void regression(point *points, size_t point_count, transform_t t,
double *slope, double *intercept) {
assert(slope);
assert(intercept);
double mx = 0;
double my = 0;
if (!calc_means(points, point_count, t, &mx, &my)) {
*slope = EMPTY_VALUE;
*intercept = EMPTY_VALUE;
return;
}
double num = calc_num(points, point_count, t, mx, my);
double den = calc_den(points, point_count, t, mx);
*slope = num / den;
*intercept = my - *slope * mx;
LOG(1, "-- slope: %g, intercept: %g\n", *slope, *intercept);
}
/* Performs LSE of consequent parameters for all branches in the network
*
* PARAMETERS: A ---> predictor variables matrix
* y ---> expected results for this sample set (P in total)
*
* PRE: A != NULL
* y != NULL
*
* POS: result != NULL && best fit consequent parameters vector returned
* or
* result == NULL
*/
static gsl_vector *
anfis_fit_linear (const gsl_matrix *A, const gsl_vector *y, size_t P, size_t M)
{
gsl_matrix *S = NULL, /* Covariance matrix */
*Snew = NULL,
*Saux = NULL;
gsl_vector *X = NULL, /* Future best fit parameters */
*Xnew = NULL;
unsigned int i = 0;
double den = 0.0, factor = 0.0;
assert (A != NULL);
assert (y != NULL);
/* Generating necessary workspace */
S = gsl_matrix_alloc (M,M);
if (S == NULL)
goto exit_point;
Snew = gsl_matrix_calloc (M,M);
if (Snew == NULL)
goto exit_point;
Saux = gsl_matrix_calloc (M,M);
if (Saux == NULL)
goto exit_point;
Xnew = gsl_vector_alloc (M);
if (Xnew == NULL)
goto exit_point;
X = gsl_vector_calloc (M);
if (X == NULL)
goto exit_point;
/* S = γ*Id , where γ is a large number */
gsl_matrix_set_identity (S);
gsl_matrix_scale (S, _gamma);
/* Performing Least Square Estimation */
for (i=0 ; i < P ; i++) {
/* Matrix A i-th row (row At_i+1 in Jang's paper) */
gsl_vector_const_view A_i = gsl_matrix_const_row (A, i);
/* Snew = S(i) * A_i+1 * At_i+1 * S(i) */
calc_num (S, &(A_i.vector), Snew, Saux, Xnew, M);
/* scale = At_i+1 * S(i) * A_i+1 */
den = calc_den (S, &(A_i.vector), Xnew);
/* Snew = Snew / (1+scale) */
gsl_matrix_scale (Snew, 1.0 / (1.0+den));
/* S(i+1) = S(i) - Snew */
gsl_matrix_sub (S, Snew);
/* factor = At_i+1 * X(i) */
gsl_blas_ddot (&(A_i.vector), X, &factor);
/* factor = yt_i+1 - factor */
factor = gsl_vector_get (y, i) - factor;
/* Xnew = S(i+1) * A_i+1 */
gsl_blas_dgemv (CblasNoTrans, 1.0, S, &(A_i.vector), 0.0, Xnew);
/* Xnew = Xnew * factor */
gsl_vector_scale (Xnew, factor);
/* X(i+1) = X(i) + Xnew */
gsl_vector_add (X, Xnew);
}
exit_point:
if (S != NULL) { gsl_matrix_free (S); S = NULL;}
if (Snew != NULL) { gsl_matrix_free (Snew); Snew = NULL;}
if (Saux != NULL) { gsl_matrix_free (Saux); Saux = NULL;}
if (Xnew != NULL) { gsl_vector_free (Xnew); Xnew = NULL;}
return X;
}
