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-rw-r--r--example/ndpiReader.c37
-rw-r--r--src/include/ndpi_typedefs.h18
-rw-r--r--src/lib/ndpi_analyze.c44
3 files changed, 87 insertions, 12 deletions
diff --git a/example/ndpiReader.c b/example/ndpiReader.c
index e41cbb21a..a3662c8ab 100644
--- a/example/ndpiReader.c
+++ b/example/ndpiReader.c
@@ -4045,6 +4045,38 @@ void desUnitTest() {
/* *********************************************** */
+void desUnitStressTest() {
+ struct ndpi_des_struct des;
+ u_int8_t trace = 1;
+ u_int num_learning_points = 1;
+ u_int i;
+ float alpha = 0.9, beta = 0.5;
+ double init_value = time(NULL) % 1000;
+
+ assert(ndpi_des_init(&des, alpha, beta, 0.05) == 0);
+
+ if(trace) {
+ printf("\nDouble Exponential Smoothing [alpha: %.1f][beta: %.1f]\n", alpha, beta);
+ }
+
+ for(i=0; i<512; i++) {
+ double prediction, confidence_band;
+ double lower, upper;
+ double value = init_value + rand() % 25;
+ int rc = ndpi_des_add_value(&des, value, &prediction, &confidence_band);
+
+ lower = prediction - confidence_band, upper = prediction + confidence_band;
+
+ if(trace) {
+ printf("%2u)\t%12.3f\t%.3f\t%12.3f\t%12.3f\t %s [%.3f]\n", i, value, prediction, lower, upper,
+ ((rc == 0) || ((value >= lower) && (value <= upper))) ? "OK" : "ANOMALY",
+ confidence_band);
+ }
+ }
+}
+
+/* *********************************************** */
+
void hwUnitTest3() {
struct ndpi_hw_struct hw;
u_int num_learning_points = 3;
@@ -4133,6 +4165,11 @@ int original_main(int argc, char **argv) {
hwUnitTest2();
#endif
+#ifdef STRESS_TEST
+ desUnitStressTest();
+ exit(0);
+#endif
+
sesUnitTest();
desUnitTest();
diff --git a/src/include/ndpi_typedefs.h b/src/include/ndpi_typedefs.h
index aac7b56db..51a2beb61 100644
--- a/src/include/ndpi_typedefs.h
+++ b/src/include/ndpi_typedefs.h
@@ -1646,7 +1646,8 @@ typedef struct {
/* **************************************** */
-#define HW_HISTORY_LEN 4
+#define HW_HISTORY_LEN 4
+#define MAX_SQUARE_ERROR_ITERATIONS 64 /* MUST be < num_values_rollup (256 max) */
struct ndpi_hw_struct {
struct {
@@ -1655,6 +1656,11 @@ struct ndpi_hw_struct {
u_int16_t num_season_periods; /* num of values of a season */
} params;
+ struct {
+ double sum_square_error;
+ u_int8_t num_values_rollup;
+ } prev_error;
+
u_int32_t num_values;
double u, v, sum_square_error;
@@ -1668,6 +1674,11 @@ struct ndpi_ses_struct {
double alpha, ro;
} params;
+ struct {
+ double sum_square_error;
+ u_int8_t num_values_rollup;
+ } prev_error;
+
u_int32_t num_values;
double sum_square_error, last_forecast, last_value;
};
@@ -1677,6 +1688,11 @@ struct ndpi_des_struct {
double alpha, beta, ro;
} params;
+ struct {
+ double sum_square_error;
+ u_int8_t num_values_rollup;
+ } prev_error;
+
u_int32_t num_values;
double sum_square_error, last_forecast, last_trend, last_value;
};
diff --git a/src/lib/ndpi_analyze.c b/src/lib/ndpi_analyze.c
index 3863dc910..191360f9d 100644
--- a/src/lib/ndpi_analyze.c
+++ b/src/lib/ndpi_analyze.c
@@ -1028,8 +1028,9 @@ int ndpi_hw_add_value(struct ndpi_hw_struct *hw, const u_int32_t _value, double
} else {
u_int idx = hw->num_values % hw->params.num_season_periods;
double prev_u, prev_v, prev_s, value = (double)_value;
- double sq, error;
-
+ double sq, error, sq_error;
+ u_int observations;
+
if(hw->num_values == hw->params.num_season_periods) {
double avg = ndpi_avg_inline(hw->y, hw->params.num_season_periods);
u_int i;
@@ -1071,8 +1072,10 @@ int ndpi_hw_add_value(struct ndpi_hw_struct *hw, const u_int32_t _value, double
*forecast = (prev_u + prev_v) * prev_s;
error = value - *forecast;
- hw->sum_square_error += error * error;
- sq = sqrt(hw->sum_square_error / (hw->num_values + 2 - hw->params.num_season_periods));
+ sq_error = error * error;
+ hw->sum_square_error += sq_error, hw->prev_error.sum_square_error += sq_error;;
+ observations = (hw->num_values < MAX_SQUARE_ERROR_ITERATIONS) ? hw->num_values : ((hw->num_values % MAX_SQUARE_ERROR_ITERATIONS) + MAX_SQUARE_ERROR_ITERATIONS);
+ sq = sqrt(hw->sum_square_error / (observations - hw->params.num_season_periods));
*confidence_band = hw->params.ro * sq;
#ifdef HW_DEBUG
@@ -1084,6 +1087,11 @@ int ndpi_hw_add_value(struct ndpi_hw_struct *hw, const u_int32_t _value, double
hw->num_values++, idx = (idx + 1) % hw->params.num_season_periods;
+ if(++hw->prev_error.num_values_rollup == MAX_SQUARE_ERROR_ITERATIONS) {
+ hw->sum_square_error = hw->prev_error.sum_square_error;
+ hw->prev_error.num_values_rollup = 0, hw->prev_error.sum_square_error = 0;
+ }
+
return(1); /* We're in business: forecast is meaningful now */
}
}
@@ -1186,7 +1194,7 @@ int ndpi_ses_init(struct ndpi_ses_struct *ses, double alpha, float significance)
1 Normal processing: forecast and confidence_band are meaningful
*/
int ndpi_ses_add_value(struct ndpi_ses_struct *ses, const u_int32_t _value, double *forecast, double *confidence_band) {
- double value = (double)_value, error;
+ double value = (double)_value, error, sq_error;
int rc;
if(ses->num_values == 0)
@@ -1195,10 +1203,12 @@ int ndpi_ses_add_value(struct ndpi_ses_struct *ses, const u_int32_t _value, doub
*forecast = (ses->params.alpha * (ses->last_value - ses->last_forecast)) + ses->last_forecast;
error = value - *forecast;
- ses->sum_square_error += error * error;
+ sq_error = error * error;
+ ses->sum_square_error += sq_error, ses->prev_error.sum_square_error += sq_error;
if(ses->num_values > 0) {
- double sq = sqrt(ses->sum_square_error / (ses->num_values+1));
+ u_int observations = (ses->num_values < MAX_SQUARE_ERROR_ITERATIONS) ? (ses->num_values + 1) : ((ses->num_values % MAX_SQUARE_ERROR_ITERATIONS) + MAX_SQUARE_ERROR_ITERATIONS + 1);
+ double sq = sqrt(ses->sum_square_error / observations);
*confidence_band = ses->params.ro * sq;
rc = 1;
@@ -1207,6 +1217,11 @@ int ndpi_ses_add_value(struct ndpi_ses_struct *ses, const u_int32_t _value, doub
ses->num_values++, ses->last_value = value, ses->last_forecast = *forecast;
+ if(++ses->prev_error.num_values_rollup == MAX_SQUARE_ERROR_ITERATIONS) {
+ ses->sum_square_error = ses->prev_error.sum_square_error;
+ ses->prev_error.num_values_rollup = 0, ses->prev_error.sum_square_error = 0;
+ }
+
#ifdef SES_DEBUG
printf("[num_values: %u][[error: %.3f][forecast: %.3f][sqe: %.3f][sq: %.3f][confidence_band: %.3f]\n",
ses->num_values, error, *forecast, ses->sum_square_error, sq, *confidence_band);
@@ -1226,7 +1241,7 @@ int ndpi_des_init(struct ndpi_des_struct *des, double alpha, double beta, float
memset(des, 0, sizeof(struct ndpi_des_struct));
des->params.alpha = alpha;
-
+
if((significance < 0) || (significance > 1)) significance = 0.05;
des->params.ro = ndpi_normal_cdf_inverse(1 - (significance / 2.));
@@ -1251,7 +1266,7 @@ int ndpi_des_init(struct ndpi_des_struct *des, double alpha, double beta, float
1 Normal processing: forecast and confidence_band are meaningful
*/
int ndpi_des_add_value(struct ndpi_des_struct *des, const u_int32_t _value, double *forecast, double *confidence_band) {
- double value = (double)_value, error;
+ double value = (double)_value, error, sq_error;
int rc;
if(des->num_values == 0)
@@ -1262,10 +1277,12 @@ int ndpi_des_add_value(struct ndpi_des_struct *des, const u_int32_t _value, doub
}
error = value - *forecast;
- des->sum_square_error += error * error;
+ sq_error = error * error;
+ des->sum_square_error += sq_error, des->prev_error.sum_square_error += sq_error;
if(des->num_values > 0) {
- double sq = sqrt(des->sum_square_error / (des->num_values+1));
+ u_int observations = (des->num_values < MAX_SQUARE_ERROR_ITERATIONS) ? (des->num_values + 1) : ((des->num_values % MAX_SQUARE_ERROR_ITERATIONS) + MAX_SQUARE_ERROR_ITERATIONS + 1);
+ double sq = sqrt(des->sum_square_error / observations);
*confidence_band = des->params.ro * sq;
rc = 1;
@@ -1273,6 +1290,11 @@ int ndpi_des_add_value(struct ndpi_des_struct *des, const u_int32_t _value, doub
*confidence_band = 0, rc = 0;
des->num_values++, des->last_value = value, des->last_forecast = *forecast;
+
+ if(++des->prev_error.num_values_rollup == MAX_SQUARE_ERROR_ITERATIONS) {
+ des->sum_square_error = des->prev_error.sum_square_error;
+ des->prev_error.num_values_rollup = 0, des->prev_error.sum_square_error = 0;
+ }
#ifdef DES_DEBUG
printf("[num_values: %u][[error: %.3f][forecast: %.3f][trend: %.3f[sqe: %.3f][sq: %.3f][confidence_band: %.3f]\n",
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