diff options
Diffstat (limited to 'src/libGenSVM.c')
| -rw-r--r-- | src/libGenSVM.c | 60 |
1 files changed, 30 insertions, 30 deletions
diff --git a/src/libGenSVM.c b/src/libGenSVM.c index b692bdb..c9b0b3c 100644 --- a/src/libGenSVM.c +++ b/src/libGenSVM.c @@ -6,8 +6,8 @@ * * @details * The functions in this file are all functions needed - * to calculate the optimal separation boundaries for - * a multiclass classification problem, using the + * to calculate the optimal separation boundaries for + * a multiclass classification problem, using the * GenSVM algorithm. * */ @@ -23,10 +23,10 @@ inline double rnd() { return (double) rand()/0x7FFFFFFF; } /** * @brief Generate matrix of simplex vertex coordinates - * + * * @details - * Generate the simplex matrix. Each row of the created - * matrix contains the coordinate vector of a single + * Generate the simplex matrix. Each row of the created + * matrix contains the coordinate vector of a single * vertex of the K-simplex in K-1 dimensions. The simplex * generated is a special simplex with edges of length 1. * The simplex matrix U must already have been allocated. @@ -83,9 +83,9 @@ void gensvm_category_matrix(struct GenModel *model, struct GenData *dataset) * * @details * The simplex difference matrix is a 3D matrix which is constructed - * as follows. For each instance i, the difference vectors between the row of + * as follows. For each instance i, the difference vectors between the row of * the simplex matrix corresponding to the class label of instance i and the - * other rows of the simplex matrix are calculated. These difference vectors + * other rows of the simplex matrix are calculated. These difference vectors * are stored in a matrix, which is one horizontal slice of the 3D matrix. * * @param[in,out] model the corresponding GenModel @@ -113,11 +113,11 @@ void gensvm_simplex_diff(struct GenModel *model, struct GenData *data) /** * @brief Calculate the scalar errors - * + * * @details * Calculate the scalar errors q based on the current estimate of V, and - * store these in Q. It is assumed that the memory for Q has already been - * allocated. In addition, the matrix ZV is calculated here. It is assigned + * store these in Q. It is assumed that the memory for Q has already been + * allocated. In addition, the matrix ZV is calculated here. It is assigned * to a pre-allocated block of memory, which is passed to this function. * * @param[in,out] model the corresponding GenModel @@ -164,16 +164,16 @@ void gensvm_calculate_errors(struct GenModel *model, struct GenData *data, } } } -} +} /** * @brief Calculate the Huber hinge errors * * @details - * For each of the scalar errors in Q the Huber hinge errors are - * calculated. The Huber hinge is here defined as + * For each of the scalar errors in Q the Huber hinge errors are + * calculated. The Huber hinge is here defined as * @f[ - * h(q) = + * h(q) = * \begin{dcases} * 1 - q - \frac{\kappa + 1}{2} & \text{if } q \leq -\kappa \\ * \frac{1}{2(\kappa + 1)} ( 1 - q)^2 & \text{if } q \in (-\kappa, 1] \\ @@ -183,7 +183,7 @@ void gensvm_calculate_errors(struct GenModel *model, struct GenData *data, * * @param[in,out] model the corresponding GenModel */ -void gensvm_calculate_huber(struct GenModel *model) +void gensvm_calculate_huber(struct GenModel *model) { long i, j; double q, value; @@ -206,10 +206,10 @@ void gensvm_calculate_huber(struct GenModel *model) * @brief seed the matrix V from an existing model or using rand * * @details - * The matrix V must be seeded before the main_loop() can start. - * This can be done by either seeding it with random numbers or + * The matrix V must be seeded before the main_loop() can start. + * This can be done by either seeding it with random numbers or * using the solution from a previous model on the same dataset - * as initial seed. The latter option usually allows for a + * as initial seed. The latter option usually allows for a * significant improvement in the number of iterations necessary * because the seeded model V is closer to the optimal V. * @@ -221,11 +221,11 @@ void gensvm_seed_model_V(struct GenModel *from_model, { long i, j, k; double cmin, cmax, value; - + long n = data->n; long m = data->m; long K = data->K; - + if (from_model == NULL) { for (i=0; i<m+1; i++) { cmin = 1e100; @@ -243,7 +243,7 @@ void gensvm_seed_model_V(struct GenModel *from_model, } } } else { - for (i=0; i<m+1; i++) + for (i=0; i<m+1; i++) for (j=0; j<K-1; j++) { value = matrix_get(from_model->V, K-1, i, j); matrix_set(to_model->V, K-1, i, j, value); @@ -255,7 +255,7 @@ void gensvm_seed_model_V(struct GenModel *from_model, * @brief Use step doubling * * @details - * Step doubling can be used to speed up the maorization algorithm. Instead of + * Step doubling can be used to speed up the maorization algorithm. Instead of * using the value at the minimimum of the majorization function, the value * ``opposite'' the majorization point is used. This can essentially cut the * number of iterations necessary to reach the minimum in half. @@ -283,10 +283,10 @@ void gensvm_step_doubling(struct GenModel *model) * @brief Initialize instance weights * * @details - * Instance weights can for instance be used to add additional weights to + * Instance weights can for instance be used to add additional weights to * instances of certain classes. Two default weight possibilities are - * implemented here. The first is unit weights, where each instance gets - * weight 1. + * implemented here. The first is unit weights, where each instance gets + * weight 1. * * The second are group size correction weights, which are calculated as * @f[ @@ -296,8 +296,8 @@ void gensvm_step_doubling(struct GenModel *model) * @f$ y_i = k @f$. * * @param[in] data GenData with the dataset - * @param[in,out] model GenModel with the weight specification. On - * exit GenModel::rho contains the instance + * @param[in,out] model GenModel with the weight specification. On + * exit GenModel::rho contains the instance * weights. */ void gensvm_initialize_weights(struct GenData *data, struct GenModel *model) @@ -311,12 +311,12 @@ void gensvm_initialize_weights(struct GenData *data, struct GenModel *model) if (model->weight_idx == 1) { for (i=0; i<n; i++) model->rho[i] = 1.0; - } + } else if (model->weight_idx == 2) { groups = Calloc(long, K); - for (i=0; i<n; i++) + for (i=0; i<n; i++) groups[data->y[i]-1]++; - for (i=0; i<n; i++) + for (i=0; i<n; i++) model->rho[i] = ((double) n)/((double) (groups[data->y[i]-1]*K)); } else { fprintf(stderr, "Unknown weight specification.\n"); |