//---------- Begin of function PieChart::draw_scale -------------//
//!
void PieChart::draw_scale() {
find_scale();
short xAxisPos;
//------ Draw y-axis -------//
if (x_axis_pos)
vga_back.bar(series_x1, series_y1-COMMON_OFFSET*2, series_x1+1, series_y2+COMMON_OFFSET*2, axis_color);
else
vga_back.bar(series_x1, series_y1-COMMON_OFFSET*2, series_x1+1, series_y2, axis_color);
short scaleNum = (series_y2-series_y1-font_ptr->max_font_height) / (font_ptr->max_font_height*3/2);
if ( scaleNum > 0 )
scaleNum = short(pow(2,floor(log10(double(scaleNum))/log10(2.0))));
else
scaleNum = 0;
short scaleStep = (series_y2-series_y1) / scaleNum;
double scaleValue;
for (int i = 0; i <= scaleNum; i++) {
if ( (x_axis_pos && i == scaleNum / 2) || !(x_axis_pos || i) )
vga_back.bar(series_x1, xAxisPos=series_y2-i*scaleStep-1, series_x2+COMMON_OFFSET*8, series_y2-i*scaleStep, axis_color);
else
vga_back.bar(series_x1, series_y2-i*scaleStep, series_x1+COMMON_OFFSET*2, series_y2-i*scaleStep, axis_color);
if (x_axis_pos)
scaleValue = max_scale*2/scaleNum*i - max_scale;
else
scaleValue = max_scale/scaleNum*i;
switch (data_type_flag) {
case DATA_FLOAT:
case DATA_DOUBLE:
font_ptr->right_put(series_x1-COMMON_OFFSET, series_y2-i*scaleStep-font_ptr->max_font_height/2, m.format(scaleValue,num_format));
break;
case DATA_INT:
case DATA_LONG:
font_ptr->right_put(series_x1-COMMON_OFFSET, series_y2-i*scaleStep-font_ptr->max_font_height/2, m.format(long(scaleValue),num_format));
}
}
//------ Draw x-axis -------//
short maxLabelWidth = font_ptr->text_width(m.format(*x_end, 4))+COMMON_OFFSET*2;
x_axis_step = (series_x2-series_x1) / (data_num-1);
short displayInc = 1;
while (displayInc * x_axis_step / maxLabelWidth < 1) displayInc++;
int xAxisStep = (*x_end - *x_start) / (data_num - 1);
for (int i = 0; i < data_num-1; i += displayInc) {
vga_back.bar(series_x2-i*x_axis_step, xAxisPos-COMMON_OFFSET*2, series_x2-i*x_axis_step, xAxisPos, axis_color);
font_ptr->center_put((series_x2-i*x_axis_step)-maxLabelWidth/2-COMMON_OFFSET, xAxisPos+COMMON_OFFSET,
(series_x2-i*x_axis_step)+maxLabelWidth/2+COMMON_OFFSET, xAxisPos+COMMON_OFFSET+font_ptr->max_font_height, m.format(*x_end-xAxisStep*i, 4));
}
}
void fast_s_large_n(double *X, double *y,
int *nn, int *pp, int *NN, int *K,
int *ggroups, int *nn_group,
int *bbest_r, double *bb, double *rrhoc,
double *bbeta, double *sscale)
{
/* *X = the design matrix as a vector (as sent by R)
// *y = the response vector
// *nn = the length of y
// *pp = the number of columns in X
// *NN = number of re-sampling candidates to be
// used in each partition
// *bbest_r = no. of best candidates to be iterated
// further
// *bb = right-hand side of S-equation (typically 1/2)
// *rrhoc = tuning constant for Tukey's bi-square loss
// (this should be associated with *bb)
// *bbeta = final estimator
// *sscale = associated scale estimator
// *ggroups = number of groups in which to split the
// random subsample
// *nn_group = size of each of the (*ggroups) groups
// to use in the random subsample
*/
void reset_mat(double **a, int n, int m);
double loss_rho(double *r, double scale, int n, int p, double rhoc);
double find_scale(double *r, double b, double rhoc,
double initial_scale, int n, int p);
// void disp_mat(double **a, int n, int m);
// void disp_vec(double *a, int n);
void fast_s_with_memory(double **x, double *y,
int *nn, int *pp, int *NN, int *K,
int *bbest_r, double *bb,
double *rrhoc,
double **best_betas, double *best_scales);
void sample_n_outof_N(int n, int N, int *x);
void refine_fast_s(double **x, double *y, double *weights,
int n, int p, double *res,
double *tmp, double *tmp2,
double **tmp_mat, double **tmp_mat2,
double *beta_cand, int kk,
int conv, double b, double rhoc, double *is, double *beta_ref,
double *scale);
int find_max(double *a, int n);
register int i,j,k,k2;
int n = *nn, p = *pp, kk = *K, *indices;
int groups = *ggroups, n_group = *nn_group, best_r = *bbest_r;
double **best_betas, *best_scales;
double **final_best_betas, *final_best_scales;
double **x, **xsamp, *ysamp, *res, sc, *beta_ref;
double *tmp, *tmp2, **tmp_mat, **tmp_mat2, *weights;
double best_sc, worst_sc, b = *bb, rhoc = *rrhoc, aux;
int pos_worst_scale, conv;
res = (double *) malloc( n * sizeof(double) );
weights = (double *) malloc( n * sizeof(double) );
tmp = (double *) malloc( n * sizeof(double) );
tmp2 = (double *) malloc( n * sizeof(double) );
tmp_mat = (double **) malloc( p * sizeof(double *) );
tmp_mat2 = (double **) malloc( p * sizeof(double *) );
for(i=0;i<p;i++) {
tmp_mat[i] = (double *) malloc( p * sizeof(double) );
tmp_mat2[i] = (double *) malloc( (p+1) * sizeof(double) );
};
beta_ref = (double *) malloc( p * sizeof(double) );
final_best_betas = (double **) malloc( best_r * sizeof( double * ) );
for(i=0; i < best_r; i++)
final_best_betas[i] = (double *) malloc(
p * sizeof(double) );
final_best_scales = (double *) malloc( best_r * sizeof(double) );
k = best_r * groups;
best_betas = (double **) malloc( k * sizeof( double * ) );
best_scales = (double *) malloc( k * sizeof( double ) );
for(i=0; i < k; i++)
best_betas[i] = (double*) malloc( p * sizeof(double) );
x = (double**) malloc( n * sizeof(double *) );
for(i=0; i<n; i++)
x[i] = (double*) malloc( p * sizeof(double) );
k = n_group * groups;
indices = (int *) malloc( k * sizeof(int) );
xsamp = (double**) malloc( k * sizeof(double *) );
ysamp = (double*) malloc( k * sizeof(double) );
for(i=0;i<k;i++)
xsamp[i] = (double*) malloc( p * sizeof(double) );
for(i=0;i<n;i++)
for(j=0;j<p;j++)
x[i][j]=X[j*n+i];
/* assume that n > 2000
// k = n_group * groups
// set the seed
*/
srand((long)37);
/* get a sample of k indices */
sample_n_outof_N(k, n-1, indices);
/* get the sampled design matrix and response */
for(i=0;i<k;i++) {
for(j=0;j<p;j++)
xsamp[i][j] = x[indices[i]][j];
ysamp[i] = y[indices[i]];
};
/* now we go through the groups and get the
// *bbest_r best betas for each group
*/
for(i=0; i<groups; i++) {
fast_s_with_memory(xsamp+i*n_group, ysamp+i*n_group,
&n_group, pp, NN, K,
bbest_r, bb, rrhoc,
best_betas+i*best_r,
best_scales+i*best_r);
};
/* now iterate (refine) these "best_r * groups"
// best betas in the (xsamp,ysamp) sample
// with kk C-steps
// and keep only the "best_r" best ones
*/
best_sc = INFI;
pos_worst_scale = conv = 0;
for(i=0; i < best_r; i++)
final_best_scales[i] = INFI;
worst_sc = INFI;
/* set the matrix to zero */
reset_mat(final_best_betas, best_r, p);
k = n_group * groups;
for(i=0; i< (best_r * groups) ; i++) {
refine_fast_s(xsamp, ysamp, weights, k, p, res,
tmp, tmp2, tmp_mat, tmp_mat2,
best_betas[i], kk, conv, b, rhoc,
best_scales+i, beta_ref,
&sc);
if ( loss_rho(res, worst_sc, k, p, rhoc) < b ) {
/* scale will be better */
sc = find_scale(res, b, rhoc, sc, k, p);
k2 = pos_worst_scale;
final_best_scales[ k2 ] = sc;
for(j=0;j<p;j++)
final_best_betas[k2][j] = beta_ref[j];
pos_worst_scale = find_max(final_best_scales, best_r);
worst_sc = final_best_scales[pos_worst_scale];
};
};
/* now iterate the best "best_r"
// betas in the whole sample (until convergence if possible)
*/
best_sc = INFI;
conv = 1;
for(i=0; i<best_r; i++) {
refine_fast_s(x, y, weights, n, p, res,
tmp, tmp2, tmp_mat, tmp_mat2,
final_best_betas[i], kk, conv, b, rhoc,
final_best_scales+i, beta_ref,
&aux);
if(aux < best_sc) {
*sscale = best_sc = aux;
for(j=0;j<p;j++)
bbeta[j] = beta_ref[j];
};
};
/* Done. Now clean-up. */
for(i=0;i<n;i++) free(x[i]); free(x);
free(best_scales);
k = best_r * groups;
for(i=0;i<k;i++) free( best_betas[i] );
free(best_betas); free(indices); free(ysamp);
k = n_group * groups;
for(i=0;i<k;i++) free(xsamp[i]);
free(xsamp); free(tmp); free(tmp2);
for(i=0;i<p;i++) {
free(tmp_mat[i]);
free(tmp_mat2[i]);
};
free(tmp_mat); free(tmp_mat2); free(weights);
for(i=0;i<best_r;i++)
free(final_best_betas[i]);
free(final_best_betas);
free(final_best_scales);
free(res);
free(beta_ref);
}
//
// main
// ----
//
int main(void)
{
libusb_device **devs;
int r; // holds return codes
ssize_t cnt;
libusb_device* dev;
libusb_device_handle* handle;
int weigh_count = WEIGH_COUNT -1;
//
// We first try to init libusb.
//
r = libusb_init(NULL);
//
// If `libusb_init` errored, then we quit immediately.
//
if (r < 0)
return r;
#ifdef DEBUG
libusb_set_debug(NULL, 3);
#endif
//
// Next, we try to get a list of USB devices on this computer.
cnt = libusb_get_device_list(NULL, &devs);
if (cnt < 0)
return (int) cnt;
//
// Once we have the list, we use **find_scale** to loop through and match
// every device against the scales.h list. **find_scale** will return the
// first device that matches, or 0 if none of them matched.
//
dev = find_scale(devs);
if(dev == 0) {
fprintf(stderr, "No USB scale found on this computer.\n");
return -1;
}
//
// Once we have a pointer to the USB scale in question, we open it.
//
r = libusb_open(dev, &handle);
//
// Note that this requires that we have permission to access this device.
// If you get the "permission denied" error, check your udev rules.
//
if(r < 0) {
if(r == LIBUSB_ERROR_ACCESS) {
fprintf(stderr, "Permission denied to scale.\n");
}
else if(r == LIBUSB_ERROR_NO_DEVICE) {
fprintf(stderr, "Scale has been disconnected.\n");
}
return -1;
}
//
// On Linux, we typically need to detach the kernel driver so that we can
// handle this USB device. We are a userspace tool, after all.
//
#ifdef __linux__
libusb_detach_kernel_driver(handle, 0);
#endif
//
// Finally, we can claim the interface to this device and begin I/O.
//
libusb_claim_interface(handle, 0);
/*
* Try to transfer data about status
*
* http://rowsandcolumns.blogspot.com/2011/02/read-from-magtek-card-swipe-reader-in.html
*/
unsigned char data[WEIGH_REPORT_SIZE];
int len;
int scale_result = -1;
//
// For some reason, we get old data the first time, so let's just get that
// out of the way now. It can't hurt to grab another packet from the scale.
//
r = libusb_interrupt_transfer(
handle,
//bmRequestType => direction: in, type: class,
// recipient: interface
LIBUSB_ENDPOINT_IN | //LIBUSB_REQUEST_TYPE_CLASS |
LIBUSB_RECIPIENT_INTERFACE,
data,
WEIGH_REPORT_SIZE, // length of data
&len,
10000 //timeout => 10 sec
);
//
// We read data from the scale in an infinite loop, stopping when
// **print_scale_data** tells us that it's successfully gotten the weight
// from the scale, or if the scale or transmissions indicates an error.
//
for(;;) {
//
// A `libusb_interrupt_transfer` of 6 bytes from the scale is the
// typical scale data packet, and the usage is laid out in *HID Point
// of Sale Usage Tables*, version 1.02.
//
r = libusb_interrupt_transfer(
handle,
//bmRequestType => direction: in, type: class,
// recipient: interface
LIBUSB_ENDPOINT_IN | //LIBUSB_REQUEST_TYPE_CLASS |
LIBUSB_RECIPIENT_INTERFACE,
data,
WEIGH_REPORT_SIZE, // length of data
&len,
10000 //timeout => 10 sec
);
//
// If the data transfer succeeded, then we pass along the data we
// received tot **print_scale_data**.
//
if(r == 0) {
#ifdef DEBUG
int i;
for(i = 0; i < WEIGH_REPORT_SIZE; i++) {
printf("%x\n", data[i]);
}
#endif
if (weigh_count < 1) {
scale_result = print_scale_data(data);
if(scale_result != 1)
break;
}
weigh_count--;
}
else {
fprintf(stderr, "Error in USB transfer\n");
scale_result = -1;
break;
}
}
//
// At the end, we make sure that we reattach the kernel driver that we
// detached earlier, close the handle to the device, free the device list
// that we retrieved, and exit libusb.
//
#ifdef __linux__
libusb_attach_kernel_driver(handle, 0);
#endif
libusb_close(handle);
libusb_free_device_list(devs, 1);
libusb_exit(NULL);
//
// The return code will be 0 for success or -1 for errors (see
// `libusb_init` above if it's neither 0 nor -1).
//
return scale_result;
}
void fast_s(double *X, double *y,
int *nn, int *pp, int *NN, int *K,
int *bbest_r, double *bb,
double *rrhoc, double *bbeta, double *sscale)
{
/*
// X an n x p design matrix (including intercept if appropriate)
// y and n vector
// *nn = n, *pp = p
// *NN = number of re-sampling candidates to be taken
// *K = number of refining steps for each candidate
// *bbest_r = number of (refined) to be retained for
// full iteration
// *bb = right-hand side of the S-equation
// *rrhoc = tuning constant of the \rho function
// *bbeta = returning fast-S estimator
// *sscale = returning associated residual scale
*/
void refine_fast_s(double **x, double *y, double *weights,
int n, int p, double *res,
double *tmp, double *tmp2,
double **tmp_mat, double **tmp_mat2,
double *beta_cand, int kk,
int conv, double b, double rhoc,
double *is,
double *beta_ref, double *scale);
// void disp_vec(double *a, int n);
// void disp_mat(double **a, int n, int m);
int find_max(double *a, int n);
double find_scale(double *r, double b, double rhoc,
double initial_scale, int n, int p);
double loss_rho(double *r, double s, int n, int p, double rhoc);
double vecprime_vec(double *a, double *b, int n);
void sample_n_outof_N(int n, int N, int *x);
int lu(double **a,int *P, double *x);
register int i,j,k;
int n = *nn, p = *pp, Nres = *NN, kk = *K, no_resamples;
int *b_i, flag, conv;
double **x, **x_samp, *beta_cand, *beta_ref, *res, aux;
double b = *bb, rhoc = *rrhoc, sc, worst_sc = INFI;
double **best_betas, *best_scales, *weights;
int best_r = *bbest_r, pos_worst_scale;
double *tmp, *tmp2, **tmp_mat2, **tmp_mat, best_sc;
best_betas = (double **) malloc( best_r * sizeof(double*) );
best_scales = (double *) malloc( best_r * sizeof(double) );
for(i=0;i<best_r;i++) {
best_betas[i] = (double*) malloc( p * sizeof(double) );
best_scales[i] = INFI; };
pos_worst_scale = 0;
res = (double *) malloc( n * sizeof(double) );
tmp = (double *) malloc( n * sizeof(double) );
tmp2 = (double *) malloc( n * sizeof(double) );
weights = (double *) malloc( n * sizeof(double) );
beta_cand = (double *) malloc( p * sizeof(double) );
beta_ref = (double *) malloc( p * sizeof(double) );
b_i = (int *) malloc( n * sizeof(int) );
x = (double **) malloc( n * sizeof(double*) );
x_samp = (double **) malloc( n * sizeof(double*) );
tmp_mat = (double **) malloc( p * sizeof(double*) );
tmp_mat2 = (double **) malloc( p * sizeof(double*) );
for(i=0;i<n;i++) {
x[i] = (double *) malloc( p * sizeof(double) );
x_samp[i] = (double *) malloc( (p+1) * sizeof(double) );
};
for(i=0;i<p;i++) {
tmp_mat[i] = (double *) malloc( p * sizeof(double) );
tmp_mat2[i] = (double *) malloc( (p+1) * sizeof(double) );
};
for(i=0;i<n;i++)
for(j=0;j<p;j++)
x[i][j]=X[j*n+i];
/* set the seed */
srand((long)37);
/* flag for refine(), conv == 0 means do k refining steps
// conv == 1 means refine until convergence
*/
conv = 0;
aux = -1.0;
/* resampling approximation */
for(i=0;i<Nres;i++) {
flag = 1;
/* find a candidate */
no_resamples=0;
while( flag == 1) {
if( (++no_resamples) > MAX_NO_RESAMPLES ) {
// Rprintf("\nToo many singular resamples\nAborting\n\n");
return;
};
/* take a sample of the indices */
sample_n_outof_N(p,n-1,b_i);
/* build the submatrix */
for(j=0;j<p;j++) {
for(k=0;k<p;k++)
x_samp[j][k]=x[b_i[j]][k];
x_samp[j][p]=y[b_i[j]];
};
/* solve the system, lu = 1 means
// matrix is singular
*/
flag = lu(x_samp,pp,beta_cand);
};
// Rprintf("\n");
// for(j=0;j<p;j++) Rprintf("%d ", b_i[j]);
// Rprintf("\n");
/* improve the re-sampling candidate */
refine_fast_s(x, y, weights, n, p, res,
tmp, tmp2, tmp_mat, tmp_mat2,
beta_cand, kk, conv, b, rhoc,
&aux, beta_ref, &sc);
if( fabs(sc) < ZERO) {
*sscale = sc;
for(j=0;j<p;j++) bbeta[j] = beta_cand[j];
free(best_scales); free(tmp); free(tmp2);
free(res); free(weights); free(beta_cand);
free(beta_ref); free(b_i);
for(i=0;i<best_r;i++)
free(best_betas[i]);
free(best_betas);
for(i=0; i<n; i++) {
free(x[i]);
free(x_samp[i]);
};
for(i=0; i<p; i++) {
free(tmp_mat[i]);
free(tmp_mat2[i]);
};
free(x); free(x_samp); free(tmp_mat); free(tmp_mat2);
return;
};
if ( loss_rho(res, worst_sc, n, p, rhoc) < b ) {
/* scale will be better */
sc = find_scale(res, b, rhoc, sc, n, p);
k = pos_worst_scale;
best_scales[ k ] = sc;
for(j=0;j<p;j++)
best_betas[k][j] = beta_ref[j];
pos_worst_scale = find_max(best_scales, best_r);
worst_sc = best_scales[pos_worst_scale];
};
};
/* now look for the very best */
best_sc = INFI;
conv = 1;
for(i=0; i<best_r; i++) {
refine_fast_s(x, y, weights, n, p, res,
tmp, tmp2, tmp_mat, tmp_mat2,
best_betas[i], kk, conv, b, rhoc,
best_scales+i, beta_ref,
&aux);
if(aux < best_sc) {
*sscale = best_sc = aux;
for(j=0;j<p;j++)
bbeta[j] = beta_ref[j];
};
};
free(best_scales); free(tmp); free(tmp2);
free(res); free(weights); free(beta_cand);
free(beta_ref); free(b_i);
for(i=0;i<best_r;i++)
free(best_betas[i]);
free(best_betas);
for(i=0; i<n; i++) {
free(x[i]);
free(x_samp[i]);
};
for(i=0; i<p; i++) {
free(tmp_mat[i]);
free(tmp_mat2[i]);
};
free(x); free(x_samp); free(tmp_mat); free(tmp_mat2);
}
