Update upstream source from tag 'upstream/1.10.0+dfsg'

Update to upstream version '1.10.0+dfsg'
with Debian dir fa5485f3d9aea3038a19eff06ba33374ac5c5d7c

1 files changed, 71 insertions, 71 deletions

diff --git a/src/statistical.c b/src/statistical.c
index 807bc25ea..d4b33fd5a 100644
--- a/src/statistical.c
+++ b/src/statistical.c
@@ -2,7 +2,7 @@
 
 // --------------------------------------------------------------------------------------------------------------------
 
-inline long double sum_and_count(long double *series, size_t entries, size_t *count) {
+inline LONG_DOUBLE sum_and_count(const LONG_DOUBLE *series, size_t entries, size_t *count) {
     if(unlikely(entries == 0)) {
         if(likely(count))
             *count = 0;
@@ -18,10 +18,10 @@ inline long double sum_and_count(long double *series, size_t entries, size_t *co
     }
 
     size_t i, c = 0;
-    long double sum = 0;
+    LONG_DOUBLE sum = 0;
 
     for(i = 0; i < entries ; i++) {
-        long double value = series[i];
+        LONG_DOUBLE value = series[i];
         if(unlikely(isnan(value) || isinf(value))) continue;
         c++;
         sum += value;
@@ -36,44 +36,44 @@ inline long double sum_and_count(long double *series, size_t entries, size_t *co
     return sum;
 }
 
-inline long double sum(long double *series, size_t entries) {
+inline LONG_DOUBLE sum(const LONG_DOUBLE *series, size_t entries) {
     return sum_and_count(series, entries, NULL);
 }
 
-inline long double average(long double *series, size_t entries) {
+inline LONG_DOUBLE average(const LONG_DOUBLE *series, size_t entries) {
     size_t count = 0;
-    long double sum = sum_and_count(series, entries, &count);
+    LONG_DOUBLE sum = sum_and_count(series, entries, &count);
 
     if(unlikely(count == 0))
         return NAN;
 
-    return sum / (long double)count;
+    return sum / (LONG_DOUBLE)count;
 }
 
 // --------------------------------------------------------------------------------------------------------------------
 
-long double moving_average(long double *series, size_t entries, size_t period) {
+LONG_DOUBLE moving_average(const LONG_DOUBLE *series, size_t entries, size_t period) {
     if(unlikely(period <= 0))
         return 0.0;
 
     size_t i, count;
-    long double sum = 0, avg = 0;
-    long double p[period];
+    LONG_DOUBLE sum = 0, avg = 0;
+    LONG_DOUBLE p[period];
 
     for(count = 0; count < period ; count++)
         p[count] = 0.0;
 
     for(i = 0, count = 0; i < entries; i++) {
-        long double value = series[i];
+        LONG_DOUBLE value = series[i];
         if(unlikely(isnan(value) || isinf(value))) continue;
 
         if(unlikely(count < period)) {
             sum += value;
-            avg = (count == period - 1) ? sum / (long double)period : 0;
+            avg = (count == period - 1) ? sum / (LONG_DOUBLE)period : 0;
         }
         else {
             sum = sum - p[count % period] + value;
-            avg = sum / (long double)period;
+            avg = sum / (LONG_DOUBLE)period;
         }
 
         p[count % period] = value;
@@ -86,8 +86,8 @@ long double moving_average(long double *series, size_t entries, size_t period) {
 // --------------------------------------------------------------------------------------------------------------------
 
 static int qsort_compare(const void *a, const void *b) {
-    long double *p1 = (long double *)a, *p2 = (long double *)b;
-    long double n1 = *p1, n2 = *p2;
+    LONG_DOUBLE *p1 = (LONG_DOUBLE *)a, *p2 = (LONG_DOUBLE *)b;
+    LONG_DOUBLE n1 = *p1, n2 = *p2;
 
     if(unlikely(isnan(n1) || isnan(n2))) {
         if(isnan(n1) && !isnan(n2)) return -1;
@@ -105,17 +105,17 @@ static int qsort_compare(const void *a, const void *b) {
     return 0;
 }
 
-inline void sort_series(long double *series, size_t entries) {
-    qsort(series, entries, sizeof(long double), qsort_compare);
+inline void sort_series(LONG_DOUBLE *series, size_t entries) {
+    qsort(series, entries, sizeof(LONG_DOUBLE), qsort_compare);
 }
 
-inline long double *copy_series(long double *series, size_t entries) {
-    long double *copy = mallocz(sizeof(long double) * entries);
-    memcpy(copy, series, sizeof(long double) * entries);
+inline LONG_DOUBLE *copy_series(const LONG_DOUBLE *series, size_t entries) {
+    LONG_DOUBLE *copy = mallocz(sizeof(LONG_DOUBLE) * entries);
+    memcpy(copy, series, sizeof(LONG_DOUBLE) * entries);
     return copy;
 }
 
-long double median_on_sorted_series(long double *series, size_t entries) {
+LONG_DOUBLE median_on_sorted_series(const LONG_DOUBLE *series, size_t entries) {
     if(unlikely(entries == 0))
         return NAN;
 
@@ -125,7 +125,7 @@ long double median_on_sorted_series(long double *series, size_t entries) {
     if(unlikely(entries == 2))
         return (series[0] + series[1]) / 2;
 
-    long double avg;
+    LONG_DOUBLE avg;
     if(entries % 2 == 0) {
         size_t m = entries / 2;
         avg = (series[m] + series[m + 1]) / 2;
@@ -137,7 +137,7 @@ long double median_on_sorted_series(long double *series, size_t entries) {
     return avg;
 }
 
-long double median(long double *series, size_t entries) {
+LONG_DOUBLE median(const LONG_DOUBLE *series, size_t entries) {
     if(unlikely(entries == 0))
         return NAN;
 
@@ -147,10 +147,10 @@ long double median(long double *series, size_t entries) {
     if(unlikely(entries == 2))
         return (series[0] + series[1]) / 2;
 
-    long double *copy = copy_series(series, entries);
+    LONG_DOUBLE *copy = copy_series(series, entries);
     sort_series(copy, entries);
 
-    long double avg = median_on_sorted_series(copy, entries);
+    LONG_DOUBLE avg = median_on_sorted_series(copy, entries);
 
     freez(copy);
     return avg;
@@ -158,18 +158,18 @@ long double median(long double *series, size_t entries) {
 
 // --------------------------------------------------------------------------------------------------------------------
 
-long double moving_median(long double *series, size_t entries, size_t period) {
+LONG_DOUBLE moving_median(const LONG_DOUBLE *series, size_t entries, size_t period) {
     if(entries <= period)
         return median(series, entries);
 
-    long double *data = copy_series(series, entries);
+    LONG_DOUBLE *data = copy_series(series, entries);
 
     size_t i;
     for(i = period; i < entries; i++) {
         data[i - period] = median(&series[i - period], period);
     }
 
-    long double avg = median(data, entries - period);
+    LONG_DOUBLE avg = median(data, entries - period);
     freez(data);
     return avg;
 }
@@ -177,13 +177,13 @@ long double moving_median(long double *series, size_t entries, size_t period) {
 // --------------------------------------------------------------------------------------------------------------------
 
 // http://stackoverflow.com/a/15150143/4525767
-long double running_median_estimate(long double *series, size_t entries) {
-    long double median = 0.0f;
-    long double average = 0.0f;
+LONG_DOUBLE running_median_estimate(const LONG_DOUBLE *series, size_t entries) {
+    LONG_DOUBLE median = 0.0f;
+    LONG_DOUBLE average = 0.0f;
     size_t i;
 
     for(i = 0; i < entries ; i++) {
-        long double value = series[i];
+        LONG_DOUBLE value = series[i];
         if(unlikely(isnan(value) || isinf(value))) continue;
 
         average += ( value - average ) * 0.1f; // rough running average.
@@ -195,7 +195,7 @@ long double running_median_estimate(long double *series, size_t entries) {
 
 // --------------------------------------------------------------------------------------------------------------------
 
-long double standard_deviation(long double *series, size_t entries) {
+LONG_DOUBLE standard_deviation(const LONG_DOUBLE *series, size_t entries) {
     if(unlikely(entries < 1))
         return NAN;
 
@@ -203,10 +203,10 @@ long double standard_deviation(long double *series, size_t entries) {
         return series[0];
 
     size_t i, count = 0;
-    long double sum = 0;
+    LONG_DOUBLE sum = 0;
 
     for(i = 0; i < entries ; i++) {
-        long double value = series[i];
+        LONG_DOUBLE value = series[i];
         if(unlikely(isnan(value) || isinf(value))) continue;
 
         count++;
@@ -219,10 +219,10 @@ long double standard_deviation(long double *series, size_t entries) {
     if(unlikely(count == 1))
         return sum;
 
-    long double average = sum / (long double)count;
+    LONG_DOUBLE average = sum / (LONG_DOUBLE)count;
 
     for(i = 0, count = 0, sum = 0; i < entries ; i++) {
-        long double value = series[i];
+        LONG_DOUBLE value = series[i];
         if(unlikely(isnan(value) || isinf(value))) continue;
 
         count++;
@@ -235,29 +235,29 @@ long double standard_deviation(long double *series, size_t entries) {
     if(unlikely(count == 1))
         return average;
 
-    long double variance = sum / (long double)(count - 1); // remove -1 to have a population stddev
+    LONG_DOUBLE variance = sum / (LONG_DOUBLE)(count - 1); // remove -1 to have a population stddev
 
-    long double stddev = sqrtl(variance);
+    LONG_DOUBLE stddev = sqrtl(variance);
     return stddev;
 }
 
 // --------------------------------------------------------------------------------------------------------------------
 
-long double single_exponential_smoothing(long double *series, size_t entries, long double alpha) {
+LONG_DOUBLE single_exponential_smoothing(const LONG_DOUBLE *series, size_t entries, LONG_DOUBLE alpha) {
     size_t i, count = 0;
-    long double level = 0, sum = 0;
+    LONG_DOUBLE level = 0, sum = 0;
 
     if(unlikely(isnan(alpha)))
         alpha = 0.3;
 
     for(i = 0; i < entries ; i++) {
-        long double value = series[i];
+        LONG_DOUBLE value = series[i];
         if(unlikely(isnan(value) || isinf(value))) continue;
         count++;
 
         sum += value;
 
-        long double last_level = level;
+        LONG_DOUBLE last_level = level;
         level = alpha * value + (1.0 - alpha) * last_level;
     }
 
@@ -267,9 +267,9 @@ long double single_exponential_smoothing(long double *series, size_t entries, lo
 // --------------------------------------------------------------------------------------------------------------------
 
 // http://grisha.org/blog/2016/02/16/triple-exponential-smoothing-forecasting-part-ii/
-long double double_exponential_smoothing(long double *series, size_t entries, long double alpha, long double beta, long double *forecast) {
+LONG_DOUBLE double_exponential_smoothing(const LONG_DOUBLE *series, size_t entries, LONG_DOUBLE alpha, LONG_DOUBLE beta, LONG_DOUBLE *forecast) {
     size_t i, count = 0;
-    long double level = series[0], trend, sum;
+    LONG_DOUBLE level = series[0], trend, sum;
 
     if(unlikely(isnan(alpha)))
         alpha = 0.3;
@@ -285,13 +285,13 @@ long double double_exponential_smoothing(long double *series, size_t entries, lo
     sum = series[0];
 
     for(i = 1; i < entries ; i++) {
-        long double value = series[i];
+        LONG_DOUBLE value = series[i];
         if(unlikely(isnan(value) || isinf(value))) continue;
         count++;
 
         sum += value;
 
-        long double last_level = level;
+        LONG_DOUBLE last_level = level;
 
         level = alpha * value + (1.0 - alpha) * (level + trend);
         trend = beta * (level - last_level) + (1.0 - beta) * trend;
@@ -327,24 +327,24 @@ long double double_exponential_smoothing(long double *series, size_t entries, lo
  *   s[t] = γ (Y[t] / a[t]) + (1-γ) s[t-p]
  */
 static int __HoltWinters(
-        long double *series,
+        const LONG_DOUBLE *series,
         int          entries,      // start_time + h
 
-        long double alpha,        // alpha parameter of Holt-Winters Filter.
-        long double beta,         // beta  parameter of Holt-Winters Filter. If set to 0, the function will do exponential smoothing.
-        long double gamma,        // gamma parameter used for the seasonal component. If set to 0, an non-seasonal model is fitted.
+        LONG_DOUBLE alpha,        // alpha parameter of Holt-Winters Filter.
+        LONG_DOUBLE beta,         // beta  parameter of Holt-Winters Filter. If set to 0, the function will do exponential smoothing.
+        LONG_DOUBLE gamma,        // gamma parameter used for the seasonal component. If set to 0, an non-seasonal model is fitted.
 
-        int *seasonal,
-        int *period,
-        long double *a,            // Start value for level (a[0]).
-        long double *b,            // Start value for trend (b[0]).
-        long double *s,            // Vector of start values for the seasonal component (s_1[0] ... s_p[0])
+        const int *seasonal,
+        const int *period,
+        const LONG_DOUBLE *a,      // Start value for level (a[0]).
+        const LONG_DOUBLE *b,      // Start value for trend (b[0]).
+        LONG_DOUBLE *s,            // Vector of start values for the seasonal component (s_1[0] ... s_p[0])
 
         /* return values */
-        long double *SSE,          // The final sum of squared errors achieved in optimizing
-        long double *level,        // Estimated values for the level component (size entries - t + 2)
-        long double *trend,        // Estimated values for the trend component (size entries - t + 2)
-        long double *season        // Estimated values for the seasonal component (size entries - t + 2)
+        LONG_DOUBLE *SSE,          // The final sum of squared errors achieved in optimizing
+        LONG_DOUBLE *level,        // Estimated values for the level component (size entries - t + 2)
+        LONG_DOUBLE *trend,        // Estimated values for the trend component (size entries - t + 2)
+        LONG_DOUBLE *season        // Estimated values for the seasonal component (size entries - t + 2)
 )
 {
     if(unlikely(entries < 4))
@@ -352,13 +352,13 @@ static int __HoltWinters(
 
     int start_time = 2;
 
-    long double res = 0, xhat = 0, stmp = 0;
+    LONG_DOUBLE res = 0, xhat = 0, stmp = 0;
     int i, i0, s0;
 
     /* copy start values to the beginning of the vectors */
     level[0] = *a;
     if(beta > 0) trend[0] = *b;
-    if(gamma > 0) memcpy(season, s, *period * sizeof(long double));
+    if(gamma > 0) memcpy(season, s, *period * sizeof(LONG_DOUBLE));
 
     for(i = start_time - 1; i < entries; i++) {
         /* indices for period i */
@@ -404,7 +404,7 @@ static int __HoltWinters(
     return 1;
 }
 
-long double holtwinters(long double *series, size_t entries, long double alpha, long double beta, long double gamma, long double *forecast) {
+LONG_DOUBLE holtwinters(const LONG_DOUBLE *series, size_t entries, LONG_DOUBLE alpha, LONG_DOUBLE beta, LONG_DOUBLE gamma, LONG_DOUBLE *forecast) {
     if(unlikely(isnan(alpha)))
         alpha = 0.3;
 
@@ -416,15 +416,15 @@ long double holtwinters(long double *series, size_t entries, long double alpha,
 
     int seasonal = 0;
     int period = 0;
-    long double a0 = series[0];
-    long double b0 = 0;
-    long double s[] = {};
+    LONG_DOUBLE a0 = series[0];
+    LONG_DOUBLE b0 = 0;
+    LONG_DOUBLE s[] = {};
 
-    long double errors = 0.0;
+    LONG_DOUBLE errors = 0.0;
     size_t nb_computations = entries;
-    long double *estimated_level  = callocz(nb_computations, sizeof(long double));
-    long double *estimated_trend  = callocz(nb_computations, sizeof(long double));
-    long double *estimated_season = callocz(nb_computations, sizeof(long double));
+    LONG_DOUBLE *estimated_level  = callocz(nb_computations, sizeof(LONG_DOUBLE));
+    LONG_DOUBLE *estimated_trend  = callocz(nb_computations, sizeof(LONG_DOUBLE));
+    LONG_DOUBLE *estimated_season = callocz(nb_computations, sizeof(LONG_DOUBLE));
 
     int ret = __HoltWinters(
             series,
@@ -443,7 +443,7 @@ long double holtwinters(long double *series, size_t entries, long double alpha,
             estimated_season
     );
 
-    long double value = estimated_level[nb_computations - 1];
+    LONG_DOUBLE value = estimated_level[nb_computations - 1];
 
     if(forecast)
         *forecast = 0.0;