diff options
Diffstat (limited to 'src/gensvm_train.c')
| -rw-r--r-- | src/gensvm_train.c | 78 |
1 files changed, 39 insertions, 39 deletions
diff --git a/src/gensvm_train.c b/src/gensvm_train.c index 680d0dd..8c32809 100644 --- a/src/gensvm_train.c +++ b/src/gensvm_train.c @@ -3,7 +3,7 @@ * @author Gertjan van den Burg * @date August 9, 2013 * @brief Main functions for training the GenSVM solution. - * + * * @details * Contains update and loss functions used to actually find * the optimal V. @@ -32,14 +32,14 @@ * @details * This function is the main training function. This function * handles the optimization of the model with the given model parameters, with - * the data given. On return the matrix GenModel::V contains the optimal + * the data given. On return the matrix GenModel::V contains the optimal * weight matrix. * * In this function, step doubling is used in the majorization algorithm after - * a burn-in of 50 iterations. If the training is finished, GenModel::t and + * a burn-in of 50 iterations. If the training is finished, GenModel::t and * GenModel::W are extracted from GenModel::V. * - * @param[in,out] model the GenModel to be trained. Contains optimal + * @param[in,out] model the GenModel to be trained. Contains optimal * V on exit. * @param[in] data the GenData to train the model with. */ @@ -54,7 +54,7 @@ void gensvm_optimize(struct GenModel *model, struct GenData *data) double *B = Calloc(double, n*(K-1)); double *ZV = Calloc(double, n*(K-1)); - double *ZAZ = Calloc(double, (m+1)*(m+1)); + double *ZAZ = Calloc(double, (m+1)*(m+1)); double *ZAZV = Calloc(double, (m+1)*(K-1)); double *ZAZVT = Calloc(double, (m+1)*(K-1)); @@ -79,16 +79,16 @@ void gensvm_optimize(struct GenModel *model, struct GenData *data) while ((it < MAX_ITER) && (Lbar - L)/L > model->epsilon) { - // ensure V contains newest V and Vbar contains V from + // ensure V contains newest V and Vbar contains V from // previous gensvm_get_update(model, data, B, ZAZ, ZAZV, ZAZVT); if (it > 50) gensvm_step_doubling(model); - + Lbar = L; L = gensvm_get_loss(model, data, ZV); - if (it%100 == 0) + if (it%100 == 0) note("iter = %li, L = %15.16f, Lbar = %15.16f, " "reldiff = %15.16f\n", it, L, Lbar, (Lbar - L)/L); it++; @@ -124,20 +124,20 @@ void gensvm_optimize(struct GenModel *model, struct GenData *data) /** * @brief Calculate the current value of the loss function - * + * * @details - * The current loss function value is calculated based on the matrix V in the + * The current loss function value is calculated based on the matrix V in the * given model. Note that the matrix ZV is passed explicitly to avoid having * to reallocate memory at every step. * - * @param[in] model GenModel structure which holds the current + * @param[in] model GenModel structure which holds the current * estimate V * @param[in] data GenData structure - * @param[in,out] ZV pre-allocated matrix ZV which is updated on + * @param[in,out] ZV pre-allocated matrix ZV which is updated on * output * @returns the current value of the loss function */ -double gensvm_get_loss(struct GenModel *model, struct GenData *data, +double gensvm_get_loss(struct GenModel *model, struct GenData *data, double *ZV) { long i, j; @@ -187,19 +187,19 @@ double gensvm_get_loss(struct GenModel *model, struct GenData *data, * * Because the function gensvm_get_update() is always called after a call to * gensvm_get_loss() with the same GenModel::V, it is unnecessary to calculate - * the updated errors GenModel::Q and GenModel::H here too. This saves on + * the updated errors GenModel::Q and GenModel::H here too. This saves on * computation time. * - * In calculating the majorization coefficients we calculate the elements of a + * In calculating the majorization coefficients we calculate the elements of a * diagonal matrix A with elements * @f[ - * A_{i, i} = \frac{1}{n} \rho_i \sum_{j \neq k} \left[ + * A_{i, i} = \frac{1}{n} \rho_i \sum_{j \neq k} \left[ * \varepsilon_i a_{ijk}^{(p)} + (1 - \varepsilon_i) \omega_i * a_{ijk}^{(p)} \right], * @f] * where @f$ k = y_i @f$. * Since this matrix is only used to calculate the matrix @f$ Z' A Z @f$, it is - * efficient to update a matrix ZAZ through consecutive rank 1 updates with + * efficient to update a matrix ZAZ through consecutive rank 1 updates with * a single element of A and the corresponding row of Z. The BLAS function * dsyr is used for this. * @@ -225,7 +225,7 @@ double gensvm_get_loss(struct GenModel *model, struct GenData *data, * solving this system is done through dposv(). * * @todo - * Consider allocating IPIV and WORK at a higher level, they probably don't + * Consider allocating IPIV and WORK at a higher level, they probably don't * change much during the iterations. * * @param [in,out] model model to be updated @@ -301,7 +301,7 @@ void gensvm_get_update(struct GenModel *model, struct GenData *data, double *B, b = 0.5 - kappa/2.0 - q; } else if ( q <= 1.0) { b = pow(1.0 - q, 3.0)/( - 2.0*pow(kappa + 1.0, + 2.0*pow(kappa + 1.0, 2.0)); } else { b = 0; @@ -336,22 +336,22 @@ void gensvm_get_update(struct GenModel *model, struct GenData *data, double *B, } else { a = 0.25*pow(p, 2.0)*pow( (p/(p - 2.0))* - (0.5 - kappa/2.0 - q), + (0.5 - kappa/2.0 - q), p - 2.0); b = a*(2.0*q + kappa - 1.0)/ - (p - 2.0) + + (p - 2.0) + 0.5*p*pow( p/(p - 2.0)* (0.5 - kappa/ - 2.0 - q), + 2.0 - q), p - 1.0); } if (q <= -kappa) { b = 0.5*p*pow( - 0.5 - kappa/2.0 - q, + 0.5 - kappa/2.0 - q, p - 1.0); } else if ( q <= 1.0) { - b = p*pow(1.0 - q, + b = p*pow(1.0 - q, 2.0*p - 1.0)/ pow(2*kappa+2.0, p); } @@ -379,8 +379,8 @@ void gensvm_get_update(struct GenModel *model, struct GenData *data, double *B, } Avalue *= in * rho[i]; - // Now we calculate the matrix ZAZ. Since this is - // guaranteed to be symmetric, we only calculate the + // Now we calculate the matrix ZAZ. Since this is + // guaranteed to be symmetric, we only calculate the // upper part of the matrix, and then copy this over // to the lower part after all calculations are done. // Note that the use of dsym is faster than dspr, even @@ -402,8 +402,8 @@ void gensvm_get_update(struct GenModel *model, struct GenData *data, double *B, for (j=0; j<m+1; j++) matrix_set(ZAZ, m+1, j, i, matrix_get(ZAZ, m+1, i, j)); */ - - // Calculate the right hand side of the system we + + // Calculate the right hand side of the system we // want to solve. cblas_dsymm( CblasRowMajor, @@ -417,7 +417,7 @@ void gensvm_get_update(struct GenModel *model, struct GenData *data, double *B, model->V, K-1, 0.0, - ZAZV, + ZAZV, K-1); cblas_dgemm( @@ -436,8 +436,8 @@ void gensvm_get_update(struct GenModel *model, struct GenData *data, double *B, ZAZV, K-1); - /* - * Add lambda to all diagonal elements except the first one. Recall + /* + * Add lambda to all diagonal elements except the first one. Recall * that ZAZ is of size m+1 and is symmetric. */ i = 0; @@ -445,19 +445,19 @@ void gensvm_get_update(struct GenModel *model, struct GenData *data, double *B, i += (m+1) + 1; ZAZ[i] += model->lambda; } - + // For the LAPACK call we need to switch to Column- // Major order. This is unnecessary for the matrix - // ZAZ because it is symmetric. The matrix ZAZV + // ZAZ because it is symmetric. The matrix ZAZV // must be converted however. for (i=0; i<m+1; i++) for (j=0; j<K-1; j++) ZAZVT[j*(m+1)+i] = ZAZV[i*(K-1)+j]; - - // We use the lower ('L') part of the matrix ZAZ, + + // We use the lower ('L') part of the matrix ZAZ, // because we have used the upper part in the BLAS // calls above in Row-major order, and Lapack uses - // column major order. + // column major order. status = dposv( 'L', @@ -470,10 +470,10 @@ void gensvm_get_update(struct GenModel *model, struct GenData *data, double *B, if (status != 0) { // This step should not be necessary, as the matrix - // ZAZ is positive semi-definite by definition. It + // ZAZ is positive semi-definite by definition. It // is included for safety. fprintf(stderr, "GenSVM warning: Received nonzero status from " - "dposv: %i\n", + "dposv: %i\n", status); int *IPIV = malloc((m+1)*sizeof(int)); double *WORK = malloc(1*sizeof(double)); @@ -507,7 +507,7 @@ void gensvm_get_update(struct GenModel *model, struct GenData *data, double *B, free(IPIV); } - // Return to Row-major order. The matrix ZAZVT contains the solution + // Return to Row-major order. The matrix ZAZVT contains the solution // after the dposv/dsysv call. for (i=0; i<m+1; i++) for (j=0; j<K-1; j++) |