honeymoose
/
OpenSearch
mirror of https://github.com/honeymoose/OpenSearch.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

[TESTS] Fix bad math in moving_avg unit tests

This commit is contained in:
Zachary Tong 2015-07-08 18:04:36 -04:00
parent d5f73ec8ac
commit 531b894b5d
1 changed files with 39 additions and 42 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

81
core/src/test/java/org/elasticsearch/search/aggregations/pipeline/moving/avg/MovAvgUnitTests.java
View File

@ -351,12 +351,11 @@ public class MovAvgUnitTests extends ElasticsearchTestCase {
// Calculate the slopes between first and second season for each period
for (int i = 0; i < period; i++) {
s += vs[i];
b += (vs[i] - vs[i + period]) / 2;
b += (vs[i + period] - vs[i]) / period;
}
s /= (double) period;
b /= (double) period;
last_s = s;
last_b = b;
// Calculate first seasonal
if (Double.compare(s, 0.0) == 0 || Double.compare(s, -0.0) == 0) {
@ -371,14 +370,13 @@ public class MovAvgUnitTests extends ElasticsearchTestCase {
s = alpha * (vs[i] / seasonal[i - period]) + (1.0d - alpha) * (last_s + last_b);
b = beta * (s - last_s) + (1 - beta) * last_b;
//seasonal[i] = gamma * (vs[i] / s) + ((1 - gamma) * seasonal[i - period]);
seasonal[i] = gamma * (vs[i] / (last_s + last_b )) + (1 - gamma) * seasonal[i - period];
last_s = s;
last_b = b;
}
int seasonCounter = (windowSize - 1) - period;
double expected = s + (0 * b) * seasonal[seasonCounter % windowSize];;
int idx = window.size() - period + (0 % period);
double expected = (s + (1 * b)) * seasonal[idx];
double actual = model.next(window);
assertThat(Double.compare(expected, actual), equalTo(0));
}
@ -424,35 +422,35 @@ public class MovAvgUnitTests extends ElasticsearchTestCase {
// Calculate the slopes between first and second season for each period
for (int i = 0; i < period; i++) {
s += vs[i];
b += (vs[i] - vs[i + period]) / 2;
b += (vs[i + period] - vs[i]) / period;
}
s /= (double) period;
b /= (double) period;
last_s = s;
last_b = b;
for (int i = 0; i < period; i++) {
// Calculate first seasonal
seasonal[i] = vs[i] / s;
// Calculate first seasonal
if (Double.compare(s, 0.0) == 0 || Double.compare(s, -0.0) == 0) {
Arrays.fill(seasonal, 0.0);
} else {
for (int i = 0; i < period; i++) {
seasonal[i] = vs[i] / s;
}
}
for (int i = period; i < vs.length; i++) {
s = alpha * (vs[i] / seasonal[i - period]) + (1.0d - alpha) * (last_s + last_b);
b = beta * (s - last_s) + (1 - beta) * last_b;
//seasonal[i] = gamma * (vs[i] / s) + ((1 - gamma) * seasonal[i - period]);
seasonal[i] = gamma * (vs[i] / (last_s + last_b )) + (1 - gamma) * seasonal[i - period];
last_s = s;
last_b = b;
}
int seasonCounter = (windowSize - 1) - period;
for (int i = 0; i < numPredictions; i++) {
expected[i] = s + (i * b) * seasonal[seasonCounter % windowSize];
assertThat(Double.compare(expected[i], actual[i]), equalTo(0));
seasonCounter += 1;
for (int i = 1; i <= numPredictions; i++) {
int idx = window.size() - period + ((i - 1) % period);
expected[i-1] = (s + (i * b)) * seasonal[idx];
assertThat(Double.compare(expected[i-1], actual[i-1]), equalTo(0));
}
}
@ -490,35 +488,36 @@ public class MovAvgUnitTests extends ElasticsearchTestCase {
counter += 1;
}
// Initial level value is average of first season
// Calculate the slopes between first and second season for each period
for (int i = 0; i < period; i++) {
s += vs[i];
b += (vs[i] - vs[i + period]) / 2;
b += (vs[i + period] - vs[i]) / period;
}
s /= (double) period;
b /= (double) period;
last_s = s;
last_b = b;
for (int i = 0; i < period; i++) {
// Calculate first seasonal
seasonal[i] = vs[i] / s;
// Calculate first seasonal
if (Double.compare(s, 0.0) == 0 || Double.compare(s, -0.0) == 0) {
Arrays.fill(seasonal, 0.0);
} else {
for (int i = 0; i < period; i++) {
seasonal[i] = vs[i] / s;
}
}
for (int i = period; i < vs.length; i++) {
s = alpha * (vs[i] - seasonal[i - period]) + (1.0d - alpha) * (last_s + last_b);
b = beta * (s - last_s) + (1 - beta) * last_b;
//seasonal[i] = gamma * (vs[i] / s) + ((1 - gamma) * seasonal[i - period]);
seasonal[i] = gamma * (vs[i] - (last_s + last_b )) + (1 - gamma) * seasonal[i - period];
seasonal[i] = gamma * (vs[i] - (last_s - last_b )) + (1 - gamma) * seasonal[i - period];
last_s = s;
last_b = b;
}
int seasonCounter = (windowSize - 1) - period;
double expected = s + (0 * b) + seasonal[seasonCounter % windowSize];
int idx = window.size() - period + (0 % period);
double expected = s + (1 * b) + seasonal[idx];
double actual = model.next(window);
assertThat(Double.compare(expected, actual), equalTo(0));
}
@ -559,40 +558,38 @@ public class MovAvgUnitTests extends ElasticsearchTestCase {
counter += 1;
}
// Initial level value is average of first season
// Calculate the slopes between first and second season for each period
for (int i = 0; i < period; i++) {
s += vs[i];
b += (vs[i] - vs[i + period]) / 2;
b += (vs[i + period] - vs[i]) / period;
}
s /= (double) period;
b /= (double) period;
last_s = s;
last_b = b;
for (int i = 0; i < period; i++) {
// Calculate first seasonal
seasonal[i] = vs[i] / s;
// Calculate first seasonal
if (Double.compare(s, 0.0) == 0 || Double.compare(s, -0.0) == 0) {
Arrays.fill(seasonal, 0.0);
} else {
for (int i = 0; i < period; i++) {
seasonal[i] = vs[i] / s;
}
}
for (int i = period; i < vs.length; i++) {
s = alpha * (vs[i] - seasonal[i - period]) + (1.0d - alpha) * (last_s + last_b);
b = beta * (s - last_s) + (1 - beta) * last_b;
//seasonal[i] = gamma * (vs[i] / s) + ((1 - gamma) * seasonal[i - period]);
seasonal[i] = gamma * (vs[i] - (last_s + last_b )) + (1 - gamma) * seasonal[i - period];
seasonal[i] = gamma * (vs[i] - (last_s - last_b )) + (1 - gamma) * seasonal[i - period];
last_s = s;
last_b = b;
}
int seasonCounter = (windowSize - 1) - period;
for (int i = 0; i < numPredictions; i++) {
expected[i] = s + (i * b) + seasonal[seasonCounter % windowSize];
assertThat(Double.compare(expected[i], actual[i]), equalTo(0));
seasonCounter += 1;
for (int i = 1; i <= numPredictions; i++) {
int idx = window.size() - period + ((i - 1) % period);
expected[i-1] = s + (i * b) + seasonal[idx];
assertThat(Double.compare(expected[i-1], actual[i-1]), equalTo(0));
}
}