diff options
Diffstat (limited to 'src/GenSVMtraintest.c')
| -rw-r--r-- | src/GenSVMtraintest.c | 20 |
1 files changed, 20 insertions, 0 deletions
diff --git a/src/GenSVMtraintest.c b/src/GenSVMtraintest.c index 40c4321..ecf455b 100644 --- a/src/GenSVMtraintest.c +++ b/src/GenSVMtraintest.c @@ -135,6 +135,16 @@ int main(int argc, char **argv) model->data_file = Calloc(char, GENSVM_MAX_LINE_LENGTH); strcpy(model->data_file, training_inputfile); + // check if we are sparse and want nonlinearity + if (traindata->Z == NULL && model->kerneltype != K_LINEAR) { + err("[GenSVM Warning]: Sparse matrices with nonlinear kernels " + "are not yet supported. Dense matrices will " + "be used.\n"); + traindata->RAW = gensvm_sparse_to_dense(traindata->spZ); + traindata->Z = traindata->RAW; + gensvm_free_sparse(traindata->spZ); + } + // seed the random number generator srand(time(NULL)); @@ -151,6 +161,16 @@ int main(int argc, char **argv) // to an output file if specified if (testing_inputfile != NULL) { gensvm_read_data(testdata, testing_inputfile); + + // check if we are sparse and want nonlinearity + if (testdata->Z == NULL && model->kerneltype != K_LINEAR) { + err("[GenSVM Warning]: Sparse matrices with nonlinear " + "kernels are not yet supported. Dense " + "matrices will be used.\n"); + testdata->Z = gensvm_sparse_to_dense(testdata->spZ); + gensvm_free_sparse(testdata->spZ); + } + gensvm_kernel_postprocess(model, traindata, testdata); // predict labels |