    // Calculate sub-gradient vector gn.
+    gsl_vector_set_zero(gn);
+    gn->data[0]= 1;
+    iParm1=1;
+    for(int k = 0; k < Kc; ++k) {
+      gn->data[iParm1++] = gsl_matrix_get(p->Xc,i,k);
+    }
+
+    for(int k1=0;k1<npar; ++k1)
+      if(gn->data[k1]!=0)
+	for(int k2=0;k2<npar; ++k2)
+	  if(gn->data[k2]!=0)
+	    *gsl_matrix_ptr(out,k1,k2) += (aux * gn->data[k1] * gn->data[k2]);
+  }
+  gsl_vector_free(gn);
+}
+
+double wgsl_cont_optim_f(gsl_vector *v, void *params) {
+  double mLogLik=0;
+  fix_parm_cont_T *p = (fix_parm_cont_T *)params;
+  mLogLik = fLogit_cont(v,p->Xc,p->y,p->lambdaL1,p->lambdaL2);
+  return mLogLik;
+}
+
+// Compute both f and df together.
+void wgsl_cont_optim_fdf (gsl_vector *x, void *params,
+			  double *f, gsl_vector *df) {
+  *f = wgsl_cont_optim_f(x, params);
+  wgsl_cont_optim_df(x, params, df);
+}
+
+int logistic_cont_fit (gsl_vector *beta,
+		       gsl_matrix *Xc,   // Continuous covariates matrix,
+		 		         // Nobs x Kc (NULL if not used).
+		       gsl_vector *y,
+		       double lambdaL1,
+		       double lambdaL2) {
+
+  double mLogLik=0;
+  fix_parm_cont_T p;
+  int npar = beta->size;
+  int iter=0;
+  double maxchange=0;
+
+  // Initializing fix parameters.
+  p.Xc=Xc;
+  p.y=y;
+  p.lambdaL1=lambdaL1;
+  p.lambdaL2=lambdaL2;
+
+  // Initial fit.
+  mLogLik = wgsl_cont_optim_f(beta,&p);
+
+  gsl_matrix *myH = gsl_matrix_alloc(npar,npar); // Hessian matrix.
+  gsl_vector *stBeta = gsl_vector_alloc(npar);   // Direction to move.
+
+  gsl_vector *myG = gsl_vector_alloc(npar);      // Gradient.
+  gsl_vector *tau = gsl_vector_alloc(npar);      // tau for QR.
+
+  for(iter=0;iter<100;iter++){
+    wgsl_cont_optim_hessian(beta,&p,myH); // Calculate Hessian.
+    wgsl_cont_optim_df(beta,&p,myG);      // Calculate Gradient.
+    gsl_linalg_QR_decomp(myH,tau);        // Calculate next beta.
+    gsl_linalg_QR_solve(myH,tau,myG,stBeta);
+    gsl_vector_sub(beta,stBeta);
+
+    // Monitor convergence.
+    maxchange=0;
+    for(int i=0;i<npar; i++)
+      if(maxchange<fabs(stBeta->data[i]))
+	maxchange=fabs(stBeta->data[i]);
+
+#ifdef _RPR_DEBUG_
+    mLogLik = wgsl_cont_optim_f(beta,&p);
+#endif
+
+    if(maxchange<1E-4)
+      break;
+  }
+
+  // Final fit.
+  mLogLik = wgsl_cont_optim_f(beta,&p);
+
+  gsl_vector_free (tau);
+  gsl_vector_free (stBeta);
+  gsl_vector_free (myG);
+  gsl_matrix_free (myH);
+
+  return 0;
+}