A B C D E F G H I L M N O P Q R S T V W X Y Z

A

alloc_array(int) - Method in class sortTest
 
alloc_bin() - Method in class experimental.histo
 
alloc_bins(int, double, double) - Method in class wavelet_util.noise_filter
Allocate an array of histogram bins that is num_bins in length.
alloc_points(double[], int, int, noise_filter.bell_info) - Method in class wavelet_util.noise_filter
Allocate and initialize an array of point objects.
alloc_sort_testElem_index() - Method in class sortTest
 
alloc_sort_testElem_val() - Method in class sortTest
 
append(double) - Method in class double_vec.double_vec
Append an item to the end of the array
array - Variable in class double_vec.double_vec
 
array_size - Variable in class double_vec.double_vec
 

B

bell_curves - class wavelet_util.bell_curves.
class bell_curves
bell_curves.bell_info - class wavelet_util.bell_curves.bell_info.
Bell curve info: mean, sigma (the standard deviation)
bell_curves.bell_info(bell_curves) - Constructor for class wavelet_util.bell_curves.bell_info
 
bell_curves.bell_info(bell_curves, double, double) - Constructor for class wavelet_util.bell_curves.bell_info
 
bell_curves.bin - class wavelet_util.bell_curves.bin.
A histogram "bin"
bell_curves.bin(bell_curves) - Constructor for class wavelet_util.bell_curves.bin
 
bell_curves.low_high - class wavelet_util.bell_curves.low_high.
Encapsulate the low and high values of a number range
bell_curves.low_high(bell_curves) - Constructor for class wavelet_util.bell_curves.low_high
 
bell_curves.low_high(bell_curves, double, double) - Constructor for class wavelet_util.bell_curves.low_high
 
bell_curves() - Constructor for class wavelet_util.bell_curves
 
binary - class wavelet_util.binary.
Calculate power and log functions using fast binary operations.
binary() - Constructor for class wavelet_util.binary
 

C

calc_histo(noise_filter.point[], int) - Method in class wavelet_util.noise_filter
Calculate the histogram of the coefficients using num_bins histogram bins
calcHisto(double[], histo.mean_median) - Method in class experimental.histo
Generate a histogram array from the input vector.
class_name() - Method in class curve_plot_test.plot_log
 
class_name() - Method in class wavelet_util.plot
 
class_name() - Method in class wavelet_util.gnuplot3D
 
class_name() - Method in class wavelet_util.coef_spectrum
 
class_name() - Method in class wavelet_util.bell_curves
 
class_name() - Method in class wavelet_util.noise_filter
 
coef - Variable in class wavelets.simple_haar
 
coef_spectrum - class wavelet_util.coef_spectrum.
After the wavelet transform is calculated, regenerate the time series with a given spectrum set to zero, or with all but a given spectrum set to zero.
coef_spectrum() - Constructor for class wavelet_util.coef_spectrum
 
compare(Object, Object) - Method in class sortTest.sort_testElem_index
if (a.index == b.index) return 0 if (a.index < b.index) return -1 if (a.index > b.index) return 1;
compare(Object, Object) - Method in class sortTest.sort_testElem_val
if (a.val == b.val) return 0 if (a.val < b.val) return -1 if (a.val > b.val) return 1;
compare(Object, Object) - Method in class sort.generic_sort
This is an abstract function that should be defined by the class derived from generic_sort.
compare(Object, Object) - Method in class wavelet_util.noise_filter.sort_by_index
if (a.index == b.index) return 0 if (a.index < b.index) return -1 if (a.index > b.index) return 1;
compare(Object, Object) - Method in class wavelet_util.noise_filter.sort_by_val
if (a.val == b.val) return 0 if (a.val < b.val) return -1 if (a.val > b.val) return 1;
copy_coef(double[], int) - Method in class wavelet_util.coef_spectrum
Make a copy of the coefficient array.
curve_plot_test - class curve_plot_test.
Generate histograms for the Haar coefficients created by applying the Haar transform to the time series for the Applied Materials (symbol: AMAT) daily close price.
curve_plot_test.plot_log - class curve_plot_test.plot_log.
Write out the log of the close price.
curve_plot_test.plot_log(curve_plot_test, double[]) - Constructor for class curve_plot_test.plot_log
 
curve_plot_test() - Constructor for class curve_plot_test
 

D

data - Variable in class wavelets.simple_haar
 
dataInput - package dataInput
 
double_vec - package double_vec
 
double_vec - class double_vec.double_vec.
class double_vec
double_vec() - Constructor for class double_vec.double_vec
Allocate an array whose initial size is StartArraySize

E

elementAt(int) - Method in class double_vec.double_vec
Return the array element at index ix.
expand(int) - Method in class double_vec.double_vec
Expand the number of array data slots by "amount" elements.
experimental - package experimental
 

F

fileOpen - Variable in class dataInput.tsRead
 
filter_one_spectrum(double[]) - Method in class wavelet_util.coef_spectrum
Moving from the high frequency coefficient spectrum to the lower frequency spectrum, set each spectrum to zero and output the regenerated time series to a file.
filter_spectrum(double[], int, int, double[]) - Method in class wavelet_util.noise_filter
This function is passed the section of the Haar coefficients that correspond to a single spectrum.
filter_test - class filter_test.
Apply the gaussian filter to a time series (in this case the time series for Applied Materials (symbol: AMAT).
filter_test() - Constructor for class filter_test
 
filter_time_series(String, double[]) - Method in class wavelet_util.noise_filter
Calculate the Haar tranform on the time series (whose length must be a factor of two) and filter it.

G

gaussian_filter(double[], double[]) - Method in class wavelet_util.noise_filter
This function is passed a set of Haar wavelet coefficients that result from the Haar wavelet transform.
generic_sort - class sort.generic_sort.
A generic sort class for arrays whose elements are derived from Object.
generic_sort() - Constructor for class sort.generic_sort
 
getArray() - Method in class dataInput.tsRead
getArray
getData() - Method in class double_vec.double_vec
Return a reference to the double_vec array.
getSize() - Method in class dataInput.tsRead
getSize Return the size of the double_vec.
gnuplot3D - class wavelet_util.gnuplot3D.
Define the class gnuplot3D for the wavelet_util package.
gnuplot3D(double[], String) - Constructor for class wavelet_util.gnuplot3D
 
graph_coef(double[]) - Method in class timeseries_histo
Graph the coefficients.

H

haar_calc(double[]) - Method in class wavelets.simple_haar
Recursively calculate the Haar transform.
haar_value - Variable in class wavelets.simple_haar
 
high - Variable in class experimental.statistics.bell_info
 
high - Variable in class experimental.histo.mean_median
 
high - Variable in class wavelet_util.bell_curves.low_high
 
histo - class experimental.histo.
Support for generating histograms for the Haar wavelet coefficients.
histo.bin - class experimental.histo.bin.
 
histo.bin(histo) - Constructor for class experimental.histo.bin
 
histo.mean_median - class experimental.histo.mean_median.
 
histo.mean_median(histo) - Constructor for class experimental.histo.mean_median
 
histo() - Constructor for class experimental.histo
 
histogram_coef(PrintWriter, int, bell_curves.low_high, double[]) - Method in class wavelet_util.bell_curves
Write out a histogram for the Haar coefficient frequency spectrum in gnuplot format.
histogram(double[], String) - Method in class experimental.histo
histo class constructor
histogram(noise_filter.bin[], noise_filter.point[]) - Method in class wavelet_util.noise_filter
Build a histogram from the sorted data in the pointz array.
histoTest - class histoTest.
Test the histogram generation code.
histoTest() - Constructor for class histoTest
 

I

index - Variable in class sortTest.testElem
 
index - Variable in class wavelet_util.noise_filter.point
 
inplace_haar - class wavelets.inplace_haar.
Copyright and Use
inplace_haar_test - class inplace_haar_test.
 
inplace_haar_test() - Constructor for class inplace_haar_test
 
inplace_haar() - Constructor for class wavelets.inplace_haar
 
integrate_curve(statistics.point[]) - Method in class experimental.statistics
The amount of Gaussian noise in the Haar wavelet coefficients can be seen by graphing a histogram of the coefficients along with a Gaussian (normal) curve.
inverse_butterfly() - Method in class wavelets.inplace_haar
Calculate the inverse Haar transform on the result of the in-place Haar transform.
inverse_ordered() - Method in class wavelets.inplace_haar
Calculate the inverse Haar transform on an ordered set of coefficients.
inverse() - Method in class wavelets.wavelet_base
 
inverse() - Method in class wavelets.inplace_haar
Regenerate the data from the Haar wavelet function.
inverse() - Method in class wavelets.simple_haar
Calculate the inverse haar transform from the coefficients and the Haar value.
isOrdered - Variable in class wavelets.inplace_haar
initially false: true means wavefx is ordered by frequency

L

length() - Method in class double_vec.double_vec
Return the number of elements currently in the array
log2(int) - Static method in class wavelet_util.binary
Calculate floor( log2( val ) ), where val > 0
low - Variable in class experimental.statistics.bell_info
 
low - Variable in class experimental.histo.mean_median
 
low - Variable in class wavelet_util.bell_curves.low_high
 

M

main(String[]) - Static method in class curve_plot_test
 
main(String[]) - Static method in class filter_test
 
main(String[]) - Static method in class histoTest
 
main(String[]) - Static method in class inplace_haar_test
 
main(String[]) - Static method in class readTest
 
main(String[]) - Static method in class simple_haar_test
 
main(String[]) - Static method in class sortTest
 
main(String[]) - Static method in class spectrum_test
 
main(String[]) - Static method in class statTest
 
main(String[]) - Static method in class timeseries_histo
 
main(String[]) - Static method in class timeseries_test
 
mean - Variable in class experimental.statistics.bell_info
 
mean - Variable in class experimental.histo.mean_median
 
mean - Variable in class wavelet_util.bell_curves.bell_info
 
mean - Variable in class wavelet_util.noise_filter.bell_info
 
median - Variable in class experimental.histo.mean_median
 
min_spectrum - Variable in class wavelet_util.coef_spectrum
 

N

nearestPower2(int) - Static method in class wavelet_util.binary
nearestPower2
new_info(double, double, double, double) - Method in class experimental.statistics
Allocate a bell_info object and initialize it with the arguments mean, sigma, low and high.
noise_filter - class wavelet_util.noise_filter.
The objective in filtering is to remove noise while keeping the features that are interesting.
noise_filter.bell_info - class wavelet_util.noise_filter.bell_info.
Bell curve info: mean, sigma (the standard deviation)
noise_filter.bell_info(noise_filter) - Constructor for class wavelet_util.noise_filter.bell_info
 
noise_filter.bell_info(noise_filter, double, double) - Constructor for class wavelet_util.noise_filter.bell_info
 
noise_filter.bin - class wavelet_util.noise_filter.bin.
A histogram bin
noise_filter.bin(noise_filter, double) - Constructor for class wavelet_util.noise_filter.bin
 
noise_filter.point - class wavelet_util.noise_filter.point.
The point class represents a coefficient value so that it can be sorted for histogramming and then resorted back into the orignal ordering (e.g., sorted by value and then sorted by index)
noise_filter.point(noise_filter, int, double) - Constructor for class wavelet_util.noise_filter.point
 
noise_filter.sort_by_index - class wavelet_util.noise_filter.sort_by_index.
Sort an array of point objects by the index field.
noise_filter.sort_by_index(noise_filter) - Constructor for class wavelet_util.noise_filter.sort_by_index
 
noise_filter.sort_by_val - class wavelet_util.noise_filter.sort_by_val.
Sort an array of point objects by the val filed.
noise_filter.sort_by_val(noise_filter) - Constructor for class wavelet_util.noise_filter.sort_by_val
 
noise_filter() - Constructor for class wavelet_util.noise_filter
 
normal_curve(PrintWriter, int, bell_curves.low_high, double[]) - Method in class wavelet_util.bell_curves
Output a gnuplot formatted histogram of the area under a normal curve through the range m.low to m.high based on the mean and standard deviation of the values in the array v.
normal_curve(statistics.bell_info, int) - Static method in class experimental.statistics
Calculate the information for a graph (composed of point objects) based on the bell_info argument.
normal_interval(bell_curves.bell_info, double, double, int) - Method in class wavelet_util.bell_curves
normal_interval
normal_interval(noise_filter.bell_info, double, double, int) - Method in class wavelet_util.noise_filter
normal_interval
normalize_to_zero(double[]) - Method in class wavelet_util.noise_filter
Normalize the noise array to zero by subtracting the smallest value from all points.
num_elem - Variable in class double_vec.double_vec
 

O

only_one_spectrum(double[]) - Method in class wavelet_util.coef_spectrum
Moving from high frequency to lower frequency, regenerate the time series from only one spectrum.
OpenFile(String) - Method in class experimental.plot
 
OpenFile(String) - Method in class wavelet_util.plot
 
openStream(String) - Method in class dataInput.tsRead
 
order() - Method in class wavelets.inplace_haar
Order the result of the in-place Haar wavelet function, referenced by wavefx.
output_time_series(String, double[]) - Method in class wavelet_util.coef_spectrum
Regenerate the time series from the coefficient array and output the time series to a file.
outputSpectrum(PrintWriter, double[], int, int) - Method in class wavelet_util.gnuplot3D
Output a Haar spectrum where the x-axis is the sample value number, the y-axis is the log2 of the window width and the z-axis is the value (e.g., average or average difference).

P

percent - Variable in class experimental.histo.bin
 
percent - Variable in class wavelet_util.bell_curves.bin
 
plot - class experimental.plot.
 
plot - class wavelet_util.plot.
 
plot_curves(double[]) - Method in class wavelet_util.bell_curves
This function is passed an ordered set of Haar wavelet coefficients.
plot_freq(double[]) - Method in class wavelet_util.bell_curves
plot_freq
plot_log(double[]) - Method in class curve_plot_test
 
plot() - Constructor for class experimental.plot
 
plot() - Constructor for class wavelet_util.plot
 
point_array(int) - Method in class experimental.statistics
Allocate an array of point objects.
power2(byte) - Static method in class wavelet_util.binary
Calculate 2n where n >= 0
pr_ordered() - Method in class wavelets.inplace_haar
Print the Haar value and coefficient showing the ordering.
pr_vals(double[]) - Method in class inplace_haar_test
Print an array of doubles
pr_vals(double[]) - Method in class simple_haar_test
Print an array of doubles
pr_values() - Method in class wavelets.simple_haar
Print the data values.
pr() - Method in class wavelets.wavelet_base
Print the wavelet function result.
pr() - Method in class wavelets.inplace_haar
Print the result of the Haar wavelet function.
pr() - Method in class wavelets.simple_haar
Print the simple Haar object (e.g, the final Haar step value and the coefficients.
print_bins(histo.bin[]) - Method in class experimental.statistics
Print a histogram bin to stanard output.
print_curve(statistics.bell_info, statistics.point[]) - Static method in class statTest
 
printArray(sortTest.testElem[]) - Method in class sortTest
 
pushStream - Variable in class dataInput.tsRead
 

Q

qsort - class sort.qsort.
This class supports the Quicksort algorithm.
qsort() - Constructor for class sort.qsort
 
QuickSort(double[], int, int) - Static method in class sort.qsort
This is a generic version of C.A.R Hoare's Quick Sort algorithm.
QuickSort(Object[], int, int) - Method in class sort.generic_sort
This is a generic version of C.A.R Hoare's Quick Sort algorithm.

R

read_date() - Method in class dataInput.tsRead
read_date
read_double(double[]) - Method in class dataInput.tsRead
read_double
readTest - class readTest.
Test the time series read code.
readTest() - Constructor for class readTest
 
remove() - Method in class double_vec.double_vec
Remove one item from the end of the array.
reverseCoef() - Method in class wavelets.simple_haar
The Haar transform coefficients are generated from the longest coefficient vector (highest frequency) to the shortest (lowest frequency).

S

set_size(int) - Method in class double_vec.double_vec
Set the number of data elements in the array to a new value (note that this will usually be smaller than the array size, unless a power of two is chosen for "new_size").
setElementAt(double, int) - Method in class double_vec.double_vec
Assign array element ix the value val.
setIsOrdered() - Method in class wavelets.inplace_haar
 
setSize(int) - Method in class dataInput.tsRead
setSize
setWavefx(double[]) - Method in class wavelets.inplace_haar
Set the wavefx reference variable to the data vector.
sigma - Variable in class experimental.statistics.bell_info
 
sigma - Variable in class wavelet_util.bell_curves.bell_info
 
sigma - Variable in class wavelet_util.noise_filter.bell_info
 
simple_haar - class wavelets.simple_haar.
Class simple_haar
simple_haar_test - class simple_haar_test.
 
simple_haar_test() - Constructor for class simple_haar_test
 
simple_haar() - Constructor for class wavelets.simple_haar
 
skip_spaces() - Method in class dataInput.tsRead
skip_spaces Skip white space characters.
sort - package sort
 
sort(double[]) - Static method in class sort.qsort
 
sort(Object[]) - Method in class sort.generic_sort
 
sortTest - class sortTest.
Test for generic sort
sortTest.sort_testElem_index - class sortTest.sort_testElem_index.
Sort by index
sortTest.sort_testElem_index(sortTest) - Constructor for class sortTest.sort_testElem_index
 
sortTest.sort_testElem_val - class sortTest.sort_testElem_val.
Sort by value
sortTest.sort_testElem_val(sortTest) - Constructor for class sortTest.sort_testElem_val
 
sortTest.testElem - class sortTest.testElem.
Test data structure: index is the array index and val is the data element.
sortTest.testElem(sortTest, int) - Constructor for class sortTest.testElem
 
sortTest() - Constructor for class sortTest
 
spectrum_calc(double[], int, int) - Method in class wavelets.inplace_haar
Recursively calculate the Haar spectrum, replacing data in the original array with the calculated averages.
spectrum_test - class spectrum_test.
Generate gnuplot files for the time series with various spectrum removed.
spectrum_test() - Constructor for class spectrum_test
 
start - Variable in class experimental.histo.bin
 
start - Variable in class wavelet_util.bell_curves.bin
 
start - Variable in class wavelet_util.noise_filter.bin
 
StartArraySize - Static variable in class double_vec.double_vec
 
statistics - class experimental.statistics.
Normal curve and normal curve graphing functions
statistics.bell_info - class experimental.statistics.bell_info.
Bell curve info: mean, sigma (the standard deviation), low (the start of the curve area) and high (the end of the curve area).
statistics.bell_info(statistics) - Constructor for class experimental.statistics.bell_info
 
statistics.bell_info(statistics, double, double, double, double) - Constructor for class experimental.statistics.bell_info
 
statistics.point - class experimental.statistics.point.
A point on a graph
statistics.point(statistics) - Constructor for class experimental.statistics.point
 
statistics() - Constructor for class experimental.statistics
 
statTest - class statTest.
Test the experimental code to generate a normal curve with the mean and standard deviation derived from a coefficient spectrum (in this case the highest frequency spectrum).
statTest() - Constructor for class statTest
 
stddev(double[]) - Static method in class experimental.statistics
Calculate the mean, standard deviation.
stddev(double[]) - Method in class wavelet_util.bell_curves
Calculate the mean and standard deviation.
subtract_gauss_curve(noise_filter.bin[], noise_filter.bell_info, int, double[]) - Method in class wavelet_util.noise_filter
Subtract the gaussian (or normal) curve from the histogram of the coefficients.
swap(double[], int, int) - Static method in class sort.qsort
 
swap(Object[], int, int) - Method in class sort.generic_sort
Exchange element a[i] and a[j]

T

time_series_read() - Method in class dataInput.tsRead
time_series_read
timeSeries - Variable in class dataInput.tsRead
 
timeseries_histo - class timeseries_histo.
Generate histograms for the Haar coefficients created by applying the Haar transform to the time series for the Applied Materials (symbol: AMAT) daily close price.
timeseries_histo() - Constructor for class timeseries_histo
 
timeseries_test - class timeseries_test.
Test the Inplace Haar wavelet algorithm with a financial time series, in this case, the daily close price for Applied Materials (symbol: AMAT).
timeseries_test() - Constructor for class timeseries_test
 
tsRead - class dataInput.tsRead.
class tsRead
tsRead.badDataError - exception dataInput.tsRead.badDataError.
class badData : an exception for bad data in the file.
tsRead.badDataError(tsRead) - Constructor for class dataInput.tsRead.badDataError
 
tsRead.badDataError(tsRead, String) - Constructor for class dataInput.tsRead.badDataError
 
tsRead(String) - Constructor for class dataInput.tsRead
tsRead constructor
twice() - Method in class double_vec.double_vec
Double the amount of memory allocated for the array.

V

val - Variable in class sortTest.testElem
 
val - Variable in class wavelet_util.noise_filter.point
 
vals - Variable in class wavelet_util.noise_filter.bin
 

W

wavefx - Variable in class wavelets.inplace_haar
result of calculating the Haar wavelet
wavelet_base - class wavelets.wavelet_base.
Wavelet base class
wavelet_base() - Constructor for class wavelets.wavelet_base
 
wavelet_calc(double[]) - Method in class wavelets.wavelet_base
Abstract function for calculating a wavelet function.
wavelet_calc(double[]) - Method in class wavelets.inplace_haar
Calculate the in-place Haar wavelet function.
wavelet_calc(double[]) - Method in class wavelets.simple_haar
Calculate the Haar wavelet transform (the ordered fast Haar wavelet tranform).
wavelet_spectrum(double[]) - Method in class wavelets.inplace_haar
Calculate the Haar wavelet spectrum
wavelet_test(double[]) - Method in class inplace_haar_test
Test the simple_haar wavelet code, using the vals argument.
wavelet_test(double[]) - Method in class simple_haar_test
Test the simple_haar wavelet code, using the vals argument.
wavelet_util - package wavelet_util
 
wavelets - package wavelets
 

X

x - Variable in class experimental.statistics.point
 

Y

y - Variable in class experimental.statistics.point
 

Z

zero_points(noise_filter.bin, int, double[]) - Method in class wavelet_util.noise_filter
Set num_points values in the histogram bin b to zero.

A B C D E F G H I L M N O P Q R S T V W X Y Z