void LowessSmoothing::smoothData(const DoubleVector& input_x, const DoubleVector& input_y, DoubleVector& smoothed_output)
{
if (input_x.size() != input_y.size())
{
throw Exception::InvalidValue(__FILE__, __LINE__, OPENMS_PRETTY_FUNCTION,
"Sizes of x and y values not equal! Aborting... ", String(input_x.size()));
}
// unable to smooth over 2 or less data points (we need at least 3)
if (input_x.size() <= 2)
{
smoothed_output = input_y;
return;
}
Size input_size = input_y.size();
// const Size q = floor( input_size * alpha );
const Size q = (window_size_ < input_size) ? static_cast<Size>(window_size_) : input_size - 1;
DoubleVector distances(input_size, 0.0);
DoubleVector sortedDistances(input_size, 0.0);
for (Size outer_idx = 0; outer_idx < input_size; ++outer_idx)
{
// Compute distances.
// Size inner_idx = 0;
for (Size inner_idx = 0; inner_idx < input_size; ++inner_idx)
{
distances[inner_idx] = std::fabs(input_x[outer_idx] - input_x[inner_idx]);
sortedDistances[inner_idx] = distances[inner_idx];
}
// Sort distances in order from smallest to largest.
std::sort(sortedDistances.begin(), sortedDistances.end());
// Compute weigths.
std::vector<double> weigths(input_size, 0);
for (Size inner_idx = 0; inner_idx < input_size; ++inner_idx)
{
weigths.at(inner_idx) = tricube_(distances[inner_idx], sortedDistances[q]);
}
//calculate regression
Math::QuadraticRegression qr;
std::vector<double>::const_iterator w_begin = weigths.begin();
qr.computeRegressionWeighted(input_x.begin(), input_x.end(), input_y.begin(), w_begin);
//smooth y-values
double rt = input_x[outer_idx];
smoothed_output.push_back(qr.eval(rt));
}
return;
}
void LowessSmoothing::smoothData(const DoubleVector & input_x, const DoubleVector & input_y, DoubleVector & smoothed_output)
{
if (input_x.size() != input_y.size())
{
throw Exception::InvalidValue(__FILE__, __LINE__, __PRETTY_FUNCTION__, "Sizes of x and y values not equal! Aborting... ", String(input_x.size()));
}
// unable to smooth over 2 or less data points (we need at least 3)
if (input_x.size() <= 2)
{
smoothed_output = input_y;
return;
}
Size input_size = input_y.size();
// alpha_ = 1 / ( input_size / window_size_ );
// const Size q = floor( input_size * alpha );
const Size q = (window_size_ < input_size) ? window_size_ : input_size - 1;
DoubleVector distances(input_size, 0.0);
DoubleVector sortedDistances(input_size, 0.0);
// DoubleVector smooth_yvals_Vec(input_size);
gsl_matrix * X = gsl_matrix_alloc(input_size, 3);
gsl_matrix * cov = gsl_matrix_alloc(3, 3);
gsl_vector * weights = gsl_vector_alloc(input_size);
gsl_vector * c = gsl_vector_alloc(3);
gsl_vector * x = gsl_vector_alloc(3);
gsl_vector * yvals_ = gsl_vector_alloc(input_size);
DoubleReal y, yErr, chisq;
// Setup the model matrix X for a quadratic fit.
// yvals_ = new double[input_size];
// Size idx = 0;
for (Size p_idx = 0; p_idx < input_y.size(); ++p_idx)
{
DoubleReal rt = input_x[p_idx];
gsl_matrix_set(X, p_idx, 0, 1.0);
gsl_matrix_set(X, p_idx, 1, rt);
gsl_matrix_set(X, p_idx, 2, rt * rt);
gsl_vector_set(yvals_, p_idx, input_y[p_idx]);
// ++idx;
}
//for(DoubleVector::const_iterator outer_peak_it = input_y.begin(); outer_peak_it != input_y.end(); ++outer_peak_it )
for (Size outer_idx = 0; outer_idx < input_y.size(); ++outer_idx)
{
// Compute distances.
// Size inner_idx = 0;
for (Size inner_idx = 0; inner_idx < input_y.size(); ++inner_idx)
{
distances[inner_idx] = std::fabs(input_x[outer_idx] - input_x[inner_idx]);
sortedDistances[inner_idx] = distances[inner_idx];
// ++inner_idx;
}
// Sort distances in order from smallest to largest.
// std::sort(sortedDistances, sortedDistances + input_size);
std::sort(sortedDistances.begin(), sortedDistances.end());
// Compute weights.
for (Size inner_idx = 0; inner_idx < input_size; ++inner_idx)
{
gsl_vector_set(weights, inner_idx, tricube_(distances[inner_idx], sortedDistances[q]));
}
gsl_multifit_linear_workspace * work = gsl_multifit_linear_alloc(input_size, 3);
gsl_multifit_wlinear(X, weights, yvals_, c, cov, &chisq, work);
gsl_multifit_linear_free(work);
DoubleReal rt = input_x[outer_idx];
gsl_vector_set(x, 0, 1.0);
gsl_vector_set(x, 1, rt);
gsl_vector_set(x, 2, rt * rt);
gsl_multifit_linear_est(x, c, cov, &y, &yErr);
smoothed_output.push_back(y);
}
gsl_matrix_free(X);
gsl_vector_free(weights);
gsl_vector_free(c);
gsl_matrix_free(cov);
return;
}
