public class AlgorithmLP extends Algorithm
Constructor and Description |
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AlgorithmLP() |
Modifier and Type | Method and Description |
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double |
actual_error(java.util.Vector y_estimate,
java.util.Vector iset)
Compute the actual error from the given data points and the estimated
values.
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void |
autocorrelate(java.util.Vector v,
double[] autoCoeff_co)
Actaully computes the autocorrelation coefficients
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void |
autoCorrelation()
Computes the autocorrelation coeffient from the data sets
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double |
calculate_lpc(double[] auto_coeff,
double[] lpc,
double[] rc_reg)
Actually calculate the LP coefficient and the Residual Error
Energy, and Reflection Coefficients
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boolean |
checkdata_LP(java.util.Vector lp)
Validates the class entered by user for Linear Prediction
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void |
display_result(double[] auto_coeff,
double[] refCoef,
double[] final_lpc,
double est_err,
double act_err,
int index,
int length)
Display the results in the process box
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void |
estimate(java.util.Vector<MyPoint> iset,
java.util.Vector<MyPoint> y_estimate,
double avg,
double[] final_lpc)
Estimates the amplitude based on the LP coeficients.
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void |
final_estimate()
Calculates the estimated points for the data inputs
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boolean |
initialize()
Implements the initialize() method in the base class.
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void |
interpol(java.util.Vector<MyPoint> v,
java.util.Vector<MyPoint> iset)
Calculates the interpolated points for the data inputs
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void |
lpcCoefficient()
Computes the Linear Prediction coefficient from the data sets
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double |
mean(java.util.Vector<MyPoint> v,
java.util.Vector<MyPoint> mv)
Calculates the mean and the zero-mean data points
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void |
run()
Implementation of the run function from the Runnable interface.
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void |
spline(double[] x,
double[] y,
double[] y2,
int size)
Actually interpolates the points
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void |
splint(MyPoint u1,
MyPoint u2,
MyPoint r,
double[] y2,
int i)
Interpolates for a point between the two known points
using Cubic Interpolation
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void |
step2_display()
Displays LP order, Error Energy and Reflection Coefficients
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computeMeans, disableControl, enableControl, nextStep, prevStep, scaleToFitData, setDataPoints, setInputPanel, setOutputPanel, setProcessBox
public boolean initialize()
initialize
in class Algorithm
public boolean checkdata_LP(java.util.Vector lp)
lp
- public void run()
public void interpol(java.util.Vector<MyPoint> v, java.util.Vector<MyPoint> iset)
v
- input data pointsiset
- interpolated data pointspublic void spline(double[] x, double[] y, double[] y2, int size)
x
- array containing the x coordinates of datapointsy
- array containing the y coordinates of datapointsy2
- array containing the interpolated y coordinatessize
- the size of the array to be interpolatedpublic void splint(MyPoint u1, MyPoint u2, MyPoint r, double[] y2, int i)
u1
- start point for the interpolationu2
- end point for the interpolationr
- returning point, basically the interpolated pointy2
- array used for reassigning of ri
- the sample numberpublic double mean(java.util.Vector<MyPoint> v, java.util.Vector<MyPoint> mv)
v
- orginal datapointsmv
- zero mean datapointspublic void autoCorrelation()
public void autocorrelate(java.util.Vector v, double[] autoCoeff_co)
v
- Vector of datapointsautoCoeff_co
- array of autocorrelation coefficientspublic void lpcCoefficient()
public double calculate_lpc(double[] auto_coeff, double[] lpc, double[] rc_reg)
auto_coeff
- array of auto correlation coefficientslpc
- array of linear prediction coefficientsrc_reg
- array of reflection coefficientspublic void final_estimate()
public void estimate(java.util.Vector<MyPoint> iset, java.util.Vector<MyPoint> y_estimate, double avg, double[] final_lpc)
iset
- interpolated data pointsy_estimate
- predicted final signal data pointsavg
- mean of the original datapoints givenfinal_lpc
- array of final linear prediction coefficientspublic double actual_error(java.util.Vector y_estimate, java.util.Vector iset)
y_estimate
- datapoints of the estimated datapointsiset
- original datapointspublic void step2_display()
public void display_result(double[] auto_coeff, double[] refCoef, double[] final_lpc, double est_err, double act_err, int index, int length)
auto_coeff
- Auto Correlation CoefficientsrefCoef
- Refelction Coefficientfinal_lpc
- Linear Prediction Coefficientsest_err
- Estimated Erroract_err
- Actual Errorlength
- Length of the data points