main/inputs.c

@@ -36,7 +36,7 @@ void taskInput(void *pvParameters);
 void initMeasurement(sMeasurement *pMeasurement);
 void updateAverage(sMeasurement *pMeasurement);
 void updatePrediction(sMeasurement *pMeasurement);
-float linearRegressionPredict(const float *samples, size_t count, size_t bufferIndex, float futureIndex);
+float linearRegressionPredict(const float *samples, size_t count, float futureIndex);
 
 void initInputs(void)
 {
@@ -162,7 +162,6 @@ void updatePrediction(sMeasurement *pMeasurement)
     predict60s->fValue = linearRegressionPredict(
         predict60s->samples,
         predict60s->bufferCount,
-        predict60s->bufferIndex,
         predict60s->bufferCount + 60.0f);
 }
 
@@ -268,7 +267,7 @@ void taskInput(void *pvParameters)
     }
 }
 
-float linearRegressionPredict(const float *samples, size_t count, size_t bufferIndex, float futureIndex)
+float linearRegressionPredict(const float *samples, size_t count, float futureIndex)
 {
     if (count == 0)
         return 0.0f; // No prediction possible with no data
@@ -277,11 +276,8 @@ float linearRegressionPredict(const float *samples, size_t count, size_t bufferI
 
     for (size_t i = 0; i < count; i++)
     {
-        // Calculate the circular buffer index for the current sample
-        size_t circularIndex = (bufferIndex + i + 1) % count;
-
         float x = (float)i;   // Time index
-        float y = samples[circularIndex]; // Sample value
+        float y = samples[i]; // Sample value
 
         sumX += x;
         sumY += y;
@@ -292,7 +288,7 @@ float linearRegressionPredict(const float *samples, size_t count, size_t bufferI
     // Calculate slope (m) and intercept (b) of the line: y = mx + b
     float denominator = (count * sumX2 - sumX * sumX);
     if (fabs(denominator) < 1e-6)  // Avoid division by zero
-        return samples[bufferIndex]; // Return the latest value as prediction
+        return samples[count - 1]; // Return last value as prediction
 
     float m = (count * sumXY - sumX * sumY) / denominator;
     float b = (sumY - m * sumX) / count;