/* Select the Pivot Element .
Strategy 1: Select a random sample of size O(logN) from the array and compute median
log to base 2 N is a good sampling choice as it scales well for smaller
and larger data sets alike
*/
int select_pivot(int low_idx, int high_idx)
{
int i = 0;
int N = floor_log2(high_idx - low_idx + 1);
int *random_indexes = calloc(N, sizeof(int));
// array
double *random_elements = calloc(N, sizeof(double));
// array
double final_median = 0;
srand(time(NULL)); // Seed the RNG
for (i = 0; i < N; i++) {
random_indexes[i] = rand() % (high_idx - low_idx + 1) + low_idx;
random_elements[i] = buf[random_indexes[i]];
}
final_median =
(median_low(random_elements, N) +
median_high(random_elements, N)) / 2;
// Free allocated memory
free(random_indexes);
free(random_elements);
return final_median;
}
void scaling_norm(double *data, int rows, int cols, double trim, int baseline, int logscale){
int i,j;
double beta = 0.0;
double mean_baseline = 0.0;
double mean_treatment = 0.0;
double med_intensity = 0.0;
double *buffer;
double *row_buffer;
if (logscale){
for (j=0; j < cols; j++){
for(i=0; i < rows; i++){
data[j*rows + i] = log(data[j*rows + i])/log(2.0);
}
}
}
if (baseline == -1){
/* pick the array with median overall (total) intensity as baseline */
buffer = Calloc(cols,double);
for (j=0; j < cols; j++){
for(i=0; i < rows; i++){
buffer[j]+=data[j*rows + i];
}
}
med_intensity = median_low(buffer, cols);
for (j = 0; j < cols; j++){
if (buffer[j] == med_intensity){
baseline = j;
break;
}
}
Free(buffer);
mean_baseline = mean_trim(&data[baseline*rows],rows,trim);
} else if (baseline == -2){
