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authorDaniel Baumann <daniel.baumann@progress-linux.org>2018-11-07 12:19:29 +0000
committerDaniel Baumann <daniel.baumann@progress-linux.org>2018-11-07 12:20:17 +0000
commita64a253794ac64cb40befee54db53bde17dd0d49 (patch)
treec1024acc5f6e508814b944d99f112259bb28b1be /libnetdata/statistical
parentNew upstream version 1.10.0+dfsg (diff)
downloadnetdata-a64a253794ac64cb40befee54db53bde17dd0d49.tar.xz
netdata-a64a253794ac64cb40befee54db53bde17dd0d49.zip
New upstream version 1.11.0+dfsgupstream/1.11.0+dfsg
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
Diffstat (limited to 'libnetdata/statistical')
-rw-r--r--libnetdata/statistical/Makefile.am9
-rw-r--r--libnetdata/statistical/Makefile.in464
-rw-r--r--libnetdata/statistical/README.md0
-rw-r--r--libnetdata/statistical/statistical.c452
-rw-r--r--libnetdata/statistical/statistical.h30
5 files changed, 955 insertions, 0 deletions
diff --git a/libnetdata/statistical/Makefile.am b/libnetdata/statistical/Makefile.am
new file mode 100644
index 000000000..1cb69ed99
--- /dev/null
+++ b/libnetdata/statistical/Makefile.am
@@ -0,0 +1,9 @@
+# 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/Makefile.in b/libnetdata/statistical/Makefile.in
new file mode 100644
index 000000000..8209a1ecb
--- /dev/null
+++ b/libnetdata/statistical/Makefile.in
@@ -0,0 +1,464 @@
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+# @configure_input@
+
+# Copyright (C) 1994-2013 Free Software Foundation, Inc.
+
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+# gives unlimited permission to copy and/or distribute it,
+# with or without modifications, as long as this notice is preserved.
+
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY, to the extent permitted by law; without
+# even the implied warranty of MERCHANTABILITY or FITNESS FOR A
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diff --git a/libnetdata/statistical/README.md b/libnetdata/statistical/README.md
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/libnetdata/statistical/README.md
diff --git a/libnetdata/statistical/statistical.c b/libnetdata/statistical/statistical.c
new file mode 100644
index 000000000..15792fd15
--- /dev/null
+++ b/libnetdata/statistical/statistical.c
@@ -0,0 +1,452 @@
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+#include "../libnetdata.h"
+
+LONG_DOUBLE default_single_exponential_smoothing_alpha = 0.1;
+
+void log_series_to_stderr(LONG_DOUBLE *series, size_t entries, calculated_number result, const char *msg) {
+ const LONG_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, "%" LONG_DOUBLE_MODIFIER, *value);
+ }
+ fprintf(stderr, " ] results in " CALCULATED_NUMBER_FORMAT "\n", result);
+}
+
+// --------------------------------------------------------------------------------------------------------------------
+
+inline LONG_DOUBLE sum_and_count(const LONG_DOUBLE *series, size_t entries, size_t *count) {
+ const LONG_DOUBLE *value, *end = &series[entries];
+ LONG_DOUBLE sum = 0;
+ size_t c = 0;
+
+ for(value = series; value < end ; value++) {
+ if(isnormal(*value)) {
+ sum += *value;
+ c++;
+ }
+ }
+
+ if(unlikely(!c)) sum = NAN;
+ if(likely(count)) *count = c;
+
+ return sum;
+}
+
+inline LONG_DOUBLE sum(const LONG_DOUBLE *series, size_t entries) {
+ return sum_and_count(series, entries, NULL);
+}
+
+inline LONG_DOUBLE average(const LONG_DOUBLE *series, size_t entries) {
+ size_t count = 0;
+ LONG_DOUBLE sum = sum_and_count(series, entries, &count);
+
+ if(unlikely(!count)) return NAN;
+ return sum / (LONG_DOUBLE)count;
+}
+
+// --------------------------------------------------------------------------------------------------------------------
+
+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];
+
+ for(count = 0; count < period ; count++)
+ p[count] = 0.0;
+
+ for(i = 0, count = 0; i < entries; i++) {
+ LONG_DOUBLE value = series[i];
+ if(unlikely(!isnormal(value))) continue;
+
+ if(unlikely(count < period)) {
+ sum += value;
+ avg = (count == period - 1) ? sum / (LONG_DOUBLE)period : 0;
+ }
+ else {
+ sum = sum - p[count % period] + value;
+ avg = sum / (LONG_DOUBLE)period;
+ }
+
+ p[count % period] = value;
+ count++;
+ }
+
+ return avg;
+}
+
+// --------------------------------------------------------------------------------------------------------------------
+
+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;
+
+ 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(LONG_DOUBLE *series, size_t entries) {
+ qsort(series, entries, sizeof(LONG_DOUBLE), qsort_compare);
+}
+
+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(const LONG_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;
+
+ LONG_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;
+}
+
+LONG_DOUBLE median(const LONG_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;
+
+ LONG_DOUBLE *copy = copy_series(series, entries);
+ sort_series(copy, entries);
+
+ LONG_DOUBLE avg = median_on_sorted_series(copy, entries);
+
+ freez(copy);
+ return avg;
+}
+
+// --------------------------------------------------------------------------------------------------------------------
+
+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);
+
+ size_t i;
+ for(i = period; i < entries; i++) {
+ data[i - period] = median(&series[i - period], period);
+ }
+
+ LONG_DOUBLE avg = median(data, entries - period);
+ freez(data);
+ return avg;
+}
+
+// --------------------------------------------------------------------------------------------------------------------
+
+// http://stackoverflow.com/a/15150143/4525767
+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];
+ if(unlikely(!isnormal(value))) continue;
+
+ average += ( value - average ) * 0.1f; // rough running average.
+ median += copysignl( average * 0.01, value - median );
+ }
+
+ return median;
+}
+
+// --------------------------------------------------------------------------------------------------------------------
+
+LONG_DOUBLE standard_deviation(const LONG_DOUBLE *series, size_t entries) {
+ if(unlikely(entries == 0)) return NAN;
+ if(unlikely(entries == 1)) return series[0];
+
+ const LONG_DOUBLE *value, *end = &series[entries];
+ size_t count;
+ LONG_DOUBLE sum;
+
+ for(count = 0, sum = 0, value = series ; value < end ;value++) {
+ if(likely(isnormal(*value))) {
+ count++;
+ sum += *value;
+ }
+ }
+
+ if(unlikely(count == 0)) return NAN;
+ if(unlikely(count == 1)) return sum;
+
+ LONG_DOUBLE average = sum / (LONG_DOUBLE)count;
+
+ for(count = 0, sum = 0, value = series ; value < end ;value++) {
+ if(isnormal(*value)) {
+ count++;
+ sum += powl(*value - average, 2);
+ }
+ }
+
+ if(unlikely(count == 0)) return NAN;
+ if(unlikely(count == 1)) return average;
+
+ LONG_DOUBLE variance = sum / (LONG_DOUBLE)(count); // remove -1 from count to have a population stddev
+ LONG_DOUBLE stddev = sqrtl(variance);
+ return stddev;
+}
+
+// --------------------------------------------------------------------------------------------------------------------
+
+LONG_DOUBLE single_exponential_smoothing(const LONG_DOUBLE *series, size_t entries, LONG_DOUBLE alpha) {
+ if(unlikely(entries == 0))
+ return NAN;
+
+ if(unlikely(isnan(alpha)))
+ alpha = default_single_exponential_smoothing_alpha;
+
+ const LONG_DOUBLE *value = series, *end = &series[entries];
+ LONG_DOUBLE level = (1.0 - alpha) * (*value);
+
+ for(value++ ; value < end; value++) {
+ if(likely(isnormal(*value)))
+ level = alpha * (*value) + (1.0 - alpha) * level;
+ }
+
+ return level;
+}
+
+LONG_DOUBLE single_exponential_smoothing_reverse(const LONG_DOUBLE *series, size_t entries, LONG_DOUBLE alpha) {
+ if(unlikely(entries == 0))
+ return NAN;
+
+ if(unlikely(isnan(alpha)))
+ alpha = default_single_exponential_smoothing_alpha;
+
+ const LONG_DOUBLE *value = &series[entries -1];
+ LONG_DOUBLE level = (1.0 - alpha) * (*value);
+
+ for(value++ ; value >= series; value--) {
+ if(likely(isnormal(*value)))
+ level = alpha * (*value) + (1.0 - alpha) * level;
+ }
+
+ return level;
+}
+
+// --------------------------------------------------------------------------------------------------------------------
+
+// http://grisha.org/blog/2016/02/16/triple-exponential-smoothing-forecasting-part-ii/
+LONG_DOUBLE double_exponential_smoothing(const LONG_DOUBLE *series, size_t entries, LONG_DOUBLE alpha, LONG_DOUBLE beta, LONG_DOUBLE *forecast) {
+ if(unlikely(entries == 0))
+ return NAN;
+
+ LONG_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 LONG_DOUBLE *value = series;
+ for(value++ ; value >= series; value--) {
+ if(likely(isnormal(*value))) {
+
+ LONG_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 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.
+
+ 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)
+)
+{
+ if(unlikely(entries < 4))
+ return 0;
+
+ int start_time = 2;
+
+ 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));
+
+ 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;
+}
+
+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;
+
+ if(unlikely(isnan(beta)))
+ beta = 0.05;
+
+ if(unlikely(isnan(gamma)))
+ gamma = 0;
+
+ int seasonal = 0;
+ int period = 0;
+ LONG_DOUBLE a0 = series[0];
+ LONG_DOUBLE b0 = 0;
+ LONG_DOUBLE s[] = {};
+
+ 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));
+
+ int ret = __HoltWinters(
+ series,
+ (int)entries,
+ alpha,
+ beta,
+ gamma,
+ &seasonal,
+ &period,
+ &a0,
+ &b0,
+ s,
+ &errors,
+ estimated_level,
+ estimated_trend,
+ estimated_season
+ );
+
+ LONG_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
new file mode 100644
index 000000000..586c6b34a
--- /dev/null
+++ b/libnetdata/statistical/statistical.h
@@ -0,0 +1,30 @@
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+#ifndef NETDATA_STATISTICAL_H
+#define NETDATA_STATISTICAL_H 1
+
+#include "../libnetdata.h"
+
+#ifndef isnormal
+#define isnormal(x) (fpclassify(x) == FP_NORMAL)
+#endif
+
+extern void log_series_to_stderr(LONG_DOUBLE *series, size_t entries, calculated_number result, const char *msg);
+
+extern LONG_DOUBLE average(const LONG_DOUBLE *series, size_t entries);
+extern LONG_DOUBLE moving_average(const LONG_DOUBLE *series, size_t entries, size_t period);
+extern LONG_DOUBLE median(const LONG_DOUBLE *series, size_t entries);
+extern LONG_DOUBLE moving_median(const LONG_DOUBLE *series, size_t entries, size_t period);
+extern LONG_DOUBLE running_median_estimate(const LONG_DOUBLE *series, size_t entries);
+extern LONG_DOUBLE standard_deviation(const LONG_DOUBLE *series, size_t entries);
+extern LONG_DOUBLE single_exponential_smoothing(const LONG_DOUBLE *series, size_t entries, LONG_DOUBLE alpha);
+extern LONG_DOUBLE single_exponential_smoothing_reverse(const LONG_DOUBLE *series, size_t entries, LONG_DOUBLE alpha);
+extern LONG_DOUBLE double_exponential_smoothing(const LONG_DOUBLE *series, size_t entries, LONG_DOUBLE alpha, LONG_DOUBLE beta, LONG_DOUBLE *forecast);
+extern LONG_DOUBLE holtwinters(const LONG_DOUBLE *series, size_t entries, LONG_DOUBLE alpha, LONG_DOUBLE beta, LONG_DOUBLE gamma, LONG_DOUBLE *forecast);
+extern LONG_DOUBLE sum_and_count(const LONG_DOUBLE *series, size_t entries, size_t *count);
+extern LONG_DOUBLE sum(const LONG_DOUBLE *series, size_t entries);
+extern LONG_DOUBLE median_on_sorted_series(const LONG_DOUBLE *series, size_t entries);
+extern LONG_DOUBLE *copy_series(const LONG_DOUBLE *series, size_t entries);
+extern void sort_series(LONG_DOUBLE *series, size_t entries);
+
+#endif //NETDATA_STATISTICAL_H