BurgAlgorithm (FsODE)

FsODE

Documentation for 0.0.2

BurgAlgorithm Type

Namespace: Altaxo.Calc.Regression
Assembly: AltaxoCore.Redist.dll
Base Type: obj

Implements Burg's algorithm with real numbers.

Constructors

Constructor	Description
`BurgAlgorithm()` Full Usage: `BurgAlgorithm()`

Instance members

Instance member	Description
`this.Coefficients` Full Usage: `this.Coefficients` Returns: `IROVector<float>`	Returns the coefficients that were calculated during the last run of the algorithm. Returns: `IROVector<float>`
`this.Execute` Full Usage: `this.Execute` Parameters: x : `IReadOnlyList<float>` - Signal for building the model. numberOfCoefficients : `int` - Number of coefficients of the model.	Uses the signal in vector x to build a model with `numberOfCoefficients` parameter. x : `IReadOnlyList<float>` Signal for building the model. numberOfCoefficients : `int` Number of coefficients of the model.
`this.Execute` Full Usage: `this.Execute` Parameters: x : `IReadOnlyList<float>` - Signal for building the model. coefficients : `IVector<float>` - Vector to be filled with the coefficients of the model.	Uses th signal in vector x to build a model with `numberOfCoefficients` parameter. x : `IReadOnlyList<float>` Signal for building the model. coefficients : `IVector<float>` Vector to be filled with the coefficients of the model.
`this.Execute` Full Usage: `this.Execute` Parameters: x : `IReadOnlyList<float>` - Signal for building the model. coefficients : `IVector<float>` - Vector to be filled with the coefficients of the model. errors : `IVector<float>` - Vector to be filled with the sum of forward and backward prediction error for every stage of the model.	Uses the signal in vector x to build a model with `numberOfCoefficients` parameter. x : `IReadOnlyList<float>` Signal for building the model. coefficients : `IVector<float>` Vector to be filled with the coefficients of the model. errors : `IVector<float>` Vector to be filled with the sum of forward and backward prediction error for every stage of the model.
`this.Execute` Full Usage: `this.Execute` Parameters: x : `IReadOnlyList<float>` - Signal for building the model. coefficients : `IVector<float>` - Vector to be filled with the coefficients of the model. errors : `IVector<float>` - Vector to be filled with the sum of forward and backward prediction error for every stage of the model. reflectionCoefficients : `IVector<float>` - Vector to be filled with the reflection coefficients.	Uses the signal in vector x to build a model with `numberOfCoefficients` parameter. x : `IReadOnlyList<float>` Signal for building the model. coefficients : `IVector<float>` Vector to be filled with the coefficients of the model. errors : `IVector<float>` Vector to be filled with the sum of forward and backward prediction error for every stage of the model. reflectionCoefficients : `IVector<float>` Vector to be filled with the reflection coefficients.
`this.GetFrequencyResponse` Full Usage: `this.GetFrequencyResponse` Parameters: fdt : `float` - Frequency. Must be given as f*dt, meaning the product of frequency and sample interval. Returns: `float` The complex frequency response at the given frequency. Modifiers: abstract	Calculates the frequency response for a given frequency. fdt : `float` Frequency. Must be given as f*dt, meaning the product of frequency and sample interval. Returns: `float` The complex frequency response at the given frequency.
`this.GetMeanPredictionErrorNonrecursivelyBackward` Full Usage: `this.GetMeanPredictionErrorNonrecursivelyBackward` Parameters: x : `IReadOnlyList<float>` - Signal for which to determine the mean backward prediction error. Returns: `float` Mean backward prediction error.	This algorithm determines the mean backward prediction error using the model stored in this instance. See remarks for details. 1. The prediction is done non recursively, i.e. part of the signal (the signal window) is used to predict the signal value before, and this predicted signal value is then compared with the original signal value stored in x to build the sum of errors. But the predicted signal value is not used to make further predictions. Instead, the signal window is moved by one point to the left and another prediction is made, with the original signal in x. This is repeated until the first point (index 0) is predicted. The return value is the square root of the sum of squared differences between predicted signal values and original values, divided by the number of predicted values. The number of predicted values is the length of the signal x minus the number of coefficents of the model. x : `IReadOnlyList<float>` Signal for which to determine the mean backward prediction error. Returns: `float` Mean backward prediction error.
`this.GetMeanPredictionErrorNonrecursivelyForward` Full Usage: `this.GetMeanPredictionErrorNonrecursivelyForward` Parameters: x : `IReadOnlyList<float>` - Signal for which to determine the mean forward prediction error. Returns: `float` Mean backward prediction error.	This algorithm determines the mean forward prediction error using the model stored in this instance. See remarks for details. 1. The prediction is done non recursively, i.e. part of the signal (the signal window) is used to predict the signal value immediately after the window, and this predicted signal value is then compared with the original signal value stored in x to build the sum of errors. But the predicted signal value is not used to make further predictions. Instead, the signal window is moved by one point to the right and another prediction is made, with the original signal in x. This is repeated until the last point is predicted. The return value is the square root of the sum of squared differences between predicted signal values and original values, divided by the number of predicted values. The number of predicted values is the length of the signal x minus the number of coefficents of the model. x : `IReadOnlyList<float>` Signal for which to determine the mean forward prediction error. Returns: `float` Mean backward prediction error.
`this.MeanSquareError` Full Usage: `this.MeanSquareError` Returns: `float`	Mean square error calculated during the last run of the algorithm. Returns: `float`
`this.NumberOfCoefficients` Full Usage: `this.NumberOfCoefficients` Returns: `int`	Returns the number of coefficients that were used for the last run of the algorithm. Returns: `int`
`this.PredictRecursivelyBackward` Full Usage: `this.PredictRecursivelyBackward` Parameters: x : `IVector<float>` - Signal which holds at least BurgAlgorithm.NumberOfCoefficients valid points (the signal window to start the prediction with) from index (lastPoint+1) to (lastPoint+NumberOfCoefficents). The predicted values are then stored in the first part of this vector from indices (0) to (lastPoint). lastPoint : `int` - Index of the last point to predict.	Predict values towards the start of the vector. The predicted values are then used to predict more values. See remarks for details. The algorithm uses a signal window of `NumberOfCoefficients` signal points after the `lastPoint` to predict the value at `lastPoint`. Then the window is shifted by one towards the start of the vecctor, hence including the predicted value, and the point at `lastPoint-1` is predicted. The procedure is repeated until the value at index 0 is predicted. x : `IVector<float>` Signal which holds at least BurgAlgorithm.NumberOfCoefficients valid points (the signal window to start the prediction with) from index (lastPoint+1) to (lastPoint+NumberOfCoefficents). The predicted values are then stored in the first part of this vector from indices (0) to (lastPoint). lastPoint : `int` Index of the last point to predict.
`this.PredictRecursivelyBackward` Full Usage: `this.PredictRecursivelyBackward` Parameters: x : `IVector<float>` - Signal which holds at least BurgAlgorithm.NumberOfCoefficients valid points (the signal window to start the prediction with) from index (lastPoint+1) to (lastPoint+NumberOfCoefficents). The predicted values are then stored in the first part of this vector from indices (lastPoint-count+1) to (lastPoint). lastPoint : `int` - Index of the last point to predict. count : `int` - Number of points to predict.	Predict values towards the start of the vector. The predicted values are then used to predict more values. See remarks for details. The algorithm uses a signal window of `NumberOfCoefficients` signal points after the `lastPoint` to predict the value at `lastPoint`. Then the window is shifted by one towards the start of the vecctor, hence including the predicted value, and the point at `lastPoint-1` is predicted. The procedure is repeated until `count` points are predicted. x : `IVector<float>` Signal which holds at least BurgAlgorithm.NumberOfCoefficients valid points (the signal window to start the prediction with) from index (lastPoint+1) to (lastPoint+NumberOfCoefficents). The predicted values are then stored in the first part of this vector from indices (lastPoint-count+1) to (lastPoint). lastPoint : `int` Index of the last point to predict. count : `int` Number of points to predict.
`this.PredictRecursivelyForward` Full Usage: `this.PredictRecursivelyForward` Parameters: x : `IVector<float>` - Signal which holds at least BurgAlgorithm.NumberOfCoefficients valid points (the signal window to start the prediction with) from index (firstPoint-NumberOfCoefficents) to (firstPoint-1). The predicted values are then stored in this vector. firstPoint : `int` - Index of the first point to predict.	Predict values towards the end of the vector. The predicted values are then used to predict more values. See remarks for details. The algorithm uses a signal window of `NumberOfCoefficients` signal points before the `firstPoint` to predict the value at `firstPoint`. Then the window is shifted by one towards the end of the vecctor, hence including the predicted value, and the point at `firstPoint+1` is predicted. The procedure is repeated until all points to the end of the vector are predicted. x : `IVector<float>` Signal which holds at least BurgAlgorithm.NumberOfCoefficients valid points (the signal window to start the prediction with) from index (firstPoint-NumberOfCoefficents) to (firstPoint-1). The predicted values are then stored in this vector. firstPoint : `int` Index of the first point to predict.
`this.PredictRecursivelyForward` Full Usage: `this.PredictRecursivelyForward` Parameters: x : `IVector<float>` - Signal which holds at least BurgAlgorithm.NumberOfCoefficients valid points (the signal window to start the prediction with) from index (firstPoint-NumberOfCoefficents) to (firstPoint-1). The predicted values are then stored in this vector. firstPoint : `int` - Index of the first point to predict. count : `int` - Number of points to predict.	Predict values towards the end of the vector. The predicted values are then used to predict more values. See remarks for details. The algorithm uses a signal window of `NumberOfCoefficients` signal points before the `firstPoint` to predict the value at `firstPoint`. Then the window is shifted by one towards the end of the vecctor, hence including the predicted value, and the point at `firstPoint+1` is predicted. The procedure is repeated until `count` points are predicted. x : `IVector<float>` Signal which holds at least BurgAlgorithm.NumberOfCoefficients valid points (the signal window to start the prediction with) from index (firstPoint-NumberOfCoefficents) to (firstPoint-1). The predicted values are then stored in this vector. firstPoint : `int` Index of the first point to predict. count : `int` Number of points to predict.

Static members

Static member	Description
`BurgAlgorithm.Execution(x, coefficients)` Full Usage: `BurgAlgorithm.Execution(x, coefficients)` Parameters: x : `IReadOnlyList<float>` - Signal for building the model. coefficients : `IVector<float>` - Vector to be filled with the coefficients of the model. Returns: `float` The mean square error of backward and forward prediction.	Uses the signal in vector x to build a model with `numberOfCoefficients` parameter. x : `IReadOnlyList<float>` Signal for building the model. coefficients : `IVector<float>` Vector to be filled with the coefficients of the model. Returns: `float` The mean square error of backward and forward prediction.
`BurgAlgorithm.Execution(x, coefficients, errors)` Full Usage: `BurgAlgorithm.Execution(x, coefficients, errors)` Parameters: x : `IReadOnlyList<float>` - Signal for building the model. coefficients : `IVector<float>` - Vector to be filled with the coefficients of the model. errors : `IVector<float>` - Vector to be filled with the sum of forward and backward prediction error for every stage of the model. Returns: `float` The mean square error of backward and forward prediction.	Uses the signal in vector x to build a model with `numberOfCoefficients` parameter. x : `IReadOnlyList<float>` Signal for building the model. coefficients : `IVector<float>` Vector to be filled with the coefficients of the model. errors : `IVector<float>` Vector to be filled with the sum of forward and backward prediction error for every stage of the model. Returns: `float` The mean square error of backward and forward prediction.
`BurgAlgorithm.Execution(x, coefficients, errors, reflectionCoefficients)` Full Usage: `BurgAlgorithm.Execution(x, coefficients, errors, reflectionCoefficients)` Parameters: x : `IReadOnlyList<float>` - Signal for building the model. coefficients : `IVector<float>` - Vector to be filled with the coefficients of the model. errors : `IVector<float>` - Vector to be filled with the sum of forward and backward prediction error for every stage of the model. reflectionCoefficients : `IVector<float>` - Vector to be filled with the reflection coefficients. Returns: `float` The mean square error of backward and forward prediction.	Uses the signal in vector x to build a model with `numberOfCoefficients` parameter. x : `IReadOnlyList<float>` Signal for building the model. coefficients : `IVector<float>` Vector to be filled with the coefficients of the model. errors : `IVector<float>` Vector to be filled with the sum of forward and backward prediction error for every stage of the model. reflectionCoefficients : `IVector<float>` Vector to be filled with the reflection coefficients. Returns: `float` The mean square error of backward and forward prediction.