What would be the java equivalent of the following code?
import scipy
from scipy.stats import zscore
zlist = [9967,11281,10752,10576,2366,11882,11798,]
z = zscore(zlist)
for e in z:
print e,scipy.stats.norm.sf(abs(e))
And the answer is:
private void run() {
double[] values = {9967,11281,10752,10576,2366,11882,11798};
double variance = StatUtils.populationVariance(values);
double sd = Math.sqrt(variance);
double mean = StatUtils.mean(values);
NormalDistribution nd = new NormalDistribution();
for ( double value: values ) {
double stdscore = (value-mean)/sd;
double sf = 1.0 - nd.cumulativeProbability(Math.abs(stdscore));
System.out.println("" + stdscore + " " + sf);
}
}
This is using The Apache Commons Mathematics Library
EDIT: Or, even better:
import java.util.function.BiConsumer;
import org.apache.commons.math3.distribution.NormalDistribution;
import org.apache.commons.math3.distribution.RealDistribution;
import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics;
public class ZScore {
public static void main(String[] args) {
ZScore program = new ZScore();
double[] values = {9967,11281,10752,10576,2366,11882,11798};
program.computeZScoreAndSurvivalFunctions(
new DescriptiveStatistics(values),
new NormalDistribution(),
(zscore, sf)->System.out.println(""+zscore+" "+sf)
);
}
private void computeZScoreAndSurvivalFunctions(
DescriptiveStatistics ds,
RealDistribution dist,
BiConsumer<Double, Double> consumer
) {
double variance = ds.getPopulationVariance();
double sd = Math.sqrt(variance);
double mean = ds.getMean();
for ( int index = 0; index < ds.getN(); ++index) {
double zscore = (ds.getElement(index)-mean)/sd;
double sf = 1.0 - dist.cumulativeProbability(Math.abs(zscore));
consumer.accept(zscore, sf);
}
}
}
I have this .java datafile. The data file is a part of an imagej plugin.
The whole data structure is here:
enter link description here
package mosaic.plugins;
import ij.IJ;
import ij.ImagePlus;
import ij.macro.Interpreter;
import ij.measure.ResultsTable;
import ij.process.ByteProcessor;
import java.awt.Color;
import java.awt.event.ActionEvent;
import java.awt.event.ActionListener;
import java.util.Map;
import java.util.Map.Entry;
import java.util.TreeMap;
import javax.swing.BorderFactory;
import javax.swing.JButton;
import javax.swing.JDialog;
import javax.swing.JPanel;
import javax.swing.JTextPane;
import javax.swing.WindowConstants;
import mosaic.plugins.utils.PlugIn8bitBase;
import net.imglib2.Cursor;
import net.imglib2.IterableInterval;
import net.imglib2.RandomAccess;
import net.imglib2.img.ImagePlusAdapter;
import net.imglib2.img.Img;
import net.imglib2.img.ImgFactory;
import net.imglib2.img.array.ArrayImgFactory;
import net.imglib2.img.display.imagej.ImageJFunctions;
import net.imglib2.type.NativeType;
import net.imglib2.type.numeric.NumericType;
import net.imglib2.type.numeric.RealType;
import net.imglib2.type.numeric.integer.UnsignedByteType;
import net.imglib2.type.numeric.real.FloatType;
import net.imglib2.view.IntervalView;
import net.imglib2.view.Views;
public class Naturalization extends PlugIn8bitBase
{
// Precision in finding your best T
private static final float EPS = 0.0001f;
// Prior parameter for first oder
// In this case is for all channels
// Fixed parameter
private static final float T1_pr = 0.3754f;
// Number of bins for the Laplacian Histogram
// In general is 4 * N_Grad
// max of laplacian value is 4 * 255
private static final int N_Lap = 2041;
// Offset shift in the histogram bins
// Has to be N_Lap / 2;
private static final int Lap_Offset = 1020;
// Number of bins for the Gradient
private static final int N_Grad = 512;
// Offset for the gradient histogram shift
private static final int Grad_Offset = 256;
// Prior parameter for second order (Parameters learned from trained data set)
// For different color R G B
// For one channel image use an average of them
private final float T2_pr[] = {0.2421f ,0.2550f, 0.2474f, 0.24816666f};
// Keeps values of PSNR for all images and channels in case of RGB. Maps: imageNumber -> map (channel, PSNR value)
private final Map<Integer, Map<Integer, Float>> iPsnrOutput = new TreeMap<Integer, Map<Integer, Float>>();
private synchronized void addPsnr(int aSlice, int aChannel, float aValue) {
Map<Integer, Float> map = iPsnrOutput.get(aSlice);
boolean isNewMap = false;
if (map == null) {
map = new TreeMap<Integer, Float>();
isNewMap = true;
}
map.put(aChannel, aValue);
if (isNewMap) {
iPsnrOutput.put(aSlice, map);
}
}
#Override
protected void processImg(ByteProcessor aOutputImg, ByteProcessor aOrigImg, int aChannelNumber) {
// perform naturalization
final ImagePlus naturalizedImg = naturalize8bitImage(aOrigImg, aChannelNumber);
// set processed pixels to output image
aOutputImg.setPixels(naturalizedImg.getProcessor().getPixels());
}
#Override
protected void postprocessBeforeShow() {
// Create result table with all stored PSNRs.
final ResultsTable rs = new ResultsTable();
for (final Entry<Integer, Map<Integer, Float>> e : iPsnrOutput.entrySet()) {
rs.incrementCounter();
for (final Entry<Integer, Float> m : e.getValue().entrySet()) {
switch(m.getKey()) {
case CHANNEL_R: rs.addValue("Naturalization R", m.getValue()); rs.addValue("Estimated R PSNR", calculate_PSNR(m.getValue())); break;
case CHANNEL_G: rs.addValue("Naturalization G", m.getValue()); rs.addValue("Estimated G PSNR", calculate_PSNR(m.getValue())); break;
case CHANNEL_B: rs.addValue("Naturalization B", m.getValue()); rs.addValue("Estimated B PSNR", calculate_PSNR(m.getValue())); break;
case CHANNEL_8G: rs.addValue("Naturalization", m.getValue()); rs.addValue("Estimated PSNR", calculate_PSNR(m.getValue())); break;
default: break;
}
}
}
if (!Interpreter.isBatchMode()) {
rs.show("Naturalization and PSNR");
showMessage();
}
}
private ImagePlus naturalize8bitImage(ByteProcessor imp, int aChannelNumber) {
Img<UnsignedByteType> TChannel = ImagePlusAdapter.wrap(new ImagePlus("", imp));
final float T2_prior = T2_pr[(aChannelNumber <= CHANNEL_B) ? 2-aChannelNumber : CHANNEL_8G];
final float[] result = {0.0f}; // ugly but one of ways to get result back via parameters;
// Perform naturalization and store PSNR result. Finally return image in ImageJ format.
TChannel = performNaturalization(TChannel, T2_prior, result);
addPsnr(imp.getSliceNumber(), aChannelNumber, result[0]);
return ImageJFunctions.wrap(TChannel,"temporaryName");
}
/**
* Naturalize the image
* #param Img original image
* #param Theta parameter
* #param Class<T> Original image
* #param Class<S> Calculation Type
* #param T2_prior Prior to use
* #param result One element array to store nautralization factor
*/
private <T extends NumericType<T> & NativeType<T> & RealType<T>, S extends RealType<S>> Img<T> doNaturalization(Img<T> image_orig, S Theta,Class<T> cls_t, float T2_prior, float[] result) throws InstantiationException, IllegalAccessException
{
if (image_orig == null) {return null;}
// Check that the image data set is 8 bit
// Otherwise return an error or hint to scale down
final T image_check = cls_t.newInstance();
final Object obj = image_check;
if (!(obj instanceof UnsignedByteType)) {
IJ.error("Error it work only with 8-bit type");
return null;
}
final float Nf = findNaturalizationFactor(image_orig, Theta, T2_prior);
result[0] = Nf;
final Img<T> image_result = naturalizeImage(image_orig, Nf, cls_t);
return image_result;
}
private <S extends RealType<S>, T extends NumericType<T> & NativeType<T> & RealType<T>>
Img<T> naturalizeImage(Img<T> image_orig, float Nf, Class<T> cls_t)
throws InstantiationException, IllegalAccessException
{
// Mean of the original image
// S mean_original = cls_s.newInstance();
// Mean<T,S> m = new Mean<T,S>();
// m.compute(image_orig.cursor(), mean_original);
// TODO: quick fix for deprecated code above. Is new 'mean' utility introduced in imglib2?
float mean_original = 0.0f;
final Cursor<T> c2 = image_orig.cursor();
float count = 0.0f;
while (c2.hasNext()) {
c2.next();
mean_original += c2.get().getRealFloat();
count += 1.0f;
}
mean_original /= count;
// Create result image
final long[] origImgDimensions = new long[2];
image_orig.dimensions(origImgDimensions);
final Img<T> image_result = image_orig.factory().create(origImgDimensions, cls_t.newInstance());
// for each pixel naturalize
final Cursor<T> cur_orig = image_orig.cursor();
final Cursor<T> cur_ir = image_result.cursor();
while (cur_orig.hasNext()) {
cur_orig.next();
cur_ir.next();
final float tmp = cur_orig.get().getRealFloat();
// Naturalize
float Nat = (int) ((tmp - mean_original)*Nf + mean_original + 0.5);
if (Nat < 0)
{Nat = 0;}
else if (Nat > 255)
{Nat = 255;}
cur_ir.get().setReal(Nat);
}
return image_result;
}
private <S extends RealType<S>, T extends NumericType<T> & NativeType<T> & RealType<T>> float findNaturalizationFactor(Img<T> image_orig, S Theta, float T2prior) {
final ImgFactory<FloatType> imgFactoryF = new ArrayImgFactory<FloatType>();
// Create one dimensional image (Histogram)
final Img<FloatType> LapCDF = imgFactoryF.create(new long[] {N_Lap}, new FloatType());
// Two dimensional image for Gradient
final Img<FloatType> GradCDF = imgFactoryF.create(new long[] {N_Grad, 2}, new FloatType());
// GradientCDF = Integral of the histogram of the of the Gradient field
// LaplacianCDF = Integral of the Histogram of the Laplacian field
final Img<FloatType> GradD = create2DGradientField();
calculateLaplaceFieldAndGradient(image_orig, LapCDF, GradD);
convertGrad2dToCDF(GradD);
calculateGradCDF(GradCDF, GradD);
calculateLapCDF(LapCDF);
// For each channel find the best T1
// EPS=precision
// for X component
float T_tmp = (float)FindT(Views.iterable(Views.hyperSlice(GradCDF, GradCDF.numDimensions()-1 , 0)), N_Grad, Grad_Offset, EPS);
// for Y component
T_tmp += FindT(Views.iterable(Views.hyperSlice(GradCDF, GradCDF.numDimensions()-1 , 1)), N_Grad, Grad_Offset, EPS);
// Average them and divide by the prior parameter
final float T1 = T_tmp/(2*T1_pr);
// Find the best parameter and divide by the T2 prior
final float T2 = (float)FindT(LapCDF, N_Lap, Lap_Offset, EPS)/T2prior;
// Calculate naturalization factor!
final float Nf = (float) ((1.0-Theta.getRealDouble())*T1 + Theta.getRealDouble()*T2);
return Nf;
}
/**
* Calculate the peak SNR from the Naturalization factor
*
* #param Nf naturalization factor
* #return the PSNR
*/
String calculate_PSNR(double x)
{
if (x >= 0 && x <= 0.934)
{
return String.format("%.2f", new Float(23.65 * Math.exp(0.6 * x) - 20.0 * Math.exp(-7.508 * x)));
}
else if (x > 0.934 && x < 1.07)
{
return new String("> 40");
}
else if (x >= 1.07 && x < 1.9)
{
return String.format("%.2f", new Float(-11.566 * x + 52.776));
}
else
{
return String.format("%.2f",new Float(13.06*x*x*x*x - 121.4 * x*x*x + 408.5 * x*x -595.5*x + 349));
}
}
private Img<UnsignedByteType> performNaturalization(Img<UnsignedByteType> channel, float T2_prior, float[] result) {
// Parameters balance between first order and second order
final FloatType Theta = new FloatType(0.5f);
try {
channel = doNaturalization(channel, Theta, UnsignedByteType.class, T2_prior, result);
} catch (final InstantiationException e) {
e.printStackTrace();
} catch (final IllegalAccessException e) {
e.printStackTrace();
}
return channel;
}
// Original data
// N = nuber of bins
// offset of the histogram
// T current
private double FindT_Evalue(float[] p_d, int N, int offset, float T)
{
double error = 0;
for (int i=-offset; i<N-offset; ++i) {
final double tmp = Math.atan(T*(i)) - p_d[i+offset];
error += (tmp*tmp);
}
return error;
}
// Find the T
// data CDF Histogram
// N number of bins
// Offset of the histogram
// eps precision
private double FindT(IterableInterval<FloatType> data, int N, int OffSet, float eps)
{
//find the best parameter between data and model atan(Tx)/pi+0.5
// Search between 0 and 1.0
float left = 0;
float right = 1.0f;
float m1 = 0.0f;
float m2 = 0.0f;
// Crate p_t to save computation (shift and rescale the original CDF)
final float p_t[] = new float[N];
// Copy the data
final Cursor<FloatType> cur_data = data.cursor();
for (int i = 0; i < N; ++i)
{
cur_data.next();
p_t[i] = (float) ((cur_data.get().getRealFloat() - 0.5)*Math.PI);
}
// While the precision is bigger than eps
while (right-left>=eps)
{
// move left and right of 1/3 (m1 and m2)
m1=left+(right-left)/3;
m2=right-(right-left)/3;
// Evaluate on m1 and m2, ane move the extreme point
if (FindT_Evalue(p_t, N, OffSet, m1) <=FindT_Evalue(p_t, N, OffSet, m2)) {
right=m2;
}
else {
left=m1;
}
}
// return the average
return (m1+m2)/2;
}
private Img<FloatType> create2DGradientField() {
final long dims[] = new long[2];
dims[0] = N_Grad;
dims[1] = N_Grad;
final Img<FloatType> GradD = new ArrayImgFactory<FloatType>().create(dims, new FloatType());
return GradD;
}
private void calculateLapCDF(Img<FloatType> LapCDF) {
final RandomAccess<FloatType> Lap_hist2 = LapCDF.randomAccess();
//convert Lap to CDF
for (int i = 1; i < N_Lap; ++i)
{
Lap_hist2.setPosition(i-1,0);
final float prec = Lap_hist2.get().getRealFloat();
Lap_hist2.move(1,0);
Lap_hist2.get().set(Lap_hist2.get().getRealFloat() + prec);
}
}
private void calculateGradCDF(Img<FloatType> GradCDF, Img<FloatType> GradD) {
final RandomAccess<FloatType> Grad_dist = GradD.randomAccess();
// Gradient on x pointer
final IntervalView<FloatType> Gradx = Views.hyperSlice(GradCDF, GradCDF.numDimensions()-1 , 0);
// Gradient on y pointer
final IntervalView<FloatType> Grady = Views.hyperSlice(GradCDF, GradCDF.numDimensions()-1 , 1);
integrateOverRowAndCol(Grad_dist, Gradx, Grady);
scaleGradiens(Gradx, Grady);
}
private void scaleGradiens(IntervalView<FloatType> Gradx, IntervalView<FloatType> Grady) {
final RandomAccess<FloatType> Gradx_r2 = Gradx.randomAccess();
final RandomAccess<FloatType> Grady_r2 = Grady.randomAccess();
//scale, divide the number of integrated bins
for (int i = 0; i < N_Grad; ++i)
{
Gradx_r2.setPosition(i,0);
Grady_r2.setPosition(i,0);
Gradx_r2.get().set((float) (Gradx_r2.get().getRealFloat() / 255.0));
Grady_r2.get().set((float) (Grady_r2.get().getRealFloat() / 255.0));
}
}
private void integrateOverRowAndCol(RandomAccess<FloatType> Grad_dist, IntervalView<FloatType> Gradx, IntervalView<FloatType> Grady) {
final int[] loc = new int[2];
// pGrad2D has 2D CDF
final RandomAccess<FloatType> Gradx_r = Gradx.randomAccess();
// Integrate over the row
for (int i = 0; i < N_Grad; ++i)
{
loc[1] = i;
Gradx_r.setPosition(i,0);
// get the row
for (int j = 0; j < N_Grad; ++j)
{
loc[0] = j;
// Set the position
Grad_dist.setPosition(loc);
// integrate over the row to get 1D vector
Gradx_r.get().set(Gradx_r.get().getRealFloat() + Grad_dist.get().getRealFloat());
}
}
final RandomAccess<FloatType> Grady_r = Grady.randomAccess();
// Integrate over the column
for (int i = 0; i < N_Grad; ++i)
{
loc[1] = i;
Grady_r.setPosition(0,0);
for (int j = 0; j < N_Grad; ++j)
{
loc[0] = j;
Grad_dist.setPosition(loc);
Grady_r.get().set(Grady_r.get().getRealFloat() + Grad_dist.get().getRealFloat());
Grady_r.move(1,0);
}
}
}
private <T extends RealType<T>> void calculateLaplaceFieldAndGradient(Img<T> image, Img<FloatType> LapCDF, Img<FloatType> GradD) {
final RandomAccess<FloatType> Grad_dist = GradD.randomAccess();
final long[] origImgDimensions = new long[2];
image.dimensions(origImgDimensions);
final Img<FloatType> laplaceField = new ArrayImgFactory<FloatType>().create(origImgDimensions, new FloatType());
// Cursor localization
final int[] indexD = new int[2];
final int[] loc_p = new int[2];
final RandomAccess<T> img_cur = image.randomAccess();
final RandomAccess<FloatType> Lap_f = laplaceField.randomAccess();
final RandomAccess<FloatType> Lap_hist = LapCDF.randomAccess();
// Normalization 1/(Number of pixel of the original image)
long n_pixel = 1;
for (int i = 0 ; i < laplaceField.numDimensions() ; i++)
{n_pixel *= laplaceField.dimension(i)-2;}
// unit to sum
final double f = 1.0/(n_pixel);
// Inside the image for Y
final Cursor<FloatType> cur = laplaceField.cursor();
// For each point of the Laplacian field
while (cur.hasNext())
{
cur.next();
// Localize cursors
cur.localize(loc_p);
// Exclude the border
boolean border = false;
for (int i = 0 ; i < image.numDimensions() ; i++)
{
if (loc_p[i] == 0)
{border = true;}
else if (loc_p[i] == image.dimension(i)-1)
{border = true;}
}
if (border == true) {
continue;
}
// get the stencil value;
img_cur.setPosition(loc_p);
float L = -4*img_cur.get().getRealFloat();
// Laplacian
for (int i = 0 ; i < 2 ; i++)
{
img_cur.move(1, i);
final float G_p = img_cur.get().getRealFloat();
img_cur.move(-1,i);
final float G_m = img_cur.get().getRealFloat();
img_cur.move(-1, i);
final float L_m = img_cur.get().getRealFloat();
img_cur.setPosition(loc_p);
L += G_p + L_m;
// Calculate the gradient + convert into bin
indexD[1-i] = (int) (Grad_Offset + G_p - G_m);
}
Lap_f.setPosition(loc_p);
// Set the Laplacian field
Lap_f.get().setReal(L);
// Histogram bin conversion
L += Lap_Offset;
Lap_hist.setPosition((int)(L),0);
Lap_hist.get().setReal(Lap_hist.get().getRealFloat() + f);
Grad_dist.setPosition(indexD);
Grad_dist.get().setReal(Grad_dist.get().getRealFloat() + f);
}
}
private void convertGrad2dToCDF(Img<FloatType> GradD) {
final RandomAccess<FloatType> Grad_dist = GradD.randomAccess();
final int[] loc = new int[GradD.numDimensions()];
// for each row
for (int j = 0; j < GradD.dimension(1); ++j)
{
loc[1] = j;
for (int i = 1; i < GradD.dimension(0) ; ++i)
{
loc[0] = i-1;
Grad_dist.setPosition(loc);
// Precedent float
final float prec = Grad_dist.get().getRealFloat();
// Move to the actual position
Grad_dist.move(1, 0);
// integration up to the current position
Grad_dist.get().set(Grad_dist.get().getRealFloat() + prec);
}
}
//col integration
for (int j = 1; j < GradD.dimension(1); ++j)
{
// Move to the actual position
loc[1] = j-1;
for (int i = 0; i < GradD.dimension(0); ++i)
{
loc[0] = i;
Grad_dist.setPosition(loc);
// Precedent float
final float prec = Grad_dist.get().getRealFloat();
// Move to the actual position
Grad_dist.move(1, 1);
Grad_dist.get().set(Grad_dist.get().getRealFloat() + prec);
}
}
}
/**
* Show information about authors and paper.
*/
private void showMessage()
{
// Create main window with panel to store gui components
final JDialog win = new JDialog((JDialog)null, "Naturalization", true);
final JPanel msg = new JPanel();
msg.setBorder(BorderFactory.createEmptyBorder(10, 10, 10, 10));
// Create message not editable but still focusable for copying
final JTextPane text = new JTextPane();
text.setContentType("text/html");
text.setText("<html>Y. Gong and I. F. Sbalzarini. Image enhancement by gradient distribution specification. In Proc. ACCV, <br>"
+ "12th Asian Conference on Computer Vision, Workshop on Emerging Topics in Image Enhancement and Restoration,<br>"
+ "pages w7–p3, Singapore, November 2014.<br><br>"
+ "Y. Gong and I. F. Sbalzarini, Gradient Distributions Priors for Biomedical Image Processing, 2014<br>http://arxiv.org/abs/1408.3300<br><br>"
+ "Y. Gong and I. F. Sbalzarini. A Natural-Scene Gradient Distribution Prior and its Application in Light-Microscopy Image Processing.<br>"
+ "IEEE Journal of Selected Topics in Signal Processing, Vol.10, No.1, February 2016, pages 99-114<br>"
+ "ISSN: 1932-4553, DOI: 10.1109/JSTSP.2015.2506122<br><br>"
+ "</html>");
text.setBorder(BorderFactory.createLineBorder(Color.BLACK, 2));
text.setEditable(false);
msg.add(text);
// Add button "Close" for closing window easily
final JButton button = new JButton("Close");
button.addActionListener(new ActionListener() {
#Override
public void actionPerformed(ActionEvent e) {
win.dispose();
}
});
msg.add(button);
// Finally show window with message
win.add(msg);
win.pack();
win.setDefaultCloseOperation(WindowConstants.DISPOSE_ON_CLOSE);
win.setVisible(true);
}
#Override
protected boolean showDialog() {
return true;
}
#Override
protected boolean setup(String aArgs) {
setFilePrefix("naturalized_");
return true;
}
}
I want it to compile it again and get a .class file or a whole .jar file of this plugin.
Which sturcuture and datas I need for get a .class data?
What are with the import files, where i can get the ij, java, javax and net files? In which structure must it be.
I am a novice in Java and only know, that the compiled command is javac.
on linux there is a command to do it which is javac
just : javac HelloWorld.java
it might be the same thing on windows but i am not sure (install a virtual linux box if there is no other way)
If something goes wrong google the error
If you want to compile a Java program from command line you should use the javac command and to invoke it just write java and then the name of your program.
Compiling a file you will have the .class file that you are looking for.
I am working on a simple program to calculate a mathematical equation. But there is a problem that I could not find. Any help would be greatly appreciated.
It seems a problem is with
alpha[j] = (double)(j-1)*2*Math.PI/(double)rotationNum;
NullPointerException is returned. There has to be some silly mistakes here.
import java.util.*;
import java.io.*;
//import Jama.Matrix;
class efun {
static double epso;
static double sigma;
static double alpha[];
static double charge;
static double axisR;
static double axisZ;
//static Random randGen;
static int numPoints = -1;
static int rotationNum;
public static void main (String[] args) {
try {
sigma = 300e-6*1e2;
epso = 8.854e-12;
/*Input arguments*/
numPoints = Integer.parseInt (args[0]);
FileReader fr = new FileReader(args[1]);
rotationNum = Integer.parseInt (args[2]);
BufferedReader br = new BufferedReader(fr);
double pointsR[] = new double[numPoints];
double pointsZ[] = new double[numPoints];
double chargeDensity[] = new double[numPoints];
double electricField = 0.0;
double ER = 0.0;
double EZ = 0.0;
double EY = 0.0;
for (int id = 0; id < numPoints; id++) {
// read file
while ( (line = br.readLine() )!= null) {
StringTokenizer stk = new StringTokenizer(line);
axisR = Double.parseDouble(stk.nextToken());
axisZ = Double.parseDouble(stk.nextToken());
charge = Double.parseDouble(stk.nextToken());
pointsR[id] = axisR;
pointsZ[id] = axisZ;
chargeDensity[id] = charge;
System.out.println("axisR: "+pointsR[id]+" and axisZ: "+ pointsZ[id]+"; its corresponding charge density is: "+ chargeDensity[id]);
double rotatedR[] = new double[numPoints];
double rotatedZ[] = new double[numPoints];
double rotatedY[] = new double[numPoints];
double sumSquarePoints[] = new double[numPoints];
for (int j = 1; j < rotationNum+1; j++) {
alpha[j] = (double)(j-1)*2*Math.PI/(double)rotationNum;
System.out.println("print alpha: "+alpha[j]);
rotatedR[id] = pointsR[id] - Math.cos(alpha[j])*pointsR[id];
rotatedZ[id] = pointsZ[id];
rotatedY[id] = pointsR[id] - Math.sin(alpha[j])*pointsR[id];
sumSquarePoints[id] = Math.sqrt(rotatedR[id]*rotatedR[id] + rotatedZ[id]*rotatedZ[id] + rotatedY[id]*rotatedY[id]);
ER += chargeDensity[id]*rotatedR[id]/(sumSquarePoints[id]*sumSquarePoints[id]*sumSquarePoints[id]);
EZ += chargeDensity[id]*rotatedZ[id]/(sumSquarePoints[id]*sumSquarePoints[id]*sumSquarePoints[id]);
EY += chargeDensity[id]*rotatedY[id]/(sumSquarePoints[id]*sumSquarePoints[id]*sumSquarePoints[id]);
System.out.println ("ER is: "+ ER);
System.out.println ("EZ is: "+ EZ);
System.out.println ("EY is: "+ EY);
}
}
}
electricField = sigma/(4*Math.PI*epso)*Math.sqrt(ER*ER + EZ*EZ + EY*EY);
System.out.println("electricField is: " + electricField);
}
catch (Exception e) {
e.printStackTrace();
}
}
}
You never initialized alpha, you only declared it, so you can't access alpha[j]. Initialize it and make sure that its size is large enough for every j:
alpha = new double[MY_SIZE];
Also, make sure that you're passing in at least 3 arguments to main so that rotationNum is assigned correctly.
Your class variable alpha is declared, but not initialized, so Java gives it the default value of null. The variable was never initialized to any array.
static double alpha[];
However, it doesn't look like you're using any other intended value in the array except for the current value. Just declare it to be a local double (not an array), and use it as a normal variable.
double alpha = (double)(j-1)*2*Math.PI/(double)rotationNum;
And use alpha instead of alpha[j] a few lines down from there.
You've never initialized alpha[]. Just like pontsR, pointsZ, and chargeDensity, you need to point alpha at a new array of doubles before you can use it.
Add alpha = new double[rotationNum ]; after getting rotationNum
I am trying to implement a simple BBands application using talib.
I do not have any expirience in Talib and technical Indicators.
Can someone have a look at say whether this is a good implementation of Bollinger Bands using Talib and give me advice how can it be improved. If anyone has expirience with Talib and Bollinger Bands it would be much appriciated to give me a hand. Here is the code.
import com.tictactec.ta.lib.Core;
import com.tictactec.ta.lib.MInteger;
import com.tictactec.ta.lib.RetCode;
import com.tictactec.ta.lib.*;
import com.tictactec.ta.lib.meta.helpers.*;
public class ExampleTALib
{
/**
* The total number of periods to generate data for.
*/
public static final int TOTAL_PERIODS = 100;
/**
* The number of periods to average together.
*/
public static final int PERIODS_AVERAGE = 20;
public static void main(String[] args)
{
double[] closePrice = new double[TOTAL_PERIODS];
double[] out = new double[TOTAL_PERIODS];
MInteger begin = new MInteger();
MInteger length = new MInteger();
double[] outRealUpperBand = new double[TOTAL_PERIODS];
double[] outRealMiddleBand = new double[TOTAL_PERIODS];
double[] outRealLowerBand = new double[TOTAL_PERIODS];
for (int i = 0; i < closePrice.length; i++) {
closePrice[i] = (double) i;
}
Core c = new Core();
// RetCode retCode = c.sma(0, closePrice.length - 1, closePrice, PERIODS_AVERAGE, begin, length, out);
// RetCode re = c.sma(startIdx, endIdx, inReal, optInTimePeriod, outBegIdx, outNBElement, outReal)
RetCode retCode = c.bbands(0, closePrice.length - 1, closePrice, PERIODS_AVERAGE,
1.0, 3.0, MAType.Ema, begin, length, outRealUpperBand, outRealMiddleBand, outRealLowerBand);
// RetCode re = c.bbands(startIdx, endIdx, inReal, optInTimePeriod, optInNbDevUp, optInNbDevDn, optInMAType, outBegIdx, outNBElement, outRealUpperBand, outRealMiddleBand, outRealLowerBand)
if (retCode == RetCode.Success) {
System.out.println("Output Begin:" + begin.value);
System.out.println("Output Begin:" + length.value);
for (int i = begin.value; i < closePrice.length; i++) {
StringBuilder line = new StringBuilder();
line.append("Period #");
line.append(i+1);
line.append(" close= ");
line.append(closePrice[i]);
line.append(" mov avg=");
line.append(out[i-begin.value]);
System.out.println(line.toString());
}
}
else {
System.out.println("Error");
}
}
}
I'm using weka java API to do a grid search in order to find the optimal parameters for MultilayerPerceptron. However, the RMSE (I'm doing regression here) given by my java code is different from that given by weka GUI. Here is the code:
public class ANN {
/**
* #param args
*/
public static void main(String[] args) throws Exception{
DataSource source = new DataSource("/home/yongfeng/ML/Project/choose_openning_price/holdout.arff");
Instances raw = source.getDataSet();
int trainSize = (int) Math.round(raw.numInstances()*0.666666666);
int testSize = raw.numInstances() - trainSize;
Instances train = new Instances(raw, 0, trainSize);
Instances test = new Instances(raw, trainSize, testSize);
train.setClassIndex(0);
test.setClassIndex(0);
final int sizeOfSearch = 15;
double[][] resultsArray = new double[sizeOfSearch][sizeOfSearch];
for (int i=0;i < sizeOfSearch;i++){
for (int j=0;j < sizeOfSearch;j++){
double m = i;
double k = j;
double learningRate = (m+1)/1000;
double momentum = (k+1)/100;
MultilayerPerceptron ann = new MultilayerPerceptron();
String options = String.format("-L %f -M %f -N 500 -V 0 -S 0 -E 20 -H a", learningRate, momentum);
ann.setOptions(weka.core.Utils.splitOptions(options));
ann.buildClassifier(train);
Evaluation eval = new Evaluation(train);
eval.evaluateModel(ann, test);
double error = eval.rootMeanSquaredError();
System.out.println("learningRate: " + learningRate + "\tMomentum: " + momentum + "\tError: " + error);
printOptions(ann.getOptions());
resultsArray[i][j] = error;
ann = null;
eval = null;
}
}
}
}
I even printed out the options in each iteration and they turned out to be the same as those in weka GUI. The attribute to be predicted is the first one, so setClassIndex(0); And used train-test set split to do the evaluation. Can anybody help? Many thanks!
Use weka.jar in weka installation folder in your java code.