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-rw-r--r--libnetdata/statistical/Makefile.am8
-rw-r--r--libnetdata/statistical/README.md12
-rw-r--r--libnetdata/statistical/statistical.c460
-rw-r--r--libnetdata/statistical/statistical.h34
4 files changed, 0 insertions, 514 deletions
diff --git a/libnetdata/statistical/Makefile.am b/libnetdata/statistical/Makefile.am
deleted file mode 100644
index 161784b8f..000000000
--- a/libnetdata/statistical/Makefile.am
+++ /dev/null
@@ -1,8 +0,0 @@
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-AUTOMAKE_OPTIONS = subdir-objects
-MAINTAINERCLEANFILES = $(srcdir)/Makefile.in
-
-dist_noinst_DATA = \
- README.md \
- $(NULL)
diff --git a/libnetdata/statistical/README.md b/libnetdata/statistical/README.md
deleted file mode 100644
index 937c26ac6..000000000
--- a/libnetdata/statistical/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
-<!--
-title: "Statistical functions"
-custom_edit_url: https://github.com/netdata/netdata/edit/master/libnetdata/statistical/README.md
-sidebar_label: "Statistical functions"
-learn_status: "Published"
-learn_topic_type: "Tasks"
-learn_rel_path: "Developers/libnetdata"
--->
-
-# Statistical functions
-
-A library for easy and fast calculations of statistical measurements like average, median etc.
diff --git a/libnetdata/statistical/statistical.c b/libnetdata/statistical/statistical.c
deleted file mode 100644
index ef9fe4e56..000000000
--- a/libnetdata/statistical/statistical.c
+++ /dev/null
@@ -1,460 +0,0 @@
-// SPDX-License-Identifier: GPL-3.0-or-later
-
-#include "../libnetdata.h"
-
-NETDATA_DOUBLE default_single_exponential_smoothing_alpha = 0.1;
-
-void log_series_to_stderr(NETDATA_DOUBLE *series, size_t entries, NETDATA_DOUBLE result, const char *msg) {
- const NETDATA_DOUBLE *value, *end = &series[entries];
-
- fprintf(stderr, "%s of %zu entries [ ", msg, entries);
- for(value = series; value < end ;value++) {
- if(value != series) fprintf(stderr, ", ");
- fprintf(stderr, "%" NETDATA_DOUBLE_MODIFIER, *value);
- }
- fprintf(stderr, " ] results in " NETDATA_DOUBLE_FORMAT "\n", result);
-}
-
-// --------------------------------------------------------------------------------------------------------------------
-
-inline NETDATA_DOUBLE sum_and_count(const NETDATA_DOUBLE *series, size_t entries, size_t *count) {
- const NETDATA_DOUBLE *value, *end = &series[entries];
- NETDATA_DOUBLE sum = 0;
- size_t c = 0;
-
- for(value = series; value < end ; value++) {
- if(netdata_double_isnumber(*value)) {
- sum += *value;
- c++;
- }
- }
-
- if(unlikely(!c)) sum = NAN;
- if(likely(count)) *count = c;
-
- return sum;
-}
-
-inline NETDATA_DOUBLE sum(const NETDATA_DOUBLE *series, size_t entries) {
- return sum_and_count(series, entries, NULL);
-}
-
-inline NETDATA_DOUBLE average(const NETDATA_DOUBLE *series, size_t entries) {
- size_t count = 0;
- NETDATA_DOUBLE sum = sum_and_count(series, entries, &count);
-
- if(unlikely(!count)) return NAN;
- return sum / (NETDATA_DOUBLE)count;
-}
-
-// --------------------------------------------------------------------------------------------------------------------
-
-NETDATA_DOUBLE moving_average(const NETDATA_DOUBLE *series, size_t entries, size_t period) {
- if(unlikely(period <= 0))
- return 0.0;
-
- size_t i, count;
- NETDATA_DOUBLE sum = 0, avg = 0;
- NETDATA_DOUBLE p[period];
-
- for(count = 0; count < period ; count++)
- p[count] = 0.0;
-
- for(i = 0, count = 0; i < entries; i++) {
- NETDATA_DOUBLE value = series[i];
- if(unlikely(!netdata_double_isnumber(value))) continue;
-
- if(unlikely(count < period)) {
- sum += value;
- avg = (count == period - 1) ? sum / (NETDATA_DOUBLE)period : 0;
- }
- else {
- sum = sum - p[count % period] + value;
- avg = sum / (NETDATA_DOUBLE)period;
- }
-
- p[count % period] = value;
- count++;
- }
-
- return avg;
-}
-
-// --------------------------------------------------------------------------------------------------------------------
-
-static int qsort_compare(const void *a, const void *b) {
- NETDATA_DOUBLE *p1 = (NETDATA_DOUBLE *)a, *p2 = (NETDATA_DOUBLE *)b;
- NETDATA_DOUBLE n1 = *p1, n2 = *p2;
-
- if(unlikely(isnan(n1) || isnan(n2))) {
- if(isnan(n1) && !isnan(n2)) return -1;
- if(!isnan(n1) && isnan(n2)) return 1;
- return 0;
- }
- if(unlikely(isinf(n1) || isinf(n2))) {
- if(!isinf(n1) && isinf(n2)) return -1;
- if(isinf(n1) && !isinf(n2)) return 1;
- return 0;
- }
-
- if(unlikely(n1 < n2)) return -1;
- if(unlikely(n1 > n2)) return 1;
- return 0;
-}
-
-inline void sort_series(NETDATA_DOUBLE *series, size_t entries) {
- qsort(series, entries, sizeof(NETDATA_DOUBLE), qsort_compare);
-}
-
-inline NETDATA_DOUBLE *copy_series(const NETDATA_DOUBLE *series, size_t entries) {
- NETDATA_DOUBLE *copy = mallocz(sizeof(NETDATA_DOUBLE) * entries);
- memcpy(copy, series, sizeof(NETDATA_DOUBLE) * entries);
- return copy;
-}
-
-NETDATA_DOUBLE median_on_sorted_series(const NETDATA_DOUBLE *series, size_t entries) {
- if(unlikely(entries == 0)) return NAN;
- if(unlikely(entries == 1)) return series[0];
- if(unlikely(entries == 2)) return (series[0] + series[1]) / 2;
-
- NETDATA_DOUBLE average;
- if(entries % 2 == 0) {
- size_t m = entries / 2;
- average = (series[m] + series[m + 1]) / 2;
- }
- else {
- average = series[entries / 2];
- }
-
- return average;
-}
-
-NETDATA_DOUBLE median(const NETDATA_DOUBLE *series, size_t entries) {
- if(unlikely(entries == 0)) return NAN;
- if(unlikely(entries == 1)) return series[0];
-
- if(unlikely(entries == 2))
- return (series[0] + series[1]) / 2;
-
- NETDATA_DOUBLE *copy = copy_series(series, entries);
- sort_series(copy, entries);
-
- NETDATA_DOUBLE avg = median_on_sorted_series(copy, entries);
-
- freez(copy);
- return avg;
-}
-
-// --------------------------------------------------------------------------------------------------------------------
-
-NETDATA_DOUBLE moving_median(const NETDATA_DOUBLE *series, size_t entries, size_t period) {
- if(entries <= period)
- return median(series, entries);
-
- NETDATA_DOUBLE *data = copy_series(series, entries);
-
- size_t i;
- for(i = period; i < entries; i++) {
- data[i - period] = median(&series[i - period], period);
- }
-
- NETDATA_DOUBLE avg = median(data, entries - period);
- freez(data);
- return avg;
-}
-
-// --------------------------------------------------------------------------------------------------------------------
-
-// http://stackoverflow.com/a/15150143/4525767
-NETDATA_DOUBLE running_median_estimate(const NETDATA_DOUBLE *series, size_t entries) {
- NETDATA_DOUBLE median = 0.0f;
- NETDATA_DOUBLE average = 0.0f;
- size_t i;
-
- for(i = 0; i < entries ; i++) {
- NETDATA_DOUBLE value = series[i];
- if(unlikely(!netdata_double_isnumber(value))) continue;
-
- average += ( value - average ) * 0.1f; // rough running average.
- median += copysignndd( average * 0.01, value - median );
- }
-
- return median;
-}
-
-// --------------------------------------------------------------------------------------------------------------------
-
-NETDATA_DOUBLE standard_deviation(const NETDATA_DOUBLE *series, size_t entries) {
- if(unlikely(entries == 0)) return NAN;
- if(unlikely(entries == 1)) return series[0];
-
- const NETDATA_DOUBLE *value, *end = &series[entries];
- size_t count;
- NETDATA_DOUBLE sum;
-
- for(count = 0, sum = 0, value = series ; value < end ;value++) {
- if(likely(netdata_double_isnumber(*value))) {
- count++;
- sum += *value;
- }
- }
-
- if(unlikely(count == 0)) return NAN;
- if(unlikely(count == 1)) return sum;
-
- NETDATA_DOUBLE average = sum / (NETDATA_DOUBLE)count;
-
- for(count = 0, sum = 0, value = series ; value < end ;value++) {
- if(netdata_double_isnumber(*value)) {
- count++;
- sum += powndd(*value - average, 2);
- }
- }
-
- if(unlikely(count == 0)) return NAN;
- if(unlikely(count == 1)) return average;
-
- NETDATA_DOUBLE variance = sum / (NETDATA_DOUBLE)(count); // remove -1 from count to have a population stddev
- NETDATA_DOUBLE stddev = sqrtndd(variance);
- return stddev;
-}
-
-// --------------------------------------------------------------------------------------------------------------------
-
-NETDATA_DOUBLE single_exponential_smoothing(const NETDATA_DOUBLE *series, size_t entries, NETDATA_DOUBLE alpha) {
- if(unlikely(entries == 0))
- return NAN;
-
- if(unlikely(isnan(alpha)))
- alpha = default_single_exponential_smoothing_alpha;
-
- const NETDATA_DOUBLE *value = series, *end = &series[entries];
- NETDATA_DOUBLE level = (1.0 - alpha) * (*value);
-
- for(value++ ; value < end; value++) {
- if(likely(netdata_double_isnumber(*value)))
- level = alpha * (*value) + (1.0 - alpha) * level;
- }
-
- return level;
-}
-
-NETDATA_DOUBLE single_exponential_smoothing_reverse(const NETDATA_DOUBLE *series, size_t entries, NETDATA_DOUBLE alpha) {
- if(unlikely(entries == 0))
- return NAN;
-
- if(unlikely(isnan(alpha)))
- alpha = default_single_exponential_smoothing_alpha;
-
- const NETDATA_DOUBLE *value = &series[entries -1];
- NETDATA_DOUBLE level = (1.0 - alpha) * (*value);
-
- for(value++ ; value >= series; value--) {
- if(likely(netdata_double_isnumber(*value)))
- level = alpha * (*value) + (1.0 - alpha) * level;
- }
-
- return level;
-}
-
-// --------------------------------------------------------------------------------------------------------------------
-
-// http://grisha.org/blog/2016/02/16/triple-exponential-smoothing-forecasting-part-ii/
-NETDATA_DOUBLE double_exponential_smoothing(const NETDATA_DOUBLE *series, size_t entries,
- NETDATA_DOUBLE alpha,
- NETDATA_DOUBLE beta,
- NETDATA_DOUBLE *forecast) {
- if(unlikely(entries == 0))
- return NAN;
-
- NETDATA_DOUBLE level, trend;
-
- if(unlikely(isnan(alpha)))
- alpha = 0.3;
-
- if(unlikely(isnan(beta)))
- beta = 0.05;
-
- level = series[0];
-
- if(likely(entries > 1))
- trend = series[1] - series[0];
- else
- trend = 0;
-
- const NETDATA_DOUBLE *value = series;
- for(value++ ; value >= series; value--) {
- if(likely(netdata_double_isnumber(*value))) {
- NETDATA_DOUBLE last_level = level;
- level = alpha * *value + (1.0 - alpha) * (level + trend);
- trend = beta * (level - last_level) + (1.0 - beta) * trend;
-
- }
- }
-
- if(forecast)
- *forecast = level + trend;
-
- return level;
-}
-
-// --------------------------------------------------------------------------------------------------------------------
-
-/*
- * Based on th R implementation
- *
- * a: level component
- * b: trend component
- * s: seasonal component
- *
- * Additive:
- *
- * Yhat[t+h] = a[t] + h * b[t] + s[t + 1 + (h - 1) mod p],
- * a[t] = α (Y[t] - s[t-p]) + (1-α) (a[t-1] + b[t-1])
- * b[t] = β (a[t] - a[t-1]) + (1-β) b[t-1]
- * s[t] = γ (Y[t] - a[t]) + (1-γ) s[t-p]
- *
- * Multiplicative:
- *
- * Yhat[t+h] = (a[t] + h * b[t]) * s[t + 1 + (h - 1) mod p],
- * a[t] = α (Y[t] / s[t-p]) + (1-α) (a[t-1] + b[t-1])
- * b[t] = β (a[t] - a[t-1]) + (1-β) b[t-1]
- * s[t] = γ (Y[t] / a[t]) + (1-γ) s[t-p]
- */
-static int __HoltWinters(
- const NETDATA_DOUBLE *series,
- int entries, // start_time + h
-
- NETDATA_DOUBLE alpha, // alpha parameter of Holt-Winters Filter.
- NETDATA_DOUBLE
- beta, // beta parameter of Holt-Winters Filter. If set to 0, the function will do exponential smoothing.
- NETDATA_DOUBLE
- gamma, // gamma parameter used for the seasonal component. If set to 0, an non-seasonal model is fitted.
-
- const int *seasonal,
- const int *period,
- const NETDATA_DOUBLE *a, // Start value for level (a[0]).
- const NETDATA_DOUBLE *b, // Start value for trend (b[0]).
- NETDATA_DOUBLE *s, // Vector of start values for the seasonal component (s_1[0] ... s_p[0])
-
- /* return values */
- NETDATA_DOUBLE *SSE, // The final sum of squared errors achieved in optimizing
- NETDATA_DOUBLE *level, // Estimated values for the level component (size entries - t + 2)
- NETDATA_DOUBLE *trend, // Estimated values for the trend component (size entries - t + 2)
- NETDATA_DOUBLE *season // Estimated values for the seasonal component (size entries - t + 2)
-)
-{
- if(unlikely(entries < 4))
- return 0;
-
- int start_time = 2;
-
- NETDATA_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(NETDATA_DOUBLE));
-
- for(i = start_time - 1; i < entries; i++) {
- /* indices for period i */
- i0 = i - start_time + 2;
- s0 = i0 + *period - 1;
-
- /* forecast *for* period i */
- xhat = level[i0 - 1] + (beta > 0 ? trend[i0 - 1] : 0);
- stmp = gamma > 0 ? season[s0 - *period] : (*seasonal != 1);
- if (*seasonal == 1)
- xhat += stmp;
- else
- xhat *= stmp;
-
- /* Sum of Squared Errors */
- res = series[i] - xhat;
- *SSE += res * res;
-
- /* estimate of level *in* period i */
- if (*seasonal == 1)
- level[i0] = alpha * (series[i] - stmp)
- + (1 - alpha) * (level[i0 - 1] + trend[i0 - 1]);
- else
- level[i0] = alpha * (series[i] / stmp)
- + (1 - alpha) * (level[i0 - 1] + trend[i0 - 1]);
-
- /* estimate of trend *in* period i */
- if (beta > 0)
- trend[i0] = beta * (level[i0] - level[i0 - 1])
- + (1 - beta) * trend[i0 - 1];
-
- /* estimate of seasonal component *in* period i */
- if (gamma > 0) {
- if (*seasonal == 1)
- season[s0] = gamma * (series[i] - level[i0])
- + (1 - gamma) * stmp;
- else
- season[s0] = gamma * (series[i] / level[i0])
- + (1 - gamma) * stmp;
- }
- }
-
- return 1;
-}
-
-NETDATA_DOUBLE holtwinters(const NETDATA_DOUBLE *series, size_t entries,
- NETDATA_DOUBLE alpha,
- NETDATA_DOUBLE beta,
- NETDATA_DOUBLE gamma,
- NETDATA_DOUBLE *forecast) {
- if(unlikely(isnan(alpha)))
- alpha = 0.3;
-
- if(unlikely(isnan(beta)))
- beta = 0.05;
-
- if(unlikely(isnan(gamma)))
- gamma = 0;
-
- int seasonal = 0;
- int period = 0;
- NETDATA_DOUBLE a0 = series[0];
- NETDATA_DOUBLE b0 = 0;
- NETDATA_DOUBLE s[] = {};
-
- NETDATA_DOUBLE errors = 0.0;
- size_t nb_computations = entries;
- NETDATA_DOUBLE *estimated_level = callocz(nb_computations, sizeof(NETDATA_DOUBLE));
- NETDATA_DOUBLE *estimated_trend = callocz(nb_computations, sizeof(NETDATA_DOUBLE));
- NETDATA_DOUBLE *estimated_season = callocz(nb_computations, sizeof(NETDATA_DOUBLE));
-
- int ret = __HoltWinters(
- series,
- (int)entries,
- alpha,
- beta,
- gamma,
- &seasonal,
- &period,
- &a0,
- &b0,
- s,
- &errors,
- estimated_level,
- estimated_trend,
- estimated_season
- );
-
- NETDATA_DOUBLE value = estimated_level[nb_computations - 1];
-
- if(forecast)
- *forecast = 0.0;
-
- freez(estimated_level);
- freez(estimated_trend);
- freez(estimated_season);
-
- if(!ret)
- return 0.0;
-
- return value;
-}
diff --git a/libnetdata/statistical/statistical.h b/libnetdata/statistical/statistical.h
deleted file mode 100644
index f3ecfadb4..000000000
--- a/libnetdata/statistical/statistical.h
+++ /dev/null
@@ -1,34 +0,0 @@
-// SPDX-License-Identifier: GPL-3.0-or-later
-
-#ifndef NETDATA_STATISTICAL_H
-#define NETDATA_STATISTICAL_H 1
-
-#include "../libnetdata.h"
-
-void log_series_to_stderr(NETDATA_DOUBLE *series, size_t entries, NETDATA_DOUBLE result, const char *msg);
-
-NETDATA_DOUBLE average(const NETDATA_DOUBLE *series, size_t entries);
-NETDATA_DOUBLE moving_average(const NETDATA_DOUBLE *series, size_t entries, size_t period);
-NETDATA_DOUBLE median(const NETDATA_DOUBLE *series, size_t entries);
-NETDATA_DOUBLE moving_median(const NETDATA_DOUBLE *series, size_t entries, size_t period);
-NETDATA_DOUBLE running_median_estimate(const NETDATA_DOUBLE *series, size_t entries);
-NETDATA_DOUBLE standard_deviation(const NETDATA_DOUBLE *series, size_t entries);
-NETDATA_DOUBLE single_exponential_smoothing(const NETDATA_DOUBLE *series, size_t entries, NETDATA_DOUBLE alpha);
-extern NETDATA_DOUBLE
-single_exponential_smoothing_reverse(const NETDATA_DOUBLE *series, size_t entries, NETDATA_DOUBLE alpha);
-NETDATA_DOUBLE double_exponential_smoothing(const NETDATA_DOUBLE *series, size_t entries,
- NETDATA_DOUBLE alpha,
- NETDATA_DOUBLE beta,
- NETDATA_DOUBLE *forecast);
-NETDATA_DOUBLE holtwinters(const NETDATA_DOUBLE *series, size_t entries,
- NETDATA_DOUBLE alpha,
- NETDATA_DOUBLE beta,
- NETDATA_DOUBLE gamma,
- NETDATA_DOUBLE *forecast);
-NETDATA_DOUBLE sum_and_count(const NETDATA_DOUBLE *series, size_t entries, size_t *count);
-NETDATA_DOUBLE sum(const NETDATA_DOUBLE *series, size_t entries);
-NETDATA_DOUBLE median_on_sorted_series(const NETDATA_DOUBLE *series, size_t entries);
-NETDATA_DOUBLE *copy_series(const NETDATA_DOUBLE *series, size_t entries);
-void sort_series(NETDATA_DOUBLE *series, size_t entries);
-
-#endif //NETDATA_STATISTICAL_H
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