This class implements the Fast Wavelet Transform. It follows Mac Cody's article in Dr. Dobb's Journal, April 1992. See also...
http://www.dfw.net/~mcody/fwt/fwt.html

Notable features of his implementation include...
1. The ability to generate a large family of wavelets (including the Haar (alpha=beta) and Daubechies) from two parameters, alpha and beta, which range between -pi and pi.
2. All data arrays have 5 elements added on to allow for convolution overrun with filters up to 6 in length (the max for this implementation).
3. After a forward transform, the detail coefficients of the deomposition are found in transform at: 2*i, for i = 1, 2, ... nLevels; and the approximation coefficients are in transform at: (2*nLevels-1). these together comprise the complete wavelet transform.

The following changes from cody's listings should also be noted...
1. The three DotProduct routines have been merged into one.
2. The four routines WaveletDecomposition, DecomposeBranches, WaveletReconstruction, ReconstructBranches have all been merged into transformForward:.
3. All indexing follows the Smalltalk 1-to-N convention, naturally.

	computation
	convolveAndDec:dataLen:filter:out: convolveAndInt:dataLen:filter:sumOutput:into: dotpData:endIndex:filter:start:stop:inc: transformForward:

	initialization
	nSamples:nLevels: setAlpha:beta:

	testing
	doWaveDemo meanSquareError: viewPhiAndPsi

	access	top
	coeffs Return all coefficients neede to reconstruct the original samples
	coeffs: Initialize this instance from the given coeff array (including header).
	samples
	samples:

	computation	top
	convolveAndDec:dataLen:filter:out: convolve the input sequence with the filter and decimate by two
	convolveAndInt:dataLen:filter:sumOutput:into: insert zeros between each element of the input sequence and convolve with the filter to interpolate the data
	dotpData:endIndex:filter:start:stop:inc:
	transformForward:

	initialization	top
	nSamples:nLevels: Initialize a wavelet transform.
	setAlpha:beta: Set alpha and beta, compute wavelet coeefs, and derive hFilter and lFilter

	testing	top
	doWaveDemo FWT new doWaveDemo
	meanSquareError: Return the mean-square error between the current sample array and some other data, presumably to evaluate a compression scheme.
	viewPhiAndPsi (FWT new nSamples: 256 nLevels: 6) viewPhiAndPsi