Class AlgorithmLP

java.lang.Object
  Algorithm
      AlgorithmLP

All Implemented Interfaces:

java.lang.Runnable

public class AlgorithmLP
extends Algorithm

Constructor Summary

AlgorithmLP()

Method Summary

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.
void	autocorrelate(java.util.Vector v, double[] autoCoeff_co) Actaully computes the autocorrelation coefficients
void	autoCorrelation() Computes the autocorrelation coeffient from the data sets
double	calculate_lpc(double[] auto_coeff, double[] lpc, double[] rc_reg) Actually calculate the LP coefficient and the Residual Error Energy, and Reflection Coefficients
boolean	checkdata_LP(java.util.Vector lp) Validates the class entered by user for Linear Prediction
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
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.
void	final_estimate() Calculates the estimated points for the data inputs
boolean	initialize() Implements the initialize() method in the base class.
void	interpol(java.util.Vector<MyPoint> v, java.util.Vector<MyPoint> iset) Calculates the interpolated points for the data inputs
void	lpcCoefficient() Computes the Linear Prediction coefficient from the data sets
double	mean(java.util.Vector<MyPoint> v, java.util.Vector<MyPoint> mv) Calculates the mean and the zero-mean data points
void	run() Implementation of the run function from the Runnable interface.
void	spline(double[] x, double[] y, double[] y2, int size) Actually interpolates the points
void	splint(MyPoint u1, MyPoint u2, MyPoint r, double[] y2, int i) Interpolates for a point between the two known points using Cubic Interpolation
void	step2_display() Displays LP order, Error Energy and Reflection Coefficients

Methods inherited from class Algorithm

computeMeans, disableControl, enableControl, nextStep, prevStep, scaleToFitData, setDataPoints, setInputPanel, setOutputPanel, setProcessBox

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

AlgorithmLP

public AlgorithmLP()

Method Detail

initialize

public boolean initialize()

Implements the initialize() method in the base class. Initializes member data and prepares for execution of first step. This method "resets" the algorithm.

Specified by:

initialize in class Algorithm

Returns:

returns true if sets of data are valid

checkdata_LP

public boolean checkdata_LP(java.util.Vector lp)

Validates the class entered by user for Linear Prediction

Parameters:

lp -

Returns:

true if data is Vector lp is valid. It is invalid if the size of lp is 1 or any element is larger than the the previous element

run

public void run()

Implementation of the run function from the Runnable interface. Determines what the current step is and calls the appropriate method.

Specified by:

run in interface java.lang.Runnable

Specified by:

run in class Algorithm

interpol

public void interpol(java.util.Vector<MyPoint> v,
                     java.util.Vector<MyPoint> iset)

Calculates the interpolated points for the data inputs

Parameters:

v - input data points

iset - interpolated data points

spline

public void spline(double[] x,
                   double[] y,
                   double[] y2,
                   int size)

Actually interpolates the points

Parameters:

x - array containing the x coordinates of datapoints

y - array containing the y coordinates of datapoints

y2 - array containing the interpolated y coordinates

size - the size of the array to be interpolated

splint

public void splint(MyPoint u1,
                   MyPoint u2,
                   MyPoint r,
                   double[] y2,
                   int i)

Interpolates for a point between the two known points using Cubic Interpolation

Parameters:

u1 - start point for the interpolation

u2 - end point for the interpolation

r - returning point, basically the interpolated point

y2 - array used for reassigning of r

i - the sample number

mean

public double mean(java.util.Vector<MyPoint> v,
                   java.util.Vector<MyPoint> mv)

Calculates the mean and the zero-mean data points

Parameters:

v - orginal datapoints

mv - zero mean datapoints

Returns:

The average in a double

autoCorrelation

public void autoCorrelation()

Computes the autocorrelation coeffient from the data sets

autocorrelate

public void autocorrelate(java.util.Vector v,
                          double[] autoCoeff_co)

Actaully computes the autocorrelation coefficients

Parameters:

v - Vector of datapoints

autoCoeff_co - array of autocorrelation coefficients

lpcCoefficient

public void lpcCoefficient()

Computes the Linear Prediction coefficient from the data sets

calculate_lpc

public double calculate_lpc(double[] auto_coeff,
                            double[] lpc,
                            double[] rc_reg)

Actually calculate the LP coefficient and the Residual Error Energy, and Reflection Coefficients

Parameters:

auto_coeff - array of auto correlation coefficients

lpc - array of linear prediction coefficients

rc_reg - array of reflection coefficients

Returns:

residual error energy in a double

final_estimate

public void final_estimate()

Calculates the estimated points for the data inputs

estimate

public 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.

Parameters:

iset - interpolated data points

y_estimate - predicted final signal data points

avg - mean of the original datapoints given

final_lpc - array of final linear prediction coefficients

actual_error

public 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.

Parameters:

y_estimate - datapoints of the estimated datapoints

iset - original datapoints

Returns:

actual error energy in a double

step2_display

public void step2_display()

Displays LP order, Error Energy and Reflection Coefficients

display_result

public 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

Parameters:

auto_coeff - Auto Correlation Coefficients

refCoef - Refelction Coefficient

final_lpc - Linear Prediction Coefficients

est_err - Estimated Error

act_err - Actual Error

length - Length of the data points