localhorst/smart-oil-heating-control-system
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bugfix/linear-regression-prediction #19

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localhorst merged 5 commits from bugfix/linear-regression-prediction into main 2024-12-26 22:47:35 +01:00
Conversation 0 Commits 5 Files Changed 1 +10 -6
1 changed files with 10 additions and 6 deletions
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16
main/inputs.c
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@ -36,7 +36,7 @@ void taskInput(void *pvParameters);
void initMeasurement(sMeasurement *pMeasurement); void initMeasurement(sMeasurement *pMeasurement);
void updateAverage(sMeasurement *pMeasurement); void updateAverage(sMeasurement *pMeasurement);
void updatePrediction(sMeasurement *pMeasurement); void updatePrediction(sMeasurement *pMeasurement);
float linearRegressionPredict(const float *samples, size_t count, float futureIndex); float linearRegressionPredict(const float *samples, size_t count, size_t bufferIndex, float futureIndex);
void initInputs(void) void initInputs(void)
{ {
@ -162,6 +162,7 @@ void updatePrediction(sMeasurement *pMeasurement)
predict60s->fValue = linearRegressionPredict( predict60s->fValue = linearRegressionPredict(
predict60s->samples, predict60s->samples,
predict60s->bufferCount, predict60s->bufferCount,
predict60s->bufferIndex,
predict60s->bufferCount + 60.0f); predict60s->bufferCount + 60.0f);
} }
@ -267,7 +268,7 @@ void taskInput(void *pvParameters)
} }
} }
float linearRegressionPredict(const float *samples, size_t count, float futureIndex) float linearRegressionPredict(const float *samples, size_t count, size_t bufferIndex, float futureIndex)
{ {
if (count == 0) if (count == 0)
return 0.0f; // No prediction possible with no data return 0.0f; // No prediction possible with no data
@ -276,8 +277,11 @@ float linearRegressionPredict(const float *samples, size_t count, float futureIn
for (size_t i = 0; i < count; i++) for (size_t i = 0; i < count; i++)
{ {
float x = (float)i; // Time index // Calculate the circular buffer index for the current sample
float y = samples[i]; // Sample value size_t circularIndex = (bufferIndex + i + 1) % count;
float x = (float)i; // Time index
float y = samples[circularIndex]; // Sample value
sumX += x; sumX += x;
sumY += y; sumY += y;
@ -287,8 +291,8 @@ float linearRegressionPredict(const float *samples, size_t count, float futureIn
// Calculate slope (m) and intercept (b) of the line: y = mx + b // Calculate slope (m) and intercept (b) of the line: y = mx + b
float denominator = (count * sumX2 - sumX * sumX); float denominator = (count * sumX2 - sumX * sumX);
if (fabs(denominator) < 1e-6) // Avoid division by zero if (fabs(denominator) < 1e-6) // Avoid division by zero
return samples[count - 1]; // Return last value as prediction return samples[bufferIndex]; // Return the latest value as prediction
float m = (count * sumXY - sumX * sumY) / denominator; float m = (count * sumXY - sumX * sumY) / denominator;
float b = (sumY - m * sumX) / count; float b = (sumY - m * sumX) / count;