static PyObject *PyLWPR_update(PyLWPR *self, PyObject *args) {
LWPR_Model *model = &(self->model);
PyArrayObject *x, *y;
if (!PyArg_ParseTuple(args, "O!O!", &PyArray_Type, &x, &PyArray_Type, &y)) return NULL;
if (set_vector_from_array(model->nIn, self->extra_in, x)) return NULL;
if (set_vector_from_array(model->nOut, self->extra_out, y)) return NULL;
lwpr_update(model,self->extra_in, self->extra_out, self->extra_out2, NULL);
return get_array_from_vector(model->nOut, self->extra_out2);
}
static PyObject *PyLWPR_update_maxw(PyLWPR *self, PyObject *args) {
LWPR_Model *model = &(self->model);
PyArrayObject *x, *y;
PyObject *o1,*o2,*result;
if (!PyArg_ParseTuple(args, "O!O!", &PyArray_Type, &x, &PyArray_Type, &y)) return NULL;
if (set_vector_from_array(model->nIn, self->extra_in, x)) return NULL;
if (set_vector_from_array(model->nOut, self->extra_out, y)) return NULL;
lwpr_update(model,self->extra_in, self->extra_out, self->extra_out2, self->extra_out3);
o1 = get_array_from_vector(model->nOut, self->extra_out2);
o2 = get_array_from_vector(model->nOut, self->extra_out3);
result = Py_BuildValue("(O,O)",o1,o2);
Py_DECREF(o1);
Py_DECREF(o2);
return result;
}
int main(int argc, char** argv)
{
// Instantiate a ModelManager:
ModelManager manager("Test LWPR");
// Parse command-line:
if (manager.parseCommandLine((const int)argc, (const char**)argv, "", 0, 0) == false)
return(1);
manager.start();
double x[2];
double y,yp;
double mse;
FILE *fp;
LWPR_Model model;
int i,j;
/* This allocates some memory and sets initial values
** Note that the model structure itself already exists (on the stack)
*/
lwpr_init_model(&model,2,1,"2D_Cross");
/* Set initial distance metric to 50*(identity matrix) */
lwpr_set_init_D_spherical(&model,50);
/* Set init_alpha to 250 in all elements */
lwpr_set_init_alpha(&model,250);
/* Set w_gen to 0.2 */
model.w_gen = 0.2;
/* See above definition, we either use srand() on Windows or srand48 everywhere else */
SEED_RAND();
for (j=0;j<20;j++) {
mse = 0.0;
for (i=0;i<1000;i++) {
x[0] = 2.0*URAND()-1.0;
x[1] = 2.0*URAND()-1.0;
y = cross(x[0],x[1]) + 0.1*URAND()-0.05;
/* Update the model with one sample
**
** x points to (x[0],x[1]) (input vector)
** &y points to y (output "vector")
** &yp points to yp (prediction "vector")
**
** If you are interested in maximum activation, call
** lwpr_update(&model, x, &y, &yp, &max_w);
*/
lwpr_update(&model, x, &y, &yp, NULL);
mse+=(y-yp)*(y-yp);
}
mse/=500;
printf("#Data = %d #RFS = %d MSE = %f\n",model.n_data, model.sub[0].numRFS, mse);
}
fp = fopen("output.txt","w");
mse = 0.0;
i=0;
for (x[1]=-1.0; x[1]<=1.01; x[1]+=0.05) {
for (x[0]=-1.0; x[0]<=1.01; x[0]+=0.05) {
y = cross(x[0],x[1]);
/* Use the model for predicting an output
**
** x points to (x[0],x[1]) (input vector)
** 0.001 is the cutoff value (clip Gaussian kernel)
** &yp points to yp (prediction "vector")
**
** If you are interested in confidence bounds or
** maximum activation, call
** lwpr_predict(&model, x, 0.001, &yp, &conf, &max_w);
*/
lwpr_predict(&model, x, 0.001, &yp, NULL, NULL);
mse += (y-yp)*(y-yp);
i++;
fprintf(fp,"%8.5f %8.5f %8.5f\n",x[0],x[1],yp);
}
fprintf(fp,"\n\n");
}
fclose(fp);
printf("MSE on test data (%d) = %f\n",i,mse/(double) i);
printf("\nTo view the output, start gnuplot, and type:\n");
printf(" splot \"output.txt\"\n\n");
/* Free the memory that was allocated for receptive fields etc.
** Note again that this does not free the LWPR_Model structure
** itself (but it exists on the stack, so it's automatically free'd) */
lwpr_free_model(&model);
// stop all our ModelComponents
manager.stop();
// all done!
return 0;
}
int main() {
double x[2],y[2],yp[2];
double mseTr[2];
double testErr[2],wTestErr[2];
double binErr[2], wBinErr[2];
double xmlErr[2], wXmlErr[2];
double sumErr;
LWPR_Model model;
int i,j;
int numRFS;
/* This allocates some memory and sets initial values
** Note that the model structure itself already exists (on the stack)
*/
lwpr_init_model(&model,2,2,"2D_Cross");
/* Set initial distance metric to 50*(identity matrix) */
lwpr_set_init_D_spherical(&model,50);
/* Set init_alpha to 250 in all elements */
lwpr_set_init_alpha(&model,250);
/* Set w_gen to 0.2 */
model.w_gen = 0.2;
/* See above definition, we either use srand() on Windows or srand48 everywhere else */
SEED_RAND();
for (j=0;j<20;j++) {
mseTr[0] = mseTr[1] = 0.0;
for (i=0;i<1000;i++) {
x[0] = 2.0*URAND()-1.0;
x[1] = 2.0*URAND()-1.0;
y[0] = cross(x[0],x[1]) + 0.1*URAND()-0.05;
y[1] = y[0] + 10; /* sanity check */
/* Update the model with one sample
**
** x points to (x[0],x[1]) (input vector)
** &y points to y (output "vector")
** &yp points to yp (prediction "vector")
**
** If you are interested in maximum activation, call
** lwpr_update(&model, x, &y, &yp, &max_w);
*/
lwpr_update(&model, x, y, yp, NULL);
mseTr[0]+=(y[0]-yp[0])*(y[0]-yp[0]);
mseTr[1]+=(y[1]-yp[1])*(y[1]-yp[1]);
}
mseTr[0]/=500;
mseTr[1]/=500;
printf("#Data = %d #RFS = %d / %d MSE = %f / %f\n",model.n_data, model.sub[0].numRFS, model.sub[1].numRFS, mseTr[0], mseTr[1]);
}
if (model.n_data != 20000) {
fprintf(stderr,"model.n_data should have been 20*1000 = 20000. Something is very wrong!\n");
exit(1);
}
if (model.sub[0].numRFS != model.sub[1].numRFS) {
fprintf(stderr,"There should have been an equal number of receptive fields for both outputs :-(\n");
exit(1);
}
numRFS = model.sub[0].numRFS;
testErrors(&model, testErr, wTestErr);
printf("MSE on test data: %f / %f\n",testErr[0], testErr[1]);
if (fabs(testErr[0]-testErr[1]) > 1e-4) {
fprintf(stderr,"MSE should be equal for both outputs, but the difference is > 1e-4\n");
exit(1);
}
printf("Weighted MSE....: %f / %f\n",wTestErr[0], wTestErr[1]);
if (fabs(wTestErr[0]-wTestErr[1]) > 1e-4) {
fprintf(stderr,"Weighted MSE should be equal for both outputs, but the difference is > 1e-4\n");
exit(1);
}
printf("Writing the model to a binary file\n");
/* Write the model to an XML file */
lwpr_write_binary(&model,"lwpr_cross_2d.dat");
/* Free the memory that was allocated for receptive fields etc. */
lwpr_free_model(&model);
printf("Re-read the model from the binary file\n");
/* Read a model from an XML file, memory allocation is done automatically,
** but later lwpr_free_model has to be called again */
j=lwpr_read_binary(&model,"lwpr_cross_2d.dat");
remove("lwpr_cross_2d.dat");
if (j==0) {
fprintf(stderr,"File could not be read, aborting\n");
exit(1);
}
printf("#Data = %d #RFS = %d / %d\n",model.n_data, model.sub[0].numRFS, model.sub[1].numRFS);
if (model.n_data != 20000 || model.sub[0].numRFS!=numRFS || model.sub[1].numRFS!=numRFS) {
fprintf(stderr,"Model (from binary file) seems to be broken :-(\n");
exit(1);
}
testErrors(&model, binErr, wBinErr);
printf("MSE on test data: %f / %f\n",binErr[0], binErr[1]);
printf("Weighted MSE....: %f / %f\n",wBinErr[0], wBinErr[1]);
sumErr = fabs(binErr[0] - testErr[0]) + fabs(binErr[1] - testErr[1]);
sumErr+= fabs(wBinErr[0] - wTestErr[0]) + fabs(wBinErr[1] - wTestErr[1]);
if (sumErr>1e-8) {
fprintf(stderr,"Error statistics from the binary-IO LWPR model are not the same :-(\n");
exit(1);
}
#if HAVE_LIBEXPAT
printf("Writing the model to an XML file\n");
/* Write the model to an XML file */
lwpr_write_xml(&model,"lwpr_cross_2d.xml");
/* Free the memory that was allocated for receptive fields etc. */
lwpr_free_model(&model);
/* Read a model from an XML file, memory allocation is done automatically,
** but later lwpr_free_model has to be called again */
j=lwpr_read_xml(&model,"lwpr_cross_2d.xml",&i);
remove("lwpr_cross_2d.xml");
printf("Re-read the model from the XML file\n");
printf("%d errors %d warnings\n",j,i);
if (j!=0) {
printf("Errors detected, aborting\n");
exit(1);
}
printf("#Data = %d #RFS = %d / %d\n",model.n_data, model.sub[0].numRFS, model.sub[1].numRFS);
if (model.n_data != 20000 || model.sub[0].numRFS!=numRFS || model.sub[1].numRFS!=numRFS) {
fprintf(stderr,"Model (from XML file) seems to be broken :-(\n");
exit(1);
}
testErrors(&model, xmlErr, wXmlErr);
printf("MSE on test data: %f / %f\n",xmlErr[0], xmlErr[1]);
printf("Weighted MSE....: %f / %f\n",wXmlErr[0], wXmlErr[1]);
sumErr = fabs(xmlErr[0] - testErr[0]) + fabs(xmlErr[1] - testErr[1]);
sumErr+= fabs(wXmlErr[0] - wTestErr[0]) + fabs(wXmlErr[1] - wTestErr[1]);
sumErr/=fabs(testErr[0]) + fabs(testErr[1]) + fabs(wTestErr[0]) + fabs(wTestErr[1]);
printf("Relative difference to the original model: %f\n",sumErr);
if (sumErr>0.0001) {
fprintf(stderr,"Error statistics from the XML-IO LWPR model differ too much :-(\n");
exit(1);
}
#else
printf("LWPR library has been compiled without EXPAT support, XML IO will not be tested.\n");
#endif
/* Free the memory that was allocated for receptive fields etc.
** Note again that this does not free the LWPR_Model structure
** itself (but it exists on the stack, so it's automatically free'd) */
lwpr_free_model(&model);
exit(0);
}
