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