add files for public distribution
based on internal repository 0a462b6 2017-11-22 14:41:39 +0100
This commit is contained in:
284
pmsco/loess/loess.i
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284
pmsco/loess/loess.i
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%module loess
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%include "typemaps.i"
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%{
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#define SWIG_FILE_WITH_INIT
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#include <errno.h>
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#define EARRLEN 1000
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#include "loess.h"
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extern void loess(struct loess_struct *lo);
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extern void loess_summary(struct loess_struct *lo);
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// not implemented
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// extern void predict(double *eval, int m, struct loess_struct *lo, struct pred_struct *pre, int se);
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%}
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%include "numpy.i"
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%init %{
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import_array();
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%}
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%apply (double *IN_ARRAY1, int DIM1) {(double *v, int n)};
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%apply (int *IN_ARRAY1, int DIM1) {(int *v, int n)};
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%apply (double **ARGOUTVIEWM_ARRAY1, int *DIM1) {(double **w, int *n)};
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%apply (int **ARGOUTVIEWM_ARRAY1, int *DIM1) {(int **w, int *n)};
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%include "loess.h"
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extern void loess(struct loess_struct *lo);
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extern void loess_summary(struct loess_struct *lo);
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// not implemented
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// extern void predict(double *eval, int m, struct loess_struct *lo, struct pred_struct *pre, int se);
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%exception {
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errno = 0;
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$action
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if (errno != 0) {
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switch(errno) {
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case ENOMEM:
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PyErr_Format(PyExc_MemoryError, "memory allocation failed.");
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break;
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case EARRLEN:
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PyErr_Format(PyExc_ValueError, "unexpected array length.");
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break;
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default:
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PyErr_Format(PyExc_Exception, "unknown exception.");
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}
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SWIG_fail;
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}
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}
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%extend loess_struct {
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//// constructor of a loess_struct
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//
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// @param n: number of data points.
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//
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// @param p: number of factors (independent variables). maximum 8.
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loess_struct(int n, int p) {
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struct loess_struct *lo;
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lo = (struct loess_struct *) malloc(sizeof(loess_struct));
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int i, max_kd;
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max_kd = n > 200 ? n : 200;
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lo->in.y = (double *) malloc(n * sizeof(double));
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lo->in.x = (double *) malloc(n * p * sizeof(double));
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lo->in.weights = (double *) malloc(n * sizeof(double));
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for(i = 0; i < (n * p); i++)
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lo->in.x[i] = 0.0;
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for(i = 0; i < n; i++) {
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lo->in.y[i] = 0.0;
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lo->in.weights[i] = 1.0;
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}
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lo->in.n = n;
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lo->in.p = p;
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lo->model.span = 0.75;
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lo->model.degree = 2;
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lo->model.normalize = TRUE;
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for(i = 0; i < 8; i++)
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lo->model.parametric[i] = lo->model.drop_square[i] = FALSE;
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lo->model.family = "gaussian";
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lo->control.surface = "interpolate";
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lo->control.statistics = "approximate";
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lo->control.cell = 0.2;
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lo->control.trace_hat = "wait.to.decide";
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lo->control.iterations = 4;
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lo->out.fitted_values = (double *) malloc(n * sizeof(double));
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lo->out.fitted_residuals = (double *) malloc(n * sizeof(double));
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lo->out.pseudovalues = (double *) malloc(n * sizeof(double));
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lo->out.diagonal = (double *) malloc(n * sizeof(double));
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lo->out.robust = (double *) malloc(n * sizeof(double));
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lo->out.divisor = (double *) malloc(p * sizeof(double));
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lo->kd_tree.parameter = (int *) malloc(7 * sizeof(int));
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lo->kd_tree.a = (int *) malloc(max_kd * sizeof(int));
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lo->kd_tree.xi = (double *) malloc(max_kd * sizeof(double));
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lo->kd_tree.vert = (double *) malloc(p * 2 * sizeof(double));
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lo->kd_tree.vval = (double *) malloc((p + 1) * max_kd * sizeof(double));
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return lo;
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}
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~loess_struct() {
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free($self->in.x);
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free($self->in.y);
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free($self->in.weights);
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free($self->out.fitted_values);
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free($self->out.fitted_residuals);
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free($self->out.pseudovalues);
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free($self->out.diagonal);
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free($self->out.robust);
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free($self->out.divisor);
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free($self->kd_tree.parameter);
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free($self->kd_tree.a);
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free($self->kd_tree.xi);
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free($self->kd_tree.vert);
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free($self->kd_tree.vval);
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free($self);
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}
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void set_x(double *v, int n) {
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int n_exp = $self->in.n * $self->in.p;
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if (n == n_exp) {
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int i;
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for(i = 0; i < n; i++)
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$self->in.x[i] = v[i];
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} else {
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errno = EARRLEN;
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}
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}
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void set_y(double *v, int n) {
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int n_exp = $self->in.n;
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if (n == n_exp) {
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int i;
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for(i = 0; i < n; i++)
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$self->in.y[i] = v[i];
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} else {
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errno = EARRLEN;
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}
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}
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void set_parametric(int *v, int n) {
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int n_exp = $self->in.p;
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if (n == n_exp) {
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int i;
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for(i = 0; i < n; i++)
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$self->model.parametric[i] = v[i];
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} else {
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errno = EARRLEN;
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}
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}
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void set_drop_square(int *v, int n) {
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int n_exp = $self->in.p;
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if (n == n_exp) {
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int i;
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for(i = 0; i < n; i++)
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$self->model.drop_square[i] = v[i];
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} else {
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errno = EARRLEN;
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}
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}
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void get_x(double **w, int *n) {
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int ni = $self->in.n * $self->in.p;
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double *temp;
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temp = (double *)malloc(ni * sizeof(double));
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if (temp == NULL)
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errno = ENOMEM;
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int i;
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for(i = 0; i < ni; i++)
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temp[i] = $self->in.x[i];
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*w = temp;
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*n = ni;
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}
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void get_y(double **w, int *n) {
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int ni = $self->in.n;
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double *temp;
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temp = (double *)malloc(ni * sizeof(double));
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if (temp == NULL)
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errno = ENOMEM;
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int i;
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for(i = 0; i < ni; i++)
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temp[i] = $self->in.y[i];
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*w = temp;
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*n = ni;
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}
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void get_weights(double **w, int *n) {
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int ni = $self->in.n;
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double *temp;
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temp = (double *)malloc(ni * sizeof(double));
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if (temp == NULL)
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errno = ENOMEM;
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int i;
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for(i = 0; i < ni; i++)
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temp[i] = $self->in.weights[i];
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*w = temp;
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*n = ni;
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}
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void get_fitted_values(double **w, int *n) {
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int ni = $self->in.n;
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double *temp;
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temp = (double *)malloc(ni * sizeof(double));
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if (temp == NULL)
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errno = ENOMEM;
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int i;
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for(i = 0; i < ni; i++)
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temp[i] = $self->out.fitted_values[i];
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*w = temp;
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*n = ni;
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}
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void get_fitted_residuals(double **w, int *n) {
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int ni = $self->in.n;
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double *temp;
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temp = (double *)malloc(ni * sizeof(double));
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if (temp == NULL)
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errno = ENOMEM;
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int i;
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for(i = 0; i < ni; i++)
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temp[i] = $self->out.fitted_residuals[i];
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*w = temp;
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*n = ni;
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}
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void get_parametric(int **w, int *n) {
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int ni = $self->in.p;
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int *temp;
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temp = (int *)malloc(ni * sizeof(int));
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if (temp == NULL)
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errno = ENOMEM;
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int i;
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for(i = 0; i < ni; i++)
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temp[i] = $self->model.parametric[i];
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*w = temp;
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*n = ni;
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}
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void get_drop_square(int **w, int *n) {
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int ni = $self->in.p;
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int *temp;
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temp = (int *)malloc(ni * sizeof(int));
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if (temp == NULL)
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errno = ENOMEM;
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int i;
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for(i = 0; i < ni; i++)
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temp[i] = $self->model.drop_square[i];
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*w = temp;
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*n = ni;
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}
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};
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