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-rw-r--r--src/gensvm_kernel.c54
1 files changed, 27 insertions, 27 deletions
diff --git a/src/gensvm_kernel.c b/src/gensvm_kernel.c
index a6bc9fc..f53bcce 100644
--- a/src/gensvm_kernel.c
+++ b/src/gensvm_kernel.c
@@ -5,7 +5,7 @@
* @brief Defines main functions for use of kernels in GenSVM.
*
* @details
- * Functions for constructing different kernels using user-supplied
+ * Functions for constructing different kernels using user-supplied
* parameters. Also contains the functions for decomposing the
* kernel matrix using several decomposition methods.
*
@@ -34,7 +34,7 @@ void gensvm_kernel_preprocess(struct GenModel *model, struct GenData *data)
return;
}
- int i;
+ int i;
long r,
n = data->n;
double *P = NULL,
@@ -55,7 +55,7 @@ void gensvm_kernel_preprocess(struct GenModel *model, struct GenData *data)
// build M and set to data (leave RAW intact)
gensvm_make_trainfactor(data, P, Sigma, r);
-
+
// Set Sigma to data->Sigma (need it again for prediction)
if (data->Sigma != NULL)
free(data->Sigma);
@@ -81,7 +81,7 @@ void gensvm_kernel_preprocess(struct GenModel *model, struct GenData *data)
data->kernelparam[0] = model->kernelparam[0];
data->kernelparam[1] = model->kernelparam[1];
}
-
+
free(K);
free(P);
}
@@ -117,13 +117,13 @@ void gensvm_make_kernel(struct GenModel *model, struct GenData *data,
x1 = &data->RAW[i*(data->m+1)+1];
x2 = &data->RAW[j*(data->m+1)+1];
if (model->kerneltype == K_POLY)
- value = gensvm_dot_poly(x1, x2,
+ value = gensvm_dot_poly(x1, x2,
model->kernelparam, data->m);
else if (model->kerneltype == K_RBF)
- value = gensvm_dot_rbf(x1, x2,
+ value = gensvm_dot_rbf(x1, x2,
model->kernelparam, data->m);
else if (model->kerneltype == K_SIGMOID)
- value = gensvm_dot_sigmoid(x1, x2,
+ value = gensvm_dot_sigmoid(x1, x2,
model->kernelparam, data->m);
else {
fprintf(stderr, "Unknown kernel type in "
@@ -154,11 +154,11 @@ long gensvm_make_eigen(double *K, long n, double **P, double **Sigma)
IWORK = Malloc(int, 5*n);
IFAIL = Malloc(int, n);
-
- // highest precision eigenvalues, may reduce for speed
+
+ // highest precision eigenvalues, may reduce for speed
abstol = 2.0*dlamch('S');
- // first perform a workspace query to determine optimal size of the
+ // first perform a workspace query to determine optimal size of the
// WORK array.
WORK = Malloc(double, 1);
status = dsyevx(
@@ -183,7 +183,7 @@ long gensvm_make_eigen(double *K, long n, double **P, double **Sigma)
IFAIL);
LWORK = WORK[0];
- // allocate the requested memory for the eigendecomposition
+ // allocate the requested memory for the eigendecomposition
WORK = (double *)realloc(WORK, LWORK*sizeof(double));
status = dsyevx(
'V',
@@ -211,7 +211,7 @@ long gensvm_make_eigen(double *K, long n, double **P, double **Sigma)
exit(1);
}
- // Select the desired number of eigenvalues, depending on their size.
+ // Select the desired number of eigenvalues, depending on their size.
// dsyevx sorts eigenvalues in ascending order.
max_eigen = tempSigma[n-1];
cutoff_idx = 0;
@@ -223,23 +223,23 @@ long gensvm_make_eigen(double *K, long n, double **P, double **Sigma)
}
num_eigen = n - cutoff_idx;
-
+
*Sigma = Calloc(double, num_eigen);
-
+
for (i=0; i<num_eigen; i++) {
(*Sigma)[i] = tempSigma[n-1 - i];
}
- // revert P to row-major order and copy only the the columns
+ // revert P to row-major order and copy only the the columns
// corresponding to the selected eigenvalues
- *P = Calloc(double, n*num_eigen);
+ *P = Calloc(double, n*num_eigen);
for (j=n-1; j>n-1-num_eigen; j--) {
for (i=0; i<n; i++) {
(*P)[i*num_eigen + (n-1)-j] = tempP[i + j*n];
}
}
- free(tempSigma);
+ free(tempSigma);
free(tempP);
free(IWORK);
free(IFAIL);
@@ -322,7 +322,7 @@ void gensvm_make_testfactor(struct GenData *testdata,
double value,
*N = NULL,
*M = NULL;
-
+
n1 = traindata->n;
n2 = testdata->n;
r = traindata->r;
@@ -340,7 +340,7 @@ void gensvm_make_testfactor(struct GenData *testdata,
exit(1);
}
- // copy M from traindata->Z because we need it in dgemm without column
+ // copy M from traindata->Z because we need it in dgemm without column
// of 1's.
for (i=0; i<n1; i++)
for (j=0; j<r; j++)
@@ -370,7 +370,7 @@ void gensvm_make_testfactor(struct GenData *testdata,
for (i=0; i<n2; i++)
matrix_mul(N, r, i, j, value);
}
-
+
// write N to Z with a column of ones
testdata->Z = Calloc(double, n2*(r+1));
if (testdata->Z == NULL) {
@@ -380,7 +380,7 @@ void gensvm_make_testfactor(struct GenData *testdata,
}
for (i=0; i<n2; i++) {
for (j=0; j<r; j++) {
- matrix_set(testdata->Z, r+1, i, j+1,
+ matrix_set(testdata->Z, r+1, i, j+1,
matrix_get(N, r, i, j));
}
matrix_set(testdata->Z, r+1, i, 0, 1.0);
@@ -394,7 +394,7 @@ void gensvm_make_testfactor(struct GenData *testdata,
/**
* @brief Compute the RBF kernel between two vectors
- *
+ *
* @details
* The RBF kernel is computed between two vectors. This kernel is defined as
* @f[
@@ -404,7 +404,7 @@ void gensvm_make_testfactor(struct GenData *testdata,
*
* @param[in] x1 first vector
* @param[in] x2 second vector
- * @param[in] kernelparam array of kernel parameters (gamma is first
+ * @param[in] kernelparam array of kernel parameters (gamma is first
* element)
* @param[in] n length of the vectors x1 and x2
* @returns kernel evaluation
@@ -413,8 +413,8 @@ double gensvm_dot_rbf(double *x1, double *x2, double *kernelparam, long n)
{
long i;
double value = 0.0;
-
- for (i=0; i<n; i++)
+
+ for (i=0; i<n; i++)
value += (x1[i] - x2[i]) * (x1[i] - x2[i]);
value *= -kernelparam[0];
return exp(value);
@@ -424,7 +424,7 @@ double gensvm_dot_rbf(double *x1, double *x2, double *kernelparam, long n)
* @brief Compute the polynomial kernel between two vectors
*
* @details
- * The polynomial kernel is computed between two vectors. This kernel is
+ * The polynomial kernel is computed between two vectors. This kernel is
* defined as
* @f[
* k(x_1, x_2) = ( \gamma \langle x_1, x_2 \rangle + c)^d
@@ -450,7 +450,7 @@ double gensvm_dot_poly(double *x1, double *x2, double *kernelparam, long n)
/**
* @brief Compute the sigmoid kernel between two vectors
- *
+ *
* @details
* The sigmoid kernel is computed between two vectors. This kernel is defined
* as
generated by cgit v1.2.3 (git 2.25.1) at 2026-01-11 09:37:42 +0000