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-rw-r--r--src/libGenSVM.c304
1 files changed, 0 insertions, 304 deletions
diff --git a/src/libGenSVM.c b/src/libGenSVM.c
index 8c22f3e..8917a3e 100644
--- a/src/libGenSVM.c
+++ b/src/libGenSVM.c
@@ -20,308 +20,4 @@
#include "gensvm.h"
#include "gensvm_matrix.h"
-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
- * 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.
- *
- * @param[in] K number of classes
- * @param[in,out] U simplex matrix of size K * (K-1)
- */
-void gensvm_simplex_gen(long K, double *U)
-{
- long i, j;
- for (i=0; i<K; i++) {
- for (j=0; j<K-1; j++) {
- if (i <= j) {
- matrix_set(U, K-1, i, j, -1.0/sqrt(2.0*(j+1)*(j+2)));
- } else if (i == j+1) {
- matrix_set(U, K-1, i, j, sqrt((j+1)/(2.0*(j+2))));
- } else {
- matrix_set(U, K-1, i, j, 0.0);
- }
- }
- }
-}
-
-/**
- * @brief Generate the category matrix
- *
- * @details
- * Generate the category matrix R. The category matrix has 1's everywhere
- * except at the column corresponding to the label of instance i, there the
- * element is 0.
- *
- * @param[in,out] model corresponding GenModel
- * @param[in] dataset corresponding GenData
- *
- */
-void gensvm_category_matrix(struct GenModel *model, struct GenData *dataset)
-{
- long i, j;
- long n = model->n;
- long K = model->K;
-
- for (i=0; i<n; i++) {
- for (j=0; j<K; j++) {
- if (dataset->y[i] != j+1)
- matrix_set(model->R, K, i, j, 1.0);
- else
- matrix_set(model->R, K, i, j, 0.0);
- }
- }
-}
-
-/**
- * @brief Generate the simplex difference matrix
- *
- * @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
- * the simplex matrix corresponding to the class label of instance i and the
- * 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
- * @param[in] data the corresponding GenData
- *
- */
-void gensvm_simplex_diff(struct GenModel *model, struct GenData *data)
-{
- long i, j, k;
- double value;
-
- long n = model->n;
- long K = model->K;
-
- for (i=0; i<n; i++) {
- for (j=0; j<K-1; j++) {
- for (k=0; k<K; k++) {
- value = matrix_get(model->U, K-1, data->y[i]-1, j);
- value -= matrix_get(model->U, K-1, k, j);
- matrix3_set(model->UU, K-1, K, i, j, k, value);
- }
- }
- }
-}
-
-/**
- * @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
- * to a pre-allocated block of memory, which is passed to this function.
- *
- * @param[in,out] model the corresponding GenModel
- * @param[in] data the corresponding GenData
- * @param[in,out] ZV a pointer to a memory block for ZV. On exit
- * this block is updated with the new ZV matrix
- * calculated with GenModel::V.
- *
- */
-void gensvm_calculate_errors(struct GenModel *model, struct GenData *data,
- double *ZV)
-{
- long i, j, k;
- double a, value;
-
- long n = model->n;
- long m = model->m;
- long K = model->K;
-
- cblas_dgemm(
- CblasRowMajor,
- CblasNoTrans,
- CblasNoTrans,
- n,
- K-1,
- m+1,
- 1.0,
- data->Z,
- m+1,
- model->V,
- K-1,
- 0.0,
- ZV,
- K-1);
-
- Memset(model->Q, double, n*K);
- for (i=0; i<n; i++) {
- for (j=0; j<K-1; j++) {
- a = matrix_get(ZV, K-1, i, j);
- for (k=0; k<K; k++) {
- value = a * matrix3_get(model->UU, K-1, K, i,
- j, k);
- matrix_add(model->Q, K, i, k, value);
- }
- }
- }
-}
-
-/**
- * @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
- * @f[
- * 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] \\
- * 0 & \text{if } q > 1
- * \end{dcases}
- * @f]
- *
- * @param[in,out] model the corresponding GenModel
- */
-void gensvm_calculate_huber(struct GenModel *model)
-{
- long i, j;
- double q, value;
-
- for (i=0; i<model->n; i++) {
- for (j=0; j<model->K; j++) {
- q = matrix_get(model->Q, model->K, i, j);
- value = 0.0;
- if (q <= -model->kappa) {
- value = 1.0 - q - (model->kappa+1.0)/2.0;
- } else if (q <= 1.0) {
- value = 1.0/(2.0*model->kappa+2.0)*pow(1.0 - q, 2.0);
- }
- matrix_set(model->H, model->K, i, j, value);
- }
- }
-}
-
-/**
- * @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
- * using the solution from a previous model on the same dataset
- * 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.
- *
- * @param[in] from_model GenModel from which to copy V
- * @param[in,out] to_model GenModel to which V will be copied
- */
-void gensvm_seed_model_V(struct GenModel *from_model,
- struct GenModel *to_model, struct GenData *data)
-{
- 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;
- cmax = -1e100;
- for (k=0; k<n; k++) {
- value = matrix_get(data->Z, m+1, k, i);
- cmin = minimum(cmin, value);
- cmax = maximum(cmax, value);
- }
- for (j=0; j<K-1; j++) {
- cmin = (abs(cmin) < 1e-10) ? -1 : cmin;
- cmax = (abs(cmax) < 1e-10) ? 1 : cmax;
- value = 1.0/cmin + (1.0/cmax - 1.0/cmin)*rnd();
- matrix_set(to_model->V, K-1, i, j, value);
- }
- }
- } else {
- 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);
- }
- }
-}
-
-/**
- * @brief Use step doubling
- *
- * @details
- * 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.
- *
- * @param[in] model GenModel containing the augmented parameters
- */
-void gensvm_step_doubling(struct GenModel *model)
-{
- long i, j;
- double value;
-
- long m = model->m;
- long K = model->K;
-
- for (i=0; i<m+1; i++) {
- for (j=0; j<K-1; j++) {
- matrix_mul(model->V, K-1, i, j, 2.0);
- value = - matrix_get(model->Vbar, K-1, i, j);
- matrix_add(model->V, K-1, i, j, value);
- }
- }
-}
-
-/**
- * @brief Initialize instance weights
- *
- * @details
- * 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.
- *
- * The second are group size correction weights, which are calculated as
- * @f[
- * \rho_i = \frac{n}{Kn_k} ,
- * @f]
- * where @f$ n_k @f$ is the number of instances in group @f$ k @f$ and
- * @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
- * weights.
- */
-void gensvm_initialize_weights(struct GenData *data, struct GenModel *model)
-{
- long *groups;
- long i;
-
- long n = model->n;
- long K = model->K;
-
- 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++)
- groups[data->y[i]-1]++;
- 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");
- exit(1);
- }
-}
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