apaam/mlpack
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Resolve Some final comments

Browse Source
This commit is contained in:
Yashwant
2019-10-05 19:18:04 +05:30
parent 932f75e0c7
commit cdb65fd5b2
1 changed files with 12 additions and 6 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/mlpack/methods/linear_svm/linear_svm_main.cpp
+12 -6
View File
@@ -108,7 +108,7 @@ PARAM_UROW_IN("labels", "A matrix containing labels (0 or 1) for the points "
"in the training set (y).", "l");
// Optimizer parameters.
PARAM_DOUBLE_IN("lambda", "L2-regularization parameter for training.", "L",
PARAM_DOUBLE_IN("lambda", "L2-regularization parameter for training.", "r",
0.0001);
PARAM_DOUBLE_IN("delta", "Margin of difference between correct class and other "
"classes.", "d", 1.0);
@@ -123,12 +123,12 @@ PARAM_DOUBLE_IN("tolerance", "Convergence tolerance for optimizer.", "e",
PARAM_INT_IN("max_iterations", "Maximum iterations for optimizer (0 indicates "
"no limit).", "n", 10000);
PARAM_DOUBLE_IN("step_size", "Step size for parallel SGD optimizer.",
"s", 0.01);
"a", 0.01);
PARAM_FLAG("shuffle", "Don't shuffle the order in which data points are "
"visited for parallel SGD.", "S");
PARAM_INT_IN("epochs", "Maximum number of full epochs over dataset for "
"psgd", "E", 50);
PARAM_INT_IN("seed", "Random seed. If 0, 'std::time(NULL)' is used.", "r", 0);
PARAM_INT_IN("seed", "Random seed. If 0, 'std::time(NULL)' is used.", "s", 0);
class LinearSVMModel
{
@@ -390,9 +390,12 @@ static void mlpackMain()
// Calculate accuracy, if desired.
if (CLI::HasParam("test_labels"))
{
arma::Row<size_t> testLabels =
arma::Row<size_t> testLabels;
arma::Row<size_t> testRawLabels =
std::move(CLI::GetParam<arma::Row<size_t>>("test_labels"));
data::NormalizeLabels(testRawLabels, testLabels, model->mappings);
if (testSet.n_cols != testLabels.n_elem)
{
Log::Fatal << "Test data given with " << PRINT_PARAM_STRING("test")
@@ -403,8 +406,11 @@ static void mlpackMain()
numClasses = CLI::GetParam<int>("num_classes") == 0 ?
model->mappings.n_elem : CLI::GetParam<int>("num_classes");
vector<size_t> correctClassCounts(numClasses, 0);
vector<size_t> labelSize(numClasses, 0);
arma::Col<size_t> correctClassCounts;
arma::Col<size_t> labelSize;
correctClassCounts.zeros(numClasses);
labelSize.zeros(numClasses);
for (arma::uword i = 0; i != predictions.n_elem; ++i)
{
if (predictions(i) == testLabels(i))