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bugfixes in reg and regsurf

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jiri committed 2026-10-29 22:48:21 +01:00
1 parent 8e82597927
commit 4f3ec9aef9
4 files changed
+140 -106
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reg.cc
+33 -43
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@@ -70,50 +70,32 @@ const T* Matern_comb_number)
unsigned int i;
unsigned int k;
T part_1 = 0; // sum(x^2)^1/2
T exponential_part;
T Matern_part = 0;
T result;
T z;
T Matern_part;
for (i = 0; i < dimensions; i++)
part_1 = part_1 + (x[i] * x[i]);
part_1 = std::pow(part_1, 0.5);
exponential_part = std::exp(-part_1/(sigma));
part_1 = std::sqrt(part_1);
z = 2 * part_1/sigma;
// Horner scheme for sum_{k=0}^{n} c_k z^(n-k)
Matern_part = Matern_comb_number[0];
for (k = 1; k <= n; k++)
Matern_part = Matern_part * z + Matern_comb_number[k];
for (k = 0; k < n; k++)
Matern_part += Matern_comb_number[i] * std::pow(2 * part_1/sigma, n - k);
result = exponential_part * Matern_part;
return(result);
return(std::exp(-part_1/sigma) * Matern_part);
}
template <typename T>
unsigned int Matern_coefficient(const unsigned int n, T* comb_number) // values of (n + k)!/(2 n)! * n!/(k!(n-k)!)
unsigned int Matern_coefficient(const unsigned int n, T* comb_number) // values of (n + k)!/(2 n)! * n!/(k!(n-k)!), k = 0..n
{
unsigned int i , j;
unsigned int k;
unsigned int part_1; // reverse value of (n + k)!/(2 n)!
unsigned int part_2; // n!/(n-k)!
unsigned int part_3; // k!
for (k = 0; k < n; k++)
{
part_1 = 1;
part_2 = 1;
part_3 = 1;
for (i = n + k + 1; i <= 2 * n; i++)
part_1 = part_1 * i;
for (i = n - k + 1; i <= n; i++)
part_2 = part_2 * i;
for (i = 1; i < k; i++)
part_3 = part_3 * i;
comb_number[k] = T(part_2)/T(part_1 * part_3);
}
// downward recurrence c_n = 1, c_{k-1} = c_k k/((n+k)(n-k+1)), no factorials (no overflow)
comb_number[n] = 1;
for (k = n; k > 0; k--)
comb_number[k - 1] = comb_number[k] * T(k)/(T(n + k) * T(n - k + 1));
return(0);
}
@@ -260,7 +242,7 @@ const string kernel)
{
for (i = 0; i < 4; i++)
REG_params[i] = params[i];
REG_Matern_comb_number_vec = NRVec<T>(REG_n);
REG_Matern_comb_number_vec = NRVec<T>(REG_n + 1);
REG_Matern_comb_number = REG_Matern_comb_number_vec.operator T*();
}
else
@@ -273,13 +255,15 @@ const string kernel)
}
}
// raw pointers to the data of the vectors below are cached for speed; the vectors must therefore never be
// reallocated: a |= b frees the old buffer of a (pointers would dangle), use a.storesubvector(0,b) (copy in place)
// copy data
REG_coordinates_vec = NRVec<T>(x, data_size * REG_count_dimensions);
REG_coordinates = REG_coordinates_vec.operator T*();
REG_y_vec = NRVec<T>(y , data_size);
REG_Y = REG_y_vec.operator T*();
// for fitting model
REG_distances_mat = NRMat<T>(data_size, data_size);
REG_distances_mat = NRMat<T>(data_size, data_size*REG_count_dimensions); //difference vectors of all pairs
REG_distances = REG_distances_mat.operator T*();
REG_kernels_mat = NRMat<T>(data_size, data_size);
REG_kernels = REG_kernels_mat.operator T*();
@@ -288,7 +272,7 @@ const string kernel)
REG_weights_vec = NRVec<T>(data_size);
REG_weights = REG_weights_vec.operator T*();
// for predictions
REG_coordinates_diff_vec = NRVec<T>(data_size);
REG_coordinates_diff_vec = NRVec<T>(data_size*REG_count_dimensions);
REG_coordinates_diff = REG_coordinates_diff_vec.operator T*();
REG_kernels_diff_vec = NRVec<T>(data_size);
REG_kernels_diff = REG_kernels_diff_vec.operator T*();
@@ -324,7 +308,7 @@ T REG<T>::Kernel(const T* x, const unsigned int dimensions, const unsigned int k
result = Laplacian_kernel(x, dimensions, REG_sigma);
break;
case 2:
result = Laplacian_kernel(x, dimensions, REG_sigma);
result = Exponential_kernel(x, dimensions, REG_sigma);
break;
case 3:
result = Matern_kernel(x, dimensions, REG_sigma, REG_n, REG_Matern_comb_number);
@@ -443,7 +427,7 @@ T REG<T>::REG_RMSE()
T y_max = REG_y_vec.amax();
T y_min = REG_y_vec.amin();
REG_y_delta_vec |= REG_y_preds_vec;
REG_y_delta_vec.storesubvector(0, REG_y_preds_vec); //in place, not |= (cached raw pointer)
REG_y_delta_vec -= REG_y_vec;
REG_y_delta_vec *= REG_y_delta_vec;
delta_square = REG_y_delta_vec.sum();
@@ -509,7 +493,7 @@ unsigned int REG<T>::REG_Fit(const T max_Loss, const unsigned int max_learning_c
REG_mat_init = true;
}
// initialitation of weight coefficients vector
REG_weights_vec |= REG_y_vec;
REG_weights_vec.storesubvector(0, REG_y_vec); //in place, not |= (cached raw pointer)
// Linear learning cycle of weight coefficient for learning
for (i = 0; i < max_learning_cycles; i++)
{
@@ -523,7 +507,7 @@ unsigned int REG<T>::REG_Fit(const T max_Loss, const unsigned int max_learning_c
REG_y_preds_vec += Lasso_part;
// calculating new weights
REG_y_temp_vec |= REG_y_vec;
REG_y_temp_vec.storesubvector(0, REG_y_vec); //in place, not |= (cached raw pointer)
REG_y_temp_vec /= REG_y_preds_vec;
REG_weights_vec *= REG_y_temp_vec;
// checking the Loss_Function value
@@ -580,8 +564,8 @@ unsigned int REG<T>::REG_Fit_grad(const T max_Loss, const unsigned int max_learn
REG_mat_init = true;
}
// initialitation of weight coefficients vector
REG_weights_vec |= REG_y_vec;
kernel_ridge_part = REG_lambda * (1.00 - REG_alpha);
REG_weights_vec.storesubvector(0, REG_y_vec); //in place, not |= (cached raw pointer)
kernel_ridge_part = REG_lambda * (1.00 - REG_alpha) * 0.5;
Lasso_part = REG_lambda * REG_alpha;
sqrt_count_x = std::sqrt(count_x);
// gradient descent Learning cycle of weight coefficient for learning
@@ -760,6 +744,9 @@ unsigned int REG<T>::REG_Get_Score(const T* x, T* y_preds, const unsigned int d
template <typename T>
unsigned int REG<T>::REG_Set_params(const T* params, const string kernel)
{
const unsigned int old_kernel_id = REG_kernel_id;
const unsigned int old_n = (REG_kernel_id == 3) ? REG_n : 0; // REG_n is set only for Matern
const T old_sigma = REG_sigma;
// setting type of kernel
REG_kernel_type = kernel;
if (kernel == "Gaussian")
@@ -784,10 +771,13 @@ unsigned int REG<T>::REG_Set_params(const T* params, const string kernel)
// calculating Matern coefficients, if Matern kernel is selected
if (REG_kernel_id == 3)
{
REG_Matern_comb_number_vec.resize(REG_n);
REG_Matern_comb_number_vec.resize(REG_n + 1);
REG_Matern_comb_number = REG_Matern_comb_number_vec.operator T*();
Matern_coefficient(REG_n, REG_Matern_comb_number);
}
// kernel matrices depend on kernel, sigma and n (not on lambda, alpha): rebuild them in the next fit
if (REG_kernel_id != old_kernel_id || REG_sigma != old_sigma || (REG_kernel_id == 3 && REG_n != old_n))
REG_mat_init = false;
return(0);
}
@@ -800,7 +790,7 @@ unsigned int REG<T>::REG_Get_params(T* params, string* kernel)
params[1] = REG_alpha;
params[2] = REG_sigma;
if (REG_kernel_type == "Mattern")
if (REG_kernel_id == 3)
params[3] = REG_n;
return(0);