/// <summary>
/// wrapper function for GetAccuracy, the name was not logical and i have to maintain a subversion pristine copy.
/// </summary>
/// <param name="problem">problem</param>
/// <returns>accuracy in %</returns>
///
public double GetAccuracyFromTestSingleSample(libSVM_Problem problem, libSVM svmModel)
{
if (problem.samples == null || problem.samples.Length == 0)
{
throw new Exception("GetAccuracy no samples");
}
if (problem.labels == null || problem.labels.Length == 0)
{
throw new Exception("GetAccuracy no labels");
}
if (problem.samples.Length != problem.labels.Length)
{
throw new Exception("GetAccuracy labels.length!=samples.length");
}
output = new libSVM_output();
int correctPredictions = 0;
double pred;
int index = problem.samples.Length - 1;
if (problem.samples[index] != null && problem.samples[index].Count > 0)
{
pred = svmModel.Predict(problem.samples[index]);
return(pred);
/*output.setValue(index, pred);
* if (pred == problem.labels[index])
* correctPredictions++;*/
}
output.accuracy = correctPredictions / (double)problem.samples.Length * 100.0;
return(1.0);
}
/// <summary>
/// GetAccuracy for problem, returning predicted labels.
/// </summary>
/// <param name="problem">problem</param>
/// <returns>accuracy in %</returns>
///
new public void GetAccuracy(libSVM_Problem problem)
{
if (problem.samples == null || problem.samples.Length == 0)
{
throw new Exception("GetAccuracy no samples");
}
if (problem.labels == null || problem.labels.Length == 0)
{
throw new Exception("GetAccuracy no labels");
}
if (problem.samples.Length != problem.labels.Length)
{
throw new Exception("GetAccuracy labels.length!=samples.length");
}
output = new libSVM_output();
int correctPredictions = 0;
double pred;
for (int i = 0; i < problem.samples.Length; i++)
{
if (problem.samples[i] != null && problem.samples[i].Count > 0)
{
pred = Predict(problem.samples[i]);
output.setValue(i, pred);
if (pred == problem.labels[i])
{
correctPredictions++;
}
}
}
output.accuracy = correctPredictions / (double)problem.samples.Length * 100.0;
}
/// <summary>
/// Trains model automatically using users grids. if your svm_type/kernel_type does not need it then set it to null
/// </summary>
/// <param name="fold">folds for samples >=2 </param>
/// <param name="problem">samples and labels</param>
/// <param name="parameter">model parameters</param>
/// <param name="grid_c">grid for C</param>
/// <param name="grid_gamma">grid for gamma</param>
/// <param name="grid_p">grid for p</param>
/// <param name="grid_nu">grid for nu</param>
/// <param name="grid_coef0">grid for coef</param>
/// <param name="grid_degree">grid for degree</param>
/// <returns>Total Accuracy in %</returns>
public void TrainAutoBestTotal(int fold, libSVM_Problem problem, libSVM_Parameter parameter, libSVM_Grid grid_c, libSVM_Grid grid_gamma, libSVM_Grid grid_p, libSVM_Grid grid_nu, libSVM_Grid grid_coef0, libSVM_Grid grid_degree)
{
Dispose();
if (problem == null)
{
throw new Exception("libSVMProblem not initialized");
}
if (problem.samples == null)
{
throw new Exception("libSVMProblem.samples = null");
}
if (problem.labels == null)
{
throw new Exception("libSVMProblem.labels = null");
}
if (problem.samples.Length != problem.labels.Length)
{
throw new Exception("libSVMProblem.samples.Length != libSVMProblem.labels.length");
}
if (parameter == null)
{
throw new Exception("libSVMParameter not initialized");
}
if (parameter.weight == null && parameter.weight_label != null)
{
throw new Exception("libSVMParameter.weight = null and libSVMParameter.weight_label != null");
}
if (parameter.weight != null && parameter.weight_label == null)
{
throw new Exception("libSVMParameter.weight_label = null and libSVMParameter.weight != null");
}
if (parameter.weight != null && parameter.weight_label != null && parameter.weight_label.Length != parameter.weight.Length)
{
throw new Exception("libSVMParameter.weight_label.Length != libSVMParameter.weight.Length");
}
if (fold < 2 || fold > problem.samples.Length)
{
throw new Exception("fold < 2 || fold > nr_samples");
}
libSVM_Grid _grid_gamma = grid_gamma;
libSVM_Grid _grid_coef0 = grid_coef0;
libSVM_Grid _grid_degree = grid_degree;
if (parameter.kernel_type == KERNEL_TYPE.LINEAR ||
parameter.kernel_type == KERNEL_TYPE.PRECOMPUTED)
{
_grid_gamma = new libSVM_Grid();
_grid_coef0 = new libSVM_Grid();
_grid_degree = new libSVM_Grid();
}
else if (parameter.kernel_type == KERNEL_TYPE.POLY)
{
if (_grid_gamma == null)
{
throw new Exception("grid_gamma not set");
}
if (_grid_coef0 == null)
{
throw new Exception("grid_coef0 not set");
}
if (_grid_degree == null)
{
throw new Exception("grid_degree not set");
}
}
else if (parameter.kernel_type == KERNEL_TYPE.RBF)
{
if (_grid_gamma == null)
{
throw new Exception("grid_gamma not set");
}
_grid_coef0 = new libSVM_Grid();
_grid_degree = new libSVM_Grid();
}
else if (parameter.kernel_type == KERNEL_TYPE.SIGMOID)
{
if (_grid_gamma == null)
{
throw new Exception("grid_gamma not set");
}
if (_grid_coef0 == null)
{
throw new Exception("grid_coef not set");
}
_grid_degree = new libSVM_Grid();
}
else
{
throw new Exception("unknown kernel_type");
}
libSVM_Grid _grid_c = grid_c;
libSVM_Grid _grid_p = grid_p;
libSVM_Grid _grid_nu = grid_nu;
if (parameter.svm_type == SVM_TYPE.C_SVC ||
parameter.svm_type == SVM_TYPE.ONE_CLASS)
{
if (_grid_c == null)
{
throw new Exception("grid_C not set");
}
_grid_nu = new libSVM_Grid();
_grid_p = new libSVM_Grid();
}
else if (parameter.svm_type == SVM_TYPE.NU_SVC || parameter.svm_type == SVM_TYPE.NU_SVR)
{
if (_grid_nu == null)
{
throw new Exception("grid_nu not set");
}
_grid_c = new libSVM_Grid();
_grid_p = new libSVM_Grid();
}
else if (parameter.svm_type == SVM_TYPE.EPSILON_SVR)
{
if (_grid_p == null)
{
throw new Exception("grid_p not set");
}
_grid_c = new libSVM_Grid();
_grid_nu = new libSVM_Grid();
}
else
{
throw new Exception("unknown svm_type");
}
double c = _grid_c.min;
double p = _grid_p.min;
double nu = _grid_nu.min;
double gamma = _grid_gamma.min;
double coef0 = _grid_coef0.min;
double degree = _grid_degree.min;
int f = 0;
int test_probe = 0;
int test_samples = 0;
//ori
int total_test_probe = 0;
int total_test_samples = 0;
libSVM_Parameter train_parameter = new libSVM_Parameter();
//copy parameters set by user;
train_parameter.weight = parameter.weight;
train_parameter.weight_label = parameter.weight_label;
train_parameter.shrinking = parameter.shrinking;
train_parameter.svm_type = parameter.svm_type;
train_parameter.kernel_type = parameter.kernel_type;
train_parameter.probability = parameter.probability;
train_parameter.cache_size = parameter.cache_size;
libSVM_Problem train_problem = new libSVM_Problem();
libSVM_Problem test_problem = new libSVM_Problem();
//ori
libSVM_output this_output = new libSVM_output();
//fold
do
{
train_problem.labels = new double[1];
train_problem.samples = new SortedDictionary <int, double> [1];
test_problem.labels = new double[1];
test_problem.samples = new SortedDictionary <int, double> [1];
int j = 0;
int k = 0;
for (int i = 0; i < problem.samples.Length; i++)
{
if ((i + 1 + f) % fold == 0)
{
Array.Resize(ref test_problem.labels, j + 1);
Array.Resize(ref test_problem.samples, j + 1);
this_output.setValue(i, 999);
test_problem.labels[j] = problem.labels[i];
test_problem.samples[j] = problem.samples[i];
j++;
}
else
{
Array.Resize(ref train_problem.labels, k + 1);
Array.Resize(ref train_problem.samples, k + 1);
train_problem.labels[k] = problem.labels[i];
train_problem.samples[k] = problem.samples[i];
k++;
}
}
//p
p = _grid_p.min;
do
{
//nu
nu = _grid_nu.min;
do
{
//gamma
gamma = _grid_gamma.min;
do
{
//coef0
coef0 = _grid_coef0.min;
do
{
//degree
degree = _grid_degree.min;
do
{
//c
c = _grid_c.min;
do
{
//svm_train alters problem_ptr so it's necessary to create it every time;
IntPtr this_problem_ptr = libSVM_Problem_to_svm_problem_ptr(train_problem);
//set generated parameters
train_parameter.C = c;
train_parameter.p = p;
train_parameter.nu = nu;
train_parameter.gamma = gamma;
train_parameter.degree = (int)degree;
train_parameter.coef0 = coef0;
IntPtr this_parameter_ptr = libSVM_Parameter_to_svm_parameter_ptr(train_parameter);
IntPtr error_ptr = svm_check_parameter(this_problem_ptr, this_parameter_ptr);
if (error_ptr != IntPtr.Zero)
{
throw new Exception(ptr_to_string(error_ptr));
}
IntPtr this_model_ptr = svm_train(this_problem_ptr, this_parameter_ptr);
int this_test_probe = 0;
//count propperly recognized test_problem.samples
for (int i = 0; i < test_problem.labels.Length; i++)
{
IntPtr svm_nodes_ptr = sample_to_svm_nodes_ptr(test_problem.samples[i]);
//ori
double testPrediction = svm_predict(this_model_ptr, svm_nodes_ptr);
this_output.setValue(this_output.getKeyByIndex(i), testPrediction);
total_test_samples++; // can also be problem.labels.length
if (test_problem.labels[i] == testPrediction)
{
this_test_probe++;
total_test_probe++;
}
Free_ptr(ref svm_nodes_ptr);
}
//if first run then just copy this
if (_model_ptr == IntPtr.Zero)
{
_model_ptr = this_model_ptr;
_parameter_ptr = this_parameter_ptr;
_problem_ptr = this_problem_ptr;
test_probe = this_test_probe;
test_samples = test_problem.samples.Length;
//ori
output = this_output;
//!-uncommenting this gets mapping of predicted labels only for best model
this_output = new libSVM_output();
//!-uncommenting this gets only best model and not runs on all 10 folds (unless best is 10th)
//exit when no better solution could be found
if (test_probe == test_problem.samples.Length)
{
goto leave;
}
}
else
//if model was better than previous then free previous data and copy this
if (this_test_probe > test_probe)
{
Free_svm_parameter_ptr(ref _parameter_ptr);
Free_svm_problem_ptr(ref _problem_ptr);
svm_free_and_destroy_model(ref _model_ptr);
_parameter_ptr = this_parameter_ptr;
_problem_ptr = this_problem_ptr;
_model_ptr = this_model_ptr;
test_probe = this_test_probe;
test_samples = test_problem.samples.Length;
//ori
output = this_output;
//!-uncommenting this gets mapping of predicted labels only for best model
this_output = new libSVM_output();
//!-uncommenting this gets only best model and not runs on all 10 folds (unless best is 10th)
//exit when no better solution could be found;
if (test_probe == test_problem.samples.Length)
{
goto leave;
}
}
//if not then free this model
else
{
Free_svm_problem_ptr(ref this_problem_ptr);
Free_svm_parameter_ptr(ref this_parameter_ptr);
svm_free_and_destroy_model(ref this_model_ptr);
//!-uncommenting this gets mapping of predicted labels only for best model
this_output = new libSVM_output();
}
c *= _grid_c.step;
} while (c < _grid_c.max);
degree *= _grid_degree.step;
} while (degree < _grid_degree.max);
coef0 *= _grid_coef0.step;
} while (coef0 < _grid_coef0.max);
gamma *= _grid_gamma.step;
} while (gamma < _grid_gamma.max);
nu *= _grid_nu.step;
} while (nu < _grid_nu.max);
p *= _grid_p.step;
} while (p < _grid_p.max);
f++;
} while (f < fold);
leave:
//return new double []{100.0 * test_probe / (double) test_samples,100.0 * total_test_probe / (double)total_test_samples};
output.accuracy = 100.0 * test_probe / (double)test_samples;
return;
}
