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Unify line endings of checked-in files to linux-style(LF) 

To re-normalize all files that have a Windows-style line ending (CRLF or
\r\n) one can use the following CLI command called in the root directory
of the git repo (the dot is important):

git add --renormalize .
This commit is contained in:
Hannes Wellmann 2021-01-25 13:24:05 +01:00
parent b0564e8aba
commit fc59374756
31 changed files with 5847 additions and 5847 deletions
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28
jzy3d-core/src/test/java/log4j.properties
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@ -1,14 +1,14 @@
# Root logger option
log4j.rootLogger=WARN, stdout
# Direct log messages to stdout
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.Target=System.out
#log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
#log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p %c{1}:%L - %m%n
#log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p %c{1} - %m%n
#log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p [%c] [%t] - %m%n
log4j.appender.stdout.layout=org.apache.log4j.EnhancedPatternLayout
log4j.appender.stdout.layout.ConversionPattern=%-5p %d{HH:mm:ss,SSS} %c{1.} [%t] : %m%n
#log4j.appender.stdout.layout.ConversionPattern=%-5p %c{1.} [%t] %d{HH:mm:ss,SSS}: %m%n
# Root logger option
log4j.rootLogger=WARN, stdout
# Direct log messages to stdout
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.Target=System.out
#log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
#log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p %c{1}:%L - %m%n
#log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p %c{1} - %m%n
#log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p [%c] [%t] - %m%n
log4j.appender.stdout.layout=org.apache.log4j.EnhancedPatternLayout
log4j.appender.stdout.layout.ConversionPattern=%-5p %d{HH:mm:ss,SSS} %c{1.} [%t] : %m%n
#log4j.appender.stdout.layout.ConversionPattern=%-5p %c{1.} [%t] %d{HH:mm:ss,SSS}: %m%n

28
jzy3d-emul-gl/log4j.properties
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@ -1,14 +1,14 @@
# Root logger option
log4j.rootLogger=INFO, stdout
# Direct log messages to stdout
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.Target=System.out
#log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
#log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p %c{1}:%L - %m%n
#log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p %c{1} - %m%n
#log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p [%c] [%t] - %m%n
log4j.appender.stdout.layout=org.apache.log4j.EnhancedPatternLayout
log4j.appender.stdout.layout.ConversionPattern=%-5p %d{HH:mm:ss,SSS} %c{1.} [%t] : %m%n
#log4j.appender.stdout.layout.ConversionPattern=%-5p %c{1.} [%t] %d{HH:mm:ss,SSS}: %m%n
# Root logger option
log4j.rootLogger=INFO, stdout
# Direct log messages to stdout
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.Target=System.out
#log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
#log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p %c{1}:%L - %m%n
#log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p %c{1} - %m%n
#log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p [%c] [%t] - %m%n
log4j.appender.stdout.layout=org.apache.log4j.EnhancedPatternLayout
log4j.appender.stdout.layout.ConversionPattern=%-5p %d{HH:mm:ss,SSS} %c{1.} [%t] : %m%n
#log4j.appender.stdout.layout.ConversionPattern=%-5p %c{1.} [%t] %d{HH:mm:ss,SSS}: %m%n

28
jzy3d-native-jogl-awt/src/test/java/org/jzy3d/chart/log4j.properties
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@ -1,14 +1,14 @@
# Root logger option
log4j.rootLogger=INFO, WARN, stdout
# Direct log messages to stdout
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.Target=System.out
#log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
#log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p %c{1}:%L - %m%n
#log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p %c{1} - %m%n
#log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p [%c] [%t] - %m%n
log4j.appender.stdout.layout=org.apache.log4j.EnhancedPatternLayout
log4j.appender.stdout.layout.ConversionPattern=%-5p %d{HH:mm:ss,SSS} %c{1.} [%t] : %m%n
#log4j.appender.stdout.layout.ConversionPattern=%-5p %c{1.} [%t] %d{HH:mm:ss,SSS}: %m%n
# Root logger option
log4j.rootLogger=INFO, WARN, stdout
# Direct log messages to stdout
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.Target=System.out
#log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
#log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p %c{1}:%L - %m%n
#log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p %c{1} - %m%n
#log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p [%c] [%t] - %m%n
log4j.appender.stdout.layout=org.apache.log4j.EnhancedPatternLayout
log4j.appender.stdout.layout.ConversionPattern=%-5p %d{HH:mm:ss,SSS} %c{1.} [%t] : %m%n
#log4j.appender.stdout.layout.ConversionPattern=%-5p %c{1.} [%t] %d{HH:mm:ss,SSS}: %m%n

4242
jzy3d-svm-mapper/data/datasets/radial/inputs.csv
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File diff suppressed because it is too large Load Diff

2
jzy3d-svm-mapper/doc/datasources.txt
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@ -1,2 +1,2 @@
http://archive.ics.uci.edu/ml/
http://archive.ics.uci.edu/ml/
kaviar: http://trac.clermont.cemagref.fr/projets/Kaviar/wiki

42
jzy3d-svm-mapper/doc/todo.txt
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@ -1,22 +1,22 @@
Parameter hierarchy
by svm
have kernel param object
UI
parameters through interface
selection of input dataset / model
train/apply on auto grid
IO
inputs
save trained model
remain compatible with libsvm format
support csv format
Scaling facilities
progress monitor?
std out?
Parameter hierarchy
by svm
have kernel param object
UI
parameters through interface
selection of input dataset / model
train/apply on auto grid
IO
inputs
save trained model
remain compatible with libsvm format
support csv format
Scaling facilities
progress monitor?
std out?
support vectors?

228
jzy3d-svm-mapper/src/demos/org/jzy3d/svm/demos/Abstract3dDemo.java
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@ -1,114 +1,114 @@
package org.jzy3d.svm.demos;
import org.instantsvm.Parameters;
import org.instantsvm.regression.RegressionInputs;
import org.instantsvm.regression.RegressionSVM;
import org.jzy3d.chart.Chart;
import org.jzy3d.chart.ChartLauncher;
import org.jzy3d.chart.SwingChartLauncher;
import org.jzy3d.chart.factories.AWTChartFactory;
import org.jzy3d.colors.Color;
import org.jzy3d.colors.ColorMapper;
import org.jzy3d.colors.colormaps.ColorMapRainbow;
import org.jzy3d.maths.BoundingBox3d;
import org.jzy3d.maths.Coord3d;
import org.jzy3d.maths.Range;
import org.jzy3d.maths.Rectangle;
import org.jzy3d.plot3d.builder.Mapper;
import org.jzy3d.plot3d.builder.concrete.OrthonormalTessellator;
import org.jzy3d.plot3d.primitives.Scatter;
import org.jzy3d.plot3d.primitives.Shape;
import org.jzy3d.plot3d.rendering.canvas.Quality;
import org.jzy3d.svm.editors.RegressionParamsEditor;
import org.jzy3d.svm.tesselation.SvmGrid;
import org.jzy3d.svm.tesselation.SvmMapper;
import org.jzy3d.svm.utils.Conversion;
public abstract class Abstract3dDemo {
public static void openChart(Chart chart) {
ChartLauncher.openChart(chart, "Svm3D ");
}
public static void openChart(Chart chart, Parameters params) {
ChartLauncher.openChart(chart, "InstantSVM & Svm3D ");
SwingChartLauncher.openPanel(new RegressionParamsEditor(params),
new Rectangle(100, 100, 800, 600), "Regression params");
}
public static void scale(Coord3d[] pts, float xfact, float yfact, float zfact) {
for (int i = 0; i < pts.length; i++) {
pts[i].x *= xfact;
pts[i].y *= yfact;
pts[i].y *= zfact;
}
}
/****************** CHART GENERATION ********************/
public static Chart getRegressionChart(Coord3d[] values, Parameters parameters) {
SvmMapper mapper = new SvmMapper(values, parameters);
mapper.getSvm().getParameters().print();
return getRegressionChart(mapper, values);
}
public static Chart getRegressionChart(RegressionSVM svm, RegressionInputs inputs) {
Coord3d[] values = Conversion.toCoord3d(inputs);
SvmMapper mapper = new SvmMapper(svm);
return getRegressionChart(mapper, values);
}
protected static Chart getRegressionChart(SvmMapper mapper, Coord3d[] values) {
Quality q = Quality.Advanced;
q.setSmoothPoint(true);
Chart chart = new AWTChartFactory().newChart(q);
// shape
BoundingBox3d b = Conversion.getBounds(values);
System.out.println(b);
final Shape s = getRingSurface(mapper, b.getXRange(), b.getYRange(), b.getZRange(), 50);
chart.getScene().getGraph().add(s);
// scatter
loadScatter(chart, values);
return chart;
}
/* 3d OBJECTS GENERATION */
public static Scatter loadScatter(Chart chart, Coord3d[] coords) {
Scatter scatter = new Scatter(coords, Color.GREEN.mulSelf(1.2f), 20);
chart.getScene().getGraph().add(scatter);
return scatter;
}
public static Shape getRingSurface(Mapper mapper, Range xrange, Range yrange, Range zrange,
int steps) {
ColorMapper cmapper = new ColorMapper(new ColorMapRainbow(), zrange.getMin(), zrange.getMax(),
new Color(1, 1, 1, .5f));
RingInterpolator tesselator =
new RingInterpolator(0, xrange.getMax(), cmapper, new Color(1f, 1f, 1f));
SvmGrid grid = new SvmGrid(xrange, steps, yrange, steps);
Shape surface = (Shape) tesselator.build(grid.apply(mapper));
surface.setFaceDisplayed(true);
surface.setWireframeDisplayed(true);
surface.setWireframeColor(Color.BLACK);
return surface;
}
public static Shape getSquareSurface(Chart chart, Mapper mapper, Range xrange, Range yrange,
int steps) {
OrthonormalTessellator tesselator = new OrthonormalTessellator();
SvmGrid grid = new SvmGrid(xrange, steps, yrange, steps);
Shape surface = (Shape) tesselator.build(grid.apply(mapper));
surface.setColorMapper(new ColorMapper(new ColorMapRainbow(), surface.getBounds().getZmin(),
surface.getBounds().getZmax(), new Color(1, 1, 1, .5f)));
surface.setFaceDisplayed(true);
surface.setWireframeDisplayed(true);
surface.setWireframeColor(Color.BLACK);
return surface;
}
}
package org.jzy3d.svm.demos;
import org.instantsvm.Parameters;
import org.instantsvm.regression.RegressionInputs;
import org.instantsvm.regression.RegressionSVM;
import org.jzy3d.chart.Chart;
import org.jzy3d.chart.ChartLauncher;
import org.jzy3d.chart.SwingChartLauncher;
import org.jzy3d.chart.factories.AWTChartFactory;
import org.jzy3d.colors.Color;
import org.jzy3d.colors.ColorMapper;
import org.jzy3d.colors.colormaps.ColorMapRainbow;
import org.jzy3d.maths.BoundingBox3d;
import org.jzy3d.maths.Coord3d;
import org.jzy3d.maths.Range;
import org.jzy3d.maths.Rectangle;
import org.jzy3d.plot3d.builder.Mapper;
import org.jzy3d.plot3d.builder.concrete.OrthonormalTessellator;
import org.jzy3d.plot3d.primitives.Scatter;
import org.jzy3d.plot3d.primitives.Shape;
import org.jzy3d.plot3d.rendering.canvas.Quality;
import org.jzy3d.svm.editors.RegressionParamsEditor;
import org.jzy3d.svm.tesselation.SvmGrid;
import org.jzy3d.svm.tesselation.SvmMapper;
import org.jzy3d.svm.utils.Conversion;
public abstract class Abstract3dDemo {
public static void openChart(Chart chart) {
ChartLauncher.openChart(chart, "Svm3D ");
}
public static void openChart(Chart chart, Parameters params) {
ChartLauncher.openChart(chart, "InstantSVM & Svm3D ");
SwingChartLauncher.openPanel(new RegressionParamsEditor(params),
new Rectangle(100, 100, 800, 600), "Regression params");
}
public static void scale(Coord3d[] pts, float xfact, float yfact, float zfact) {
for (int i = 0; i < pts.length; i++) {
pts[i].x *= xfact;
pts[i].y *= yfact;
pts[i].y *= zfact;
}
}
/****************** CHART GENERATION ********************/
public static Chart getRegressionChart(Coord3d[] values, Parameters parameters) {
SvmMapper mapper = new SvmMapper(values, parameters);
mapper.getSvm().getParameters().print();
return getRegressionChart(mapper, values);
}
public static Chart getRegressionChart(RegressionSVM svm, RegressionInputs inputs) {
Coord3d[] values = Conversion.toCoord3d(inputs);
SvmMapper mapper = new SvmMapper(svm);
return getRegressionChart(mapper, values);
}
protected static Chart getRegressionChart(SvmMapper mapper, Coord3d[] values) {
Quality q = Quality.Advanced;
q.setSmoothPoint(true);
Chart chart = new AWTChartFactory().newChart(q);
// shape
BoundingBox3d b = Conversion.getBounds(values);
System.out.println(b);
final Shape s = getRingSurface(mapper, b.getXRange(), b.getYRange(), b.getZRange(), 50);
chart.getScene().getGraph().add(s);
// scatter
loadScatter(chart, values);
return chart;
}
/* 3d OBJECTS GENERATION */
public static Scatter loadScatter(Chart chart, Coord3d[] coords) {
Scatter scatter = new Scatter(coords, Color.GREEN.mulSelf(1.2f), 20);
chart.getScene().getGraph().add(scatter);
return scatter;
}
public static Shape getRingSurface(Mapper mapper, Range xrange, Range yrange, Range zrange,
int steps) {
ColorMapper cmapper = new ColorMapper(new ColorMapRainbow(), zrange.getMin(), zrange.getMax(),
new Color(1, 1, 1, .5f));
RingInterpolator tesselator =
new RingInterpolator(0, xrange.getMax(), cmapper, new Color(1f, 1f, 1f));
SvmGrid grid = new SvmGrid(xrange, steps, yrange, steps);
Shape surface = (Shape) tesselator.build(grid.apply(mapper));
surface.setFaceDisplayed(true);
surface.setWireframeDisplayed(true);
surface.setWireframeColor(Color.BLACK);
return surface;
}
public static Shape getSquareSurface(Chart chart, Mapper mapper, Range xrange, Range yrange,
int steps) {
OrthonormalTessellator tesselator = new OrthonormalTessellator();
SvmGrid grid = new SvmGrid(xrange, steps, yrange, steps);
Shape surface = (Shape) tesselator.build(grid.apply(mapper));
surface.setColorMapper(new ColorMapper(new ColorMapRainbow(), surface.getBounds().getZmin(),
surface.getBounds().getZmax(), new Color(1, 1, 1, .5f)));
surface.setFaceDisplayed(true);
surface.setWireframeDisplayed(true);
surface.setWireframeColor(Color.BLACK);
return surface;
}
}

98
jzy3d-svm-mapper/src/demos/org/jzy3d/svm/demos/ConeRegressionDemo.java
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@ -1,49 +1,49 @@
package org.jzy3d.svm.demos;
import java.io.IOException;
import org.instantsvm.Parameters;
import org.instantsvm.regression.RegressionInputs;
import org.instantsvm.regression.RegressionParameters;
import org.instantsvm.regression.RegressionSVM;
import org.instantsvm.utils.LazySVM;
import org.jzy3d.maths.Coord3d;
import org.jzy3d.plot3d.builder.concrete.SphereScatterGenerator;
import org.jzy3d.svm.utils.Conversion;
import libsvm.svm_parameter;
public class ConeRegressionDemo extends Abstract3dDemo {
public static void main(String[] args) throws IOException {
RegressionInputs inputs = Conversion.toRegressionInputs(getInputs());
Parameters params = getParams();
RegressionSVM svm = LazySVM.loadOrTrainRegression("generated", params, inputs);
System.out.println("Maybe reused a trained svm!");
openChart(getRegressionChart(svm, inputs));
}
public static Coord3d[] getInputs() {
return Conversion.toArray(SphereScatterGenerator.generate(new Coord3d(0, 0, 0), 1, 20, true));
}
public static Parameters getParams() {
Parameters params = new RegressionParameters(0.01, 100, 0.5);
if (false) {
params.getParam().svm_type = svm_parameter.EPSILON_SVR;
params.getParam().eps = 0.01;
params.getParam().C = 1000;
params.getParam().probability = 1;
params.getParam().gamma = 0.5;
// params.getParam().coef0 = 0.5;
// params.getParam().degree = 10;
// params.getParam().p = 0.00001;
// params.getParam().gamma = 1;
// params.getParam().shrinking = 0;
}
return params;
}
}
package org.jzy3d.svm.demos;
import java.io.IOException;
import org.instantsvm.Parameters;
import org.instantsvm.regression.RegressionInputs;
import org.instantsvm.regression.RegressionParameters;
import org.instantsvm.regression.RegressionSVM;
import org.instantsvm.utils.LazySVM;
import org.jzy3d.maths.Coord3d;
import org.jzy3d.plot3d.builder.concrete.SphereScatterGenerator;
import org.jzy3d.svm.utils.Conversion;
import libsvm.svm_parameter;
public class ConeRegressionDemo extends Abstract3dDemo {
public static void main(String[] args) throws IOException {
RegressionInputs inputs = Conversion.toRegressionInputs(getInputs());
Parameters params = getParams();
RegressionSVM svm = LazySVM.loadOrTrainRegression("generated", params, inputs);
System.out.println("Maybe reused a trained svm!");
openChart(getRegressionChart(svm, inputs));
}
public static Coord3d[] getInputs() {
return Conversion.toArray(SphereScatterGenerator.generate(new Coord3d(0, 0, 0), 1, 20, true));
}
public static Parameters getParams() {
Parameters params = new RegressionParameters(0.01, 100, 0.5);
if (false) {
params.getParam().svm_type = svm_parameter.EPSILON_SVR;
params.getParam().eps = 0.01;
params.getParam().C = 1000;
params.getParam().probability = 1;
params.getParam().gamma = 0.5;
// params.getParam().coef0 = 0.5;
// params.getParam().degree = 10;
// params.getParam().p = 0.00001;
// params.getParam().gamma = 1;
// params.getParam().shrinking = 0;
}
return params;
}
}

162
jzy3d-svm-mapper/src/demos/org/jzy3d/svm/demos/RingsRegressionDemo.java
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@ -1,81 +1,81 @@
package org.jzy3d.svm.demos;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import org.instantsvm.Parameters;
import org.instantsvm.XValResult;
import org.instantsvm.regression.RegressionInputs;
import org.instantsvm.regression.RegressionParameters;
import org.instantsvm.regression.RegressionSVM;
import org.jzy3d.maths.Array;
import org.jzy3d.maths.Coord3d;
import org.jzy3d.svm.utils.Conversion;
import libsvm.svm_parameter;
public class RingsRegressionDemo extends Abstract3dDemo {
public static void main(String[] args) throws IOException {
RegressionInputs inputs = Conversion.toRegressionInputs(getInputs());
Parameters params = getParams();
RegressionSVM svm = new RegressionSVM();
XValResult r = svm.xval(inputs.getX(), inputs.getY(), params, 3, 0, 10000, 6, 0, 5, 6);
System.out.println("Best parameters:" + r);
Array.print(r.errors);
params.getParam().C = r.bestC;
params.getParam().gamma = r.bestG;
svm.train(inputs, params);
System.out.println("Maybe reused a trained svm!");
openChart(getRegressionChart(svm, inputs));
}
public static Coord3d[] getInputs() {
double[] radius = {0.2, 0.5, 0.8};
double[] height = {0, 0.5, -0.5};
int n[] = {12, 12, 12};
return Conversion.toArray(generate(radius, height, n));
}
public static List<Coord3d> generate(double[] radius, double height[], int[] n) {
List<Coord3d> rings = new ArrayList<Coord3d>();
for (int i = 0; i < n.length; i++) {
double r = radius[i];
double h = height[i];
double t = 0;
double step = 2 * Math.PI / n[i];
while (t < (2 * Math.PI)) {
Coord3d c = new Coord3d(t, 0, r).cartesian();
c.z += h;
rings.add(c);
t += step;
}
}
return rings;
}
public static Parameters getParams() {
Parameters params = new RegressionParameters(0.01, 100, 0.5);
if (false) {
params.getParam().svm_type = svm_parameter.EPSILON_SVR;
params.getParam().eps = 0.01;
params.getParam().C = 1000;
params.getParam().probability = 1;
params.getParam().gamma = 0.5;
// params.getParam().coef0 = 0.5;
// params.getParam().degree = 10;
// params.getParam().p = 0.00001;
// params.getParam().gamma = 1;
// params.getParam().shrinking = 0;
}
return params;
}
}
package org.jzy3d.svm.demos;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import org.instantsvm.Parameters;
import org.instantsvm.XValResult;
import org.instantsvm.regression.RegressionInputs;
import org.instantsvm.regression.RegressionParameters;
import org.instantsvm.regression.RegressionSVM;
import org.jzy3d.maths.Array;
import org.jzy3d.maths.Coord3d;
import org.jzy3d.svm.utils.Conversion;
import libsvm.svm_parameter;
public class RingsRegressionDemo extends Abstract3dDemo {
public static void main(String[] args) throws IOException {
RegressionInputs inputs = Conversion.toRegressionInputs(getInputs());
Parameters params = getParams();
RegressionSVM svm = new RegressionSVM();
XValResult r = svm.xval(inputs.getX(), inputs.getY(), params, 3, 0, 10000, 6, 0, 5, 6);
System.out.println("Best parameters:" + r);
Array.print(r.errors);
params.getParam().C = r.bestC;
params.getParam().gamma = r.bestG;
svm.train(inputs, params);
System.out.println("Maybe reused a trained svm!");
openChart(getRegressionChart(svm, inputs));
}
public static Coord3d[] getInputs() {
double[] radius = {0.2, 0.5, 0.8};
double[] height = {0, 0.5, -0.5};
int n[] = {12, 12, 12};
return Conversion.toArray(generate(radius, height, n));
}
public static List<Coord3d> generate(double[] radius, double height[], int[] n) {
List<Coord3d> rings = new ArrayList<Coord3d>();
for (int i = 0; i < n.length; i++) {
double r = radius[i];
double h = height[i];
double t = 0;
double step = 2 * Math.PI / n[i];
while (t < (2 * Math.PI)) {
Coord3d c = new Coord3d(t, 0, r).cartesian();
c.z += h;
rings.add(c);
t += step;
}
}
return rings;
}
public static Parameters getParams() {
Parameters params = new RegressionParameters(0.01, 100, 0.5);
if (false) {
params.getParam().svm_type = svm_parameter.EPSILON_SVR;
params.getParam().eps = 0.01;
params.getParam().C = 1000;
params.getParam().probability = 1;
params.getParam().gamma = 0.5;
// params.getParam().coef0 = 0.5;
// params.getParam().degree = 10;
// params.getParam().p = 0.00001;
// params.getParam().gamma = 1;
// params.getParam().shrinking = 0;
}
return params;
}
}

74
jzy3d-svm-mapper/src/svm3d/org/jzy3d/svm/editors/EditorToolkit.java
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@ -1,37 +1,37 @@
package org.jzy3d.svm.editors;
import java.awt.Component;
import javax.swing.JSlider;
import javax.swing.JTextField;
public class EditorToolkit {
public static JSlider createSlider(final String title, int min, int max) {
final JSlider slider = new JSlider();
// slider.setBorder(BorderFactory.createTitledBorder(title));
Component[] c = slider.getComponents();
for (Component ci : c) {
System.out.println(ci);
}
slider.setMinimum(min);
slider.setMaximum(max);
slider.setMajorTickSpacing(20);
slider.setMinorTickSpacing(5);
slider.setPaintTicks(true);
slider.setPaintLabels(true);
/*
* slider.addChangeListener(new ChangeListener() {
*
* @Override public void stateChanged(ChangeEvent e) { System.out.println(title + ": " +
* slider.getValue()); }
*
* });
*/
return slider;
}
public static JTextField createTextField(String content) {
JTextField field = new JTextField(content);
return field;
}
}
package org.jzy3d.svm.editors;
import java.awt.Component;
import javax.swing.JSlider;
import javax.swing.JTextField;
public class EditorToolkit {
public static JSlider createSlider(final String title, int min, int max) {
final JSlider slider = new JSlider();
// slider.setBorder(BorderFactory.createTitledBorder(title));
Component[] c = slider.getComponents();
for (Component ci : c) {
System.out.println(ci);
}
slider.setMinimum(min);
slider.setMaximum(max);
slider.setMajorTickSpacing(20);
slider.setMinorTickSpacing(5);
slider.setPaintTicks(true);
slider.setPaintLabels(true);
/*
* slider.addChangeListener(new ChangeListener() {
*
* @Override public void stateChanged(ChangeEvent e) { System.out.println(title + ": " +
* slider.getValue()); }
*
* });
*/
return slider;
}
public static JTextField createTextField(String content) {
JTextField field = new JTextField(content);
return field;
}
}

82
jzy3d-svm-mapper/src/svm3d/org/jzy3d/svm/editors/RegressionParamsEditor.java
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@ -1,41 +1,41 @@
package org.jzy3d.svm.editors;
import java.awt.GridLayout;
import javax.swing.JLabel;
import javax.swing.JPanel;
import javax.swing.JTextField;
import org.instantsvm.Parameters;
public class RegressionParamsEditor extends JPanel {
public RegressionParamsEditor(Parameters params) {
setLayout(new GridLayout(3, 2));
// int min = 0;
// int max = 100;
setParameters(params);
}
public void setParameters(Parameters params) {
addField(0, "eps", Double.toString(params.getParam().eps));
addField(1, "C", Double.toString(params.getParam().C));
addField(2, "gamma", Double.toString(params.getParam().gamma));
}
protected void addField(int id, String name, String value) {
fields[id] = EditorToolkit.createTextField(value);
add(new JLabel(name));
add(fields[id]);
}
public JTextField getField(int i) {
return fields[i];
}
protected JTextField[] fields = new JTextField[4];
private static final long serialVersionUID = 3090387949522460142L;
}
package org.jzy3d.svm.editors;
import java.awt.GridLayout;
import javax.swing.JLabel;
import javax.swing.JPanel;
import javax.swing.JTextField;
import org.instantsvm.Parameters;
public class RegressionParamsEditor extends JPanel {
public RegressionParamsEditor(Parameters params) {
setLayout(new GridLayout(3, 2));
// int min = 0;
// int max = 100;
setParameters(params);
}
public void setParameters(Parameters params) {
addField(0, "eps", Double.toString(params.getParam().eps));
addField(1, "C", Double.toString(params.getParam().C));
addField(2, "gamma", Double.toString(params.getParam().gamma));
}
protected void addField(int id, String name, String value) {
fields[id] = EditorToolkit.createTextField(value);
add(new JLabel(name));
add(fields[id]);
}
public JTextField getField(int i) {
return fields[i];
}
protected JTextField[] fields = new JTextField[4];
private static final long serialVersionUID = 3090387949522460142L;
}

78
jzy3d-svm-mapper/src/svm3d/org/jzy3d/svm/tesselation/SvmGrid.java
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@ -1,39 +1,39 @@
package org.jzy3d.svm.tesselation;
import java.util.ArrayList;
import java.util.List;
import java.util.Vector;
import org.jzy3d.maths.Coord3d;
import org.jzy3d.maths.Range;
import org.jzy3d.plot3d.builder.Mapper;
import org.jzy3d.plot3d.builder.concrete.OrthonormalGrid;
import org.jzy3d.svm.utils.Conversion;
import libsvm.svm_node;
public class SvmGrid extends OrthonormalGrid {
public SvmGrid(Range xyrange, int xysteps) {
super(xyrange, xysteps);
}
public SvmGrid(Range xrange, int xsteps, Range yrange, int ysteps) {
super(xrange, xsteps, yrange, ysteps);
}
@Override
public List<Coord3d> apply(Mapper mapper) {
if (mapper instanceof SvmMapper) {
SvmMapper svmm = (SvmMapper) mapper;
Vector<svm_node[]> nodes = Conversion.toDataset(xrange, yrange, xsteps, ysteps);
double[] out = svmm.f(nodes);
List<Coord3d> output = new ArrayList<Coord3d>(xsteps * ysteps);
for (int i = 0; i < out.length; i++)
output.add(new Coord3d(nodes.get(i)[0].value, nodes.get(i)[1].value, out[i]));
return output;
}
return null;
}
}
package org.jzy3d.svm.tesselation;
import java.util.ArrayList;
import java.util.List;
import java.util.Vector;
import org.jzy3d.maths.Coord3d;
import org.jzy3d.maths.Range;
import org.jzy3d.plot3d.builder.Mapper;
import org.jzy3d.plot3d.builder.concrete.OrthonormalGrid;
import org.jzy3d.svm.utils.Conversion;
import libsvm.svm_node;
public class SvmGrid extends OrthonormalGrid {
public SvmGrid(Range xyrange, int xysteps) {
super(xyrange, xysteps);
}
public SvmGrid(Range xrange, int xsteps, Range yrange, int ysteps) {
super(xrange, xsteps, yrange, ysteps);
}
@Override
public List<Coord3d> apply(Mapper mapper) {
if (mapper instanceof SvmMapper) {
SvmMapper svmm = (SvmMapper) mapper;
Vector<svm_node[]> nodes = Conversion.toDataset(xrange, yrange, xsteps, ysteps);
double[] out = svmm.f(nodes);
List<Coord3d> output = new ArrayList<Coord3d>(xsteps * ysteps);
for (int i = 0; i < out.length; i++)
output.add(new Coord3d(nodes.get(i)[0].value, nodes.get(i)[1].value, out[i]));
return output;
}
return null;
}
}

108
jzy3d-svm-mapper/src/svm3d/org/jzy3d/svm/tesselation/SvmMapper.java
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@ -1,54 +1,54 @@
package org.jzy3d.svm.tesselation;
import java.util.Vector;
import org.instantsvm.Parameters;
import org.instantsvm.SVM;
import org.instantsvm.regression.RegressionSVM;
import org.jzy3d.maths.Coord3d;
import org.jzy3d.plot3d.builder.Mapper;
import org.jzy3d.svm.utils.Conversion;
import libsvm.svm_node;
public class SvmMapper extends Mapper {
public SvmMapper(Coord3d[] input) {
this(input, new Parameters());
}
public SvmMapper(Coord3d[] input, Parameters parameters) {
params = parameters;
svm = new SVM();
svm.train(Conversion.copyInputs(input), Conversion.copyTargets(input), params);
}
public SvmMapper(RegressionSVM svm) {
this.svm = svm;
}
public SVM getSvm() {
return svm;
}
@Override
public double f(double x, double y) {
output = svm.apply(Conversion.toDataset(Conversion.toVector(x, y)));
return output[0];
}
public double[] f(Vector<svm_node[]> nodes) {
output = svm.apply(nodes);
return output;
}
public double[] getOutput() {
return output;
}
protected double[] output;
protected Parameters params;
protected SVM svm;
}
package org.jzy3d.svm.tesselation;
import java.util.Vector;
import org.instantsvm.Parameters;
import org.instantsvm.SVM;
import org.instantsvm.regression.RegressionSVM;
import org.jzy3d.maths.Coord3d;
import org.jzy3d.plot3d.builder.Mapper;
import org.jzy3d.svm.utils.Conversion;
import libsvm.svm_node;
public class SvmMapper extends Mapper {
public SvmMapper(Coord3d[] input) {
this(input, new Parameters());
}
public SvmMapper(Coord3d[] input, Parameters parameters) {
params = parameters;
svm = new SVM();
svm.train(Conversion.copyInputs(input), Conversion.copyTargets(input), params);
}
public SvmMapper(RegressionSVM svm) {
this.svm = svm;
}
public SVM getSvm() {
return svm;
}
@Override
public double f(double x, double y) {
output = svm.apply(Conversion.toDataset(Conversion.toVector(x, y)));
return output[0];
}
public double[] f(Vector<svm_node[]> nodes) {
output = svm.apply(nodes);
return output;
}
public double[] getOutput() {
return output;
}
protected double[] output;
protected Parameters params;
protected SVM svm;
}

230
jzy3d-svm-mapper/src/svm3d/org/jzy3d/svm/utils/Conversion.java
View File

@ -1,115 +1,115 @@
package org.jzy3d.svm.utils;
import java.util.List;
import java.util.Vector;
import org.instantsvm.regression.RegressionInputs;
import org.jzy3d.maths.BoundingBox3d;
import org.jzy3d.maths.Coord3d;
import org.jzy3d.maths.Range;
import libsvm.svm_node;
/** Helpers to convert Jzy3d datatypes to libsvm datatypes */
public class Conversion extends org.instantsvm.utils.Conversion {
public static Vector<svm_node[]> copyInputs(Coord3d[] input) {
Vector<svm_node[]> inputs = new Vector<svm_node[]>(input.length);
for (int i = 0; i < input.length; i++)
inputs.add(toVector(input[i]));
return inputs;
}
public static Vector<Double> copyTargets(Coord3d[] input) {
Vector<Double> targets = new Vector<Double>(input.length);
for (int i = 0; i < input.length; i++)
targets.add((double) input[i].z);
return targets;
}
public static Vector<svm_node[]> toDataset(Range xrange, Range yrange, int xsteps, int ysteps) {
double xstep = xrange.getRange() / (double) (xsteps - 1);
double ystep = yrange.getRange() / (double) (ysteps - 1);
Vector<svm_node[]> output = new Vector<svm_node[]>(xsteps * ysteps);
for (int xi = 0; xi < xsteps; xi++) {
for (int yi = 0; yi < ysteps; yi++) {
double x = xrange.getMin() + xi * xstep;
double y = yrange.getMin() + yi * ystep;
output.add(toVector(x, y));
}
}
return output;
}
public static BoundingBox3d getBounds(Coord3d[] coords) {
BoundingBox3d box = new BoundingBox3d();
for (int i = 0; i < coords.length; i++) {
box.add(coords[i]);
}
return box;
}
public static Coord3d[] toArray(List<Coord3d> list) {
Coord3d[] points = new Coord3d[list.size()];
int k = 0;
for (Coord3d c : list) {
points[k++] = c;
}
return points;
}
public static RegressionInputs toRegressionInputs(List<Coord3d> list) {
return new RegressionInputs(toXVector(list), toYVector(list));
}
public static RegressionInputs toRegressionInputs(Coord3d[] list) {
return new RegressionInputs(toXVector(list), toYVector(list));
}
public static svm_node[] toVector(Coord3d c) {
svm_node[] vec = new svm_node[2];
vec[0] = toVector(0, c.x);
vec[1] = toVector(1, c.y);
return vec;
}
public static Vector<svm_node[]> toXVector(List<Coord3d> list) {
Vector<svm_node[]> v = new Vector<svm_node[]>(list.size());
for (Coord3d c : list)
v.add(toVector(c));
return v;
}
public static Vector<Double> toYVector(List<Coord3d> list) {
Vector<Double> v = new Vector<Double>(list.size());
for (Coord3d c : list)
v.add((double) c.z);
return v;
}
public static Vector<svm_node[]> toXVector(Coord3d[] list) {
Vector<svm_node[]> v = new Vector<svm_node[]>(list.length);
for (Coord3d c : list)
v.add(toVector(c));
return v;
}
public static Vector<Double> toYVector(Coord3d[] list) {
Vector<Double> v = new Vector<Double>(list.length);
for (Coord3d c : list)
v.add((double) c.z);
return v;
}
public static Coord3d[] toCoord3d(RegressionInputs inputs) {
Vector<svm_node[]> x = inputs.getX();
Vector<Double> y = inputs.getY();
int n = x.size();
Coord3d[] output = new Coord3d[n];
for (int i = 0; i < n; i++)
output[i] = new Coord3d(x.get(i)[0].value, x.get(i)[1].value, y.get(i));
return output;
}
}
package org.jzy3d.svm.utils;
import java.util.List;
import java.util.Vector;
import org.instantsvm.regression.RegressionInputs;
import org.jzy3d.maths.BoundingBox3d;
import org.jzy3d.maths.Coord3d;
import org.jzy3d.maths.Range;
import libsvm.svm_node;
/** Helpers to convert Jzy3d datatypes to libsvm datatypes */
public class Conversion extends org.instantsvm.utils.Conversion {
public static Vector<svm_node[]> copyInputs(Coord3d[] input) {
Vector<svm_node[]> inputs = new Vector<svm_node[]>(input.length);
for (int i = 0; i < input.length; i++)
inputs.add(toVector(input[i]));
return inputs;
}
public static Vector<Double> copyTargets(Coord3d[] input) {
Vector<Double> targets = new Vector<Double>(input.length);
for (int i = 0; i < input.length; i++)
targets.add((double) input[i].z);
return targets;
}
public static Vector<svm_node[]> toDataset(Range xrange, Range yrange, int xsteps, int ysteps) {
double xstep = xrange.getRange() / (double) (xsteps - 1);
double ystep = yrange.getRange() / (double) (ysteps - 1);
Vector<svm_node[]> output = new Vector<svm_node[]>(xsteps * ysteps);
for (int xi = 0; xi < xsteps; xi++) {
for (int yi = 0; yi < ysteps; yi++) {
double x = xrange.getMin() + xi * xstep;
double y = yrange.getMin() + yi * ystep;
output.add(toVector(x, y));
}
}
return output;
}
public static BoundingBox3d getBounds(Coord3d[] coords) {
BoundingBox3d box = new BoundingBox3d();
for (int i = 0; i < coords.length; i++) {
box.add(coords[i]);
}
return box;
}
public static Coord3d[] toArray(List<Coord3d> list) {
Coord3d[] points = new Coord3d[list.size()];
int k = 0;
for (Coord3d c : list) {
points[k++] = c;
}
return points;
}
public static RegressionInputs toRegressionInputs(List<Coord3d> list) {
return new RegressionInputs(toXVector(list), toYVector(list));
}
public static RegressionInputs toRegressionInputs(Coord3d[] list) {
return new RegressionInputs(toXVector(list), toYVector(list));
}
public static svm_node[] toVector(Coord3d c) {
svm_node[] vec = new svm_node[2];
vec[0] = toVector(0, c.x);
vec[1] = toVector(1, c.y);
return vec;
}
public static Vector<svm_node[]> toXVector(List<Coord3d> list) {
Vector<svm_node[]> v = new Vector<svm_node[]>(list.size());
for (Coord3d c : list)
v.add(toVector(c));
return v;
}
public static Vector<Double> toYVector(List<Coord3d> list) {
Vector<Double> v = new Vector<Double>(list.size());
for (Coord3d c : list)
v.add((double) c.z);
return v;
}
public static Vector<svm_node[]> toXVector(Coord3d[] list) {
Vector<svm_node[]> v = new Vector<svm_node[]>(list.length);
for (Coord3d c : list)
v.add(toVector(c));
return v;
}
public static Vector<Double> toYVector(Coord3d[] list) {
Vector<Double> v = new Vector<Double>(list.length);
for (Coord3d c : list)
v.add((double) c.z);
return v;
}
public static Coord3d[] toCoord3d(RegressionInputs inputs) {
Vector<svm_node[]> x = inputs.getX();
Vector<Double> y = inputs.getY();
int n = x.size();
Coord3d[] output = new Coord3d[n];
for (int i = 0; i < n; i++)
output[i] = new Coord3d(x.get(i)[0].value, x.get(i)[1].value, y.get(i));
return output;
}
}

4242
jzy3d-tools-libsvm/data/datasets/radial/inputs.csv
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File diff suppressed because it is too large Load Diff

2
jzy3d-tools-libsvm/doc/datasources.txt
View File

@ -1,2 +1,2 @@
http://archive.ics.uci.edu/ml/
http://archive.ics.uci.edu/ml/
kaviar: http://trac.clermont.cemagref.fr/projets/Kaviar/wiki

42
jzy3d-tools-libsvm/doc/todo.txt
View File

@ -1,22 +1,22 @@
Parameter hierarchy
by svm
have kernel param object
UI
parameters through interface
selection of input dataset / model
train/apply on auto grid
IO
inputs
save trained model
remain compatible with libsvm format
support csv format
Scaling facilities
progress monitor?
std out?
Parameter hierarchy
by svm
have kernel param object
UI
parameters through interface
selection of input dataset / model
train/apply on auto grid
IO
inputs
save trained model
remain compatible with libsvm format
support csv format
Scaling facilities
progress monitor?
std out?
support vectors?

74
jzy3d-tools-libsvm/src/demos/org/instantsvm/demos/regression/RegressionDemo.java
View File

@ -1,37 +1,37 @@
package org.instantsvm.demos.regression;
import java.io.IOException;
import org.instantsvm.regression.RegressionInputs;
import org.instantsvm.regression.RegressionParameters;
import org.instantsvm.regression.RegressionSVM;
import org.instantsvm.utils.LibSvmConsole;
import org.instantsvm.utils.LibSvmIO;
public class RegressionDemo {
public static void main(String[] args) throws IOException {
regressionTrial("bodyfat");
}
public static void regressionTrial(String name) throws IOException {
RegressionInputs inputs =
LibSvmIO.loadRegression(LibSvmIO.DIR_DATASETS + name + "/inputs.lsvm");
RegressionParameters p = new RegressionParameters();
// Train and save
RegressionSVM svm = new RegressionSVM();
svm.train(inputs, p);
svm.save(LibSvmIO.DIR_MODELS + name + "/", "model.lsvm");
svm = null;
// Reload and apply
svm = new RegressionSVM(LibSvmIO.DIR_MODELS + name + "/model.lsvm"); // TODO: does not reload
// params correctly
// Print results and parameters
LibSvmConsole.print(svm.apply(inputs.getX()));
System.out.println();
p.print();
svm.getParameters().print();
}
}
package org.instantsvm.demos.regression;
import java.io.IOException;
import org.instantsvm.regression.RegressionInputs;
import org.instantsvm.regression.RegressionParameters;
import org.instantsvm.regression.RegressionSVM;
import org.instantsvm.utils.LibSvmConsole;
import org.instantsvm.utils.LibSvmIO;
public class RegressionDemo {
public static void main(String[] args) throws IOException {
regressionTrial("bodyfat");
}
public static void regressionTrial(String name) throws IOException {
RegressionInputs inputs =
LibSvmIO.loadRegression(LibSvmIO.DIR_DATASETS + name + "/inputs.lsvm");
RegressionParameters p = new RegressionParameters();
// Train and save
RegressionSVM svm = new RegressionSVM();
svm.train(inputs, p);
svm.save(LibSvmIO.DIR_MODELS + name + "/", "model.lsvm");
svm = null;
// Reload and apply
svm = new RegressionSVM(LibSvmIO.DIR_MODELS + name + "/model.lsvm"); // TODO: does not reload
// params correctly
// Print results and parameters
LibSvmConsole.print(svm.apply(inputs.getX()));
System.out.println();
p.print();
svm.getParameters().print();
}
}

92
jzy3d-tools-libsvm/src/demos/org/instantsvm/demos/regression/XValDemo.java
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@ -1,46 +1,46 @@
package org.instantsvm.demos.regression;
import java.io.IOException;
import org.instantsvm.XValResult;
import org.instantsvm.regression.RegressionInputs;
import org.instantsvm.regression.RegressionParameters;
import org.instantsvm.regression.RegressionSVM;
import org.instantsvm.utils.LibSvmConsole;
import org.instantsvm.utils.LibSvmIO;
public class XValDemo {
public static void main(String[] args) throws IOException {
regressionTrial("radial");
}
public static void regressionTrial(String name) throws IOException {
RegressionInputs inputs = LibSvmIO.loadRegression(LibSvmIO.DIR_DATASETS + name + "/inputs.csv");
RegressionParameters p = new RegressionParameters();
scale(inputs, 1000, 1000, 1);
// Train and save
RegressionSVM svm = new RegressionSVM();
XValResult r = svm.xval(inputs.getX(), inputs.getY(), p, 3, 0, 10000, 6, 0, 5, 6);
System.out.println("Best parameters:" + r);
LibSvmConsole.print(r.errors);
/*
* svm.train(inputs, p); svm.save(LibSvmIO.DIR_MODELS + name + "/", "model.lsvm"); svm = null;
*
* // Reload and apply svm = new RegressionSVM(LibSvmIO.DIR_MODELS + name + "/model.lsvm"); //
* TODO: does not reload params correctly
*
* // Print results and parameters LibSvmConsole.print( svm.apply( inputs.getX() ) );
* System.out.println(); p.print(); svm.getParameters().print();
*/
}
public static void scale(RegressionInputs inputs, float xfact, float yfact, float zfact) {
for (int i = 0; i < inputs.getX().size(); i++) {
inputs.getX().get(i)[0].value *= xfact;
inputs.getX().get(i)[1].value *= yfact;
inputs.getY().set(i, inputs.getY().get(i) * zfact);
}
}
}
package org.instantsvm.demos.regression;
import java.io.IOException;
import org.instantsvm.XValResult;
import org.instantsvm.regression.RegressionInputs;
import org.instantsvm.regression.RegressionParameters;
import org.instantsvm.regression.RegressionSVM;
import org.instantsvm.utils.LibSvmConsole;
import org.instantsvm.utils.LibSvmIO;
public class XValDemo {
public static void main(String[] args) throws IOException {
regressionTrial("radial");
}
public static void regressionTrial(String name) throws IOException {
RegressionInputs inputs = LibSvmIO.loadRegression(LibSvmIO.DIR_DATASETS + name + "/inputs.csv");
RegressionParameters p = new RegressionParameters();
scale(inputs, 1000, 1000, 1);
// Train and save
RegressionSVM svm = new RegressionSVM();
XValResult r = svm.xval(inputs.getX(), inputs.getY(), p, 3, 0, 10000, 6, 0, 5, 6);
System.out.println("Best parameters:" + r);
LibSvmConsole.print(r.errors);
/*
* svm.train(inputs, p); svm.save(LibSvmIO.DIR_MODELS + name + "/", "model.lsvm"); svm = null;
*
* // Reload and apply svm = new RegressionSVM(LibSvmIO.DIR_MODELS + name + "/model.lsvm"); //
* TODO: does not reload params correctly
*
* // Print results and parameters LibSvmConsole.print( svm.apply( inputs.getX() ) );
* System.out.println(); p.print(); svm.getParameters().print();
*/
}
public static void scale(RegressionInputs inputs, float xfact, float yfact, float zfact) {
for (int i = 0; i < inputs.getX().size(); i++) {
inputs.getX().get(i)[0].value *= xfact;
inputs.getX().get(i)[1].value *= yfact;
inputs.getY().set(i, inputs.getY().get(i) * zfact);
}
}
}

146
jzy3d-tools-libsvm/src/instantsvm/org/instantsvm/Parameters.java
View File

@ -1,73 +1,73 @@
package org.instantsvm;
import org.instantsvm.utils.LibSvmConsole;
import libsvm.svm_parameter;
/**
* options: -s svm_type : set type of SVM (default 0) 0 -- C-SVC 1 -- nu-SVC 2 -- one-class SVM 3 --
* epsilon-SVR 4 -- nu-SVR -t kernel_type : set type of kernel function (default 2) 0 -- linear:
* u'*v 1 -- polynomial: (gamma*u'*v + coef0)^degree 2 -- radial basis function: exp(-gamma*|u-v|^2)
* 3 -- sigmoid: tanh(gamma*u'*v + coef0) 4 -- precomputed kernel (kernel values in
* training_set_file) -d degree : set degree in kernel function (default 3) -g gamma : set gamma in
* kernel function (default 1/num_features) -r coef0 : set coef0 in kernel function (default 0) -c
* cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1) -n nu : set the
* parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5) -p epsilon : set the epsilon in
* loss function of epsilon-SVR (default 0.1) -m cachesize : set cache memory size in MB (default
* 100) -e epsilon : set tolerance of termination criterion (default 0.001) -h shrinking : whether
* to use the shrinking heuristics, 0 or 1 (default 1) -b probability_estimates : whether to train a
* SVC or SVR model for probability estimates, 0 or 1 (default 0) -wi weight : set the parameter C
* of class i to weight*C, for C-SVC (default 1) -v n : n-fold cross validation mode -q : quiet mode
* (no outputs)
*/
public class Parameters {
public Parameters() {
param = new svm_parameter();
defaults();
}
public Parameters(svm_parameter param) {
this.param = param;
}
public Parameters(double eps, double C, double gamma) {
param = new svm_parameter();
defaults();
param.eps = eps;
param.C = C;
param.gamma = gamma;
}
public void setParam(svm_parameter param) {
this.param = param;
}
public svm_parameter getParam() {
return param;
}
protected void defaults() {
param.svm_type = svm_parameter.C_SVC;
param.kernel_type = svm_parameter.RBF;
param.degree = 3;
param.gamma = 0; // 1/num_features
param.coef0 = 0;
param.nu = 0.5;
param.cache_size = 100;
param.C = 1;
param.eps = 1e-3;
param.p = 0.1;
param.shrinking = 0;
param.probability = 0;
param.nr_weight = 0;
param.weight_label = new int[0];
param.weight = new double[0];
}
public void print() {
System.out.println(this.getClass().getSimpleName() + ":");
LibSvmConsole.print(param);
}
protected svm_parameter param;
}
package org.instantsvm;
import org.instantsvm.utils.LibSvmConsole;
import libsvm.svm_parameter;
/**
* options: -s svm_type : set type of SVM (default 0) 0 -- C-SVC 1 -- nu-SVC 2 -- one-class SVM 3 --
* epsilon-SVR 4 -- nu-SVR -t kernel_type : set type of kernel function (default 2) 0 -- linear:
* u'*v 1 -- polynomial: (gamma*u'*v + coef0)^degree 2 -- radial basis function: exp(-gamma*|u-v|^2)
* 3 -- sigmoid: tanh(gamma*u'*v + coef0) 4 -- precomputed kernel (kernel values in
* training_set_file) -d degree : set degree in kernel function (default 3) -g gamma : set gamma in
* kernel function (default 1/num_features) -r coef0 : set coef0 in kernel function (default 0) -c
* cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1) -n nu : set the
* parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5) -p epsilon : set the epsilon in
* loss function of epsilon-SVR (default 0.1) -m cachesize : set cache memory size in MB (default
* 100) -e epsilon : set tolerance of termination criterion (default 0.001) -h shrinking : whether
* to use the shrinking heuristics, 0 or 1 (default 1) -b probability_estimates : whether to train a
* SVC or SVR model for probability estimates, 0 or 1 (default 0) -wi weight : set the parameter C
* of class i to weight*C, for C-SVC (default 1) -v n : n-fold cross validation mode -q : quiet mode
* (no outputs)
*/
public class Parameters {
public Parameters() {
param = new svm_parameter();
defaults();
}
public Parameters(svm_parameter param) {
this.param = param;
}
public Parameters(double eps, double C, double gamma) {
param = new svm_parameter();
defaults();
param.eps = eps;
param.C = C;
param.gamma = gamma;
}
public void setParam(svm_parameter param) {
this.param = param;
}
public svm_parameter getParam() {
return param;
}
protected void defaults() {
param.svm_type = svm_parameter.C_SVC;
param.kernel_type = svm_parameter.RBF;
param.degree = 3;
param.gamma = 0; // 1/num_features
param.coef0 = 0;
param.nu = 0.5;
param.cache_size = 100;
param.C = 1;
param.eps = 1e-3;
param.p = 0.1;
param.shrinking = 0;
param.probability = 0;
param.nr_weight = 0;
param.weight_label = new int[0];
param.weight = new double[0];
}
public void print() {
System.out.println(this.getClass().getSimpleName() + ":");
LibSvmConsole.print(param);
}
protected svm_parameter param;
}

532
jzy3d-tools-libsvm/src/instantsvm/org/instantsvm/SVM.java
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@ -1,266 +1,266 @@
package org.instantsvm;
import java.io.File;
import java.io.IOException;
import java.util.Vector;
import org.instantsvm.utils.LibSvmConsole;
import org.instantsvm.utils.LibSvmIO;
import libsvm.svm;
import libsvm.svm_model;
import libsvm.svm_node;
import libsvm.svm_parameter;
import libsvm.svm_problem;
public class SVM {
public SVM() {}
public SVM(String filename) throws IOException {
this.load(filename);
}
public SVM(svm_model model) {
mountModel(model);
}
public void train(Vector<svm_node[]> vx, Vector<Double> vy) {
train(vx, vy, getDefaultParameters());
}
public void train(Vector<svm_node[]> vx, Vector<Double> vy, Parameters parameters) {
this.parameters = parameters;
this.param = parameters.getParam();
load(vx, vy);
model = svm.svm_train(prob, param);
parameters.setParam(param);
}
public XValResult xval(Vector<svm_node[]> vx, Vector<Double> vy, Parameters parameters,
int nrfold, double cMin, double cMax, int cSteps, double gMin, double gMax, int gSteps) {
this.parameters = parameters;
this.param = parameters.getParam();
load(vx, vy);
double[][] errors = new double[cSteps][gSteps];
double cRange = cMax - cMin;
double gRange = gMax - gMin;
double cStep = cRange / cSteps;
double gStep = gRange / gSteps;
double bestC = 0;
double bestG = 0;
double min = Double.POSITIVE_INFINITY;
for (int i = 0; i < cSteps; i++) {
for (int j = 0; j < gSteps; j++) {
param.C = cMin + cStep * i;
param.gamma = gMin + gStep * j;
errors[i][j] = do_cross_validation(nrfold);
if (errors[i][j] < min) {
min = errors[i][j];
bestC = param.C;
bestG = param.gamma;
}
}
}
return new XValResult(errors, bestC, bestG);
}
public double[] apply(Vector<svm_node[]> x) {
return predict(x, null, 0);
}
public Parameters getParameters() {
return parameters;
}
public svm_node[][] getSupportVectors() {
return model.SV;
}
public double[][] getCoefs() {
return model.sv_coef;
}
public void save(String directory, String filename) throws IOException {
File dir = new File(directory);
if (!dir.exists())
dir.mkdirs();
svm.svm_save_model(directory + filename, model);
}
public void save(String filename) throws IOException {
svm.svm_save_model(filename, model);
}
public void load(String filename) throws IOException {
mountModel(LibSvmIO.loadModel(filename));
}
public void print() {
System.out.println("Support vectors (" + model.SV.length + "):");
LibSvmConsole.print(model.SV);
System.out.println("Coefficients (" + model.sv_coef.length + "):");
LibSvmConsole.print(model.sv_coef);
}
public Parameters getDefaultParameters() {
return new Parameters();
}
/*******************************************************/
protected void mountModel(svm_model model) {
this.model = model;
this.parameters = new Parameters(model.param);
}
protected void load(Vector<svm_node[]> vx, Vector<Double> vy) {
int max_index = 0;
prob = new svm_problem();
prob.l = vy.size();
prob.x = new svm_node[prob.l][];
for (int i = 0; i < prob.l; i++)
prob.x[i] = vx.elementAt(i);
prob.y = new double[prob.l];
for (int i = 0; i < prob.l; i++)
prob.y[i] = vy.elementAt(i);
if (param.gamma == 0 && max_index > 0)
param.gamma = 1.0 / max_index;
if (param.kernel_type == svm_parameter.PRECOMPUTED)
for (int i = 0; i < prob.l; i++) {
if (prob.x[i][0].index != 0) {
System.err.print("Wrong kernel matrix: first column must be 0:sample_serial_number\n");
}
if ((int) prob.x[i][0].value <= 0 || (int) prob.x[i][0].value > max_index) {
System.err.print("Wrong input format: sample_serial_number out of range\n");
}
}
}
protected double[] predict(Vector<svm_node[]> x, double[] targets, int predict_probability) {
double[] output = new double[x.size()];
int correct = 0;
int total = 0;
double error = 0;
double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
int svm_type = svm.svm_get_svm_type(model);
int nr_class = svm.svm_get_nr_class(model);
double[] prob_estimates = null;
if (predict_probability == 1) {
if (svm_type == svm_parameter.EPSILON_SVR || svm_type == svm_parameter.NU_SVR) {
System.out.print(
"Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma="
+ svm.svm_get_svr_probability(model) + "\n");
} else {
int[] labels = new int[nr_class];
svm.svm_get_labels(model, labels);
prob_estimates = new double[nr_class];
// output.writeBytes("labels");
// for(int j=0;j<nr_class;j++)
// output.writeBytes(" "+labels[j]);
// output.writeBytes("\n");
}
}
for (int i = 0; i < x.size(); i++) {
double v;
if (predict_probability == 1
&& (svm_type == svm_parameter.C_SVC || svm_type == svm_parameter.NU_SVC)) {
v = svm.svm_predict_probability(model, x.get(i), prob_estimates);
// output.writeBytes(v+" ");
// for(int j=0;j<nr_class;j++)
// output.writeBytes(prob_estimates[j]+" ");
// output.writeBytes("\n");
} else {
v = svm.svm_predict(model, x.get(i));
// output.writeBytes(v+"\n");
// System.out.println(x.get(i)[0].value + " " + x.get(i)[1].value + " " + v);
}
output[i] = v;
if (targets != null) {
if (v == targets[i])
++correct;
error += (v - targets[i]) * (v - targets[i]);
sumv += v;
sumy += targets[i];
sumvv += v * v;
sumyy += targets[i] * targets[i];
sumvy += v * targets[i];
++total;
}
}
if (true) {
if (svm_type == svm_parameter.EPSILON_SVR || svm_type == svm_parameter.NU_SVR) {
System.out.print("Mean squared error = " + error / total + " (regression)\n");
System.out.print("Squared correlation coefficient = "
+ ((total * sumvy - sumv * sumy) * (total * sumvy - sumv * sumy))
/ ((total * sumvv - sumv * sumv) * (total * sumyy - sumy * sumy))
+ " (regression)\n");
} else
System.out.print("Accuracy = " + (double) correct / total * 100 + "% (" + correct + "/"
+ total + ") (classification)\n");
}
return output;
}
protected double do_cross_validation(int nr_fold) {
int i;
int total_correct = 0;
double total_error = 0;
double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
double[] target = new double[prob.l];
svm.svm_cross_validation(prob, param, nr_fold, target);
if (param.svm_type == svm_parameter.EPSILON_SVR || param.svm_type == svm_parameter.NU_SVR) {
for (i = 0; i < prob.l; i++) {
double y = prob.y[i];
double v = target[i];
total_error += (v - y) * (v - y);
sumv += v;
sumy += y;
sumvv += v * v;
sumyy += y * y;
sumvy += v * y;
}
System.out.print("Cross Validation Mean squared error = " + total_error / prob.l + "\n");
System.out.print("Cross Validation Squared correlation coefficient = "
+ ((prob.l * sumvy - sumv * sumy) * (prob.l * sumvy - sumv * sumy))
/ ((prob.l * sumvv - sumv * sumv) * (prob.l * sumyy - sumy * sumy))
+ "\n");
return total_error;
} else {
for (i = 0; i < prob.l; i++)
if (target[i] == prob.y[i])
++total_correct;
System.out.print("Cross Validation Accuracy = " + 100.0 * total_correct / prob.l + "%\n");
return 100.0 * total_correct / prob.l;
}
}
/****************************/
protected svm_parameter param;
protected svm_problem prob;
protected svm_model model;
// protected int cross_validation;
// protected int nr_fold;
protected Parameters parameters;
}
package org.instantsvm;
import java.io.File;
import java.io.IOException;
import java.util.Vector;
import org.instantsvm.utils.LibSvmConsole;
import org.instantsvm.utils.LibSvmIO;
import libsvm.svm;
import libsvm.svm_model;
import libsvm.svm_node;
import libsvm.svm_parameter;
import libsvm.svm_problem;
public class SVM {
public SVM() {}
public SVM(String filename) throws IOException {
this.load(filename);
}
public SVM(svm_model model) {
mountModel(model);
}
public void train(Vector<svm_node[]> vx, Vector<Double> vy) {
train(vx, vy, getDefaultParameters());
}
public void train(Vector<svm_node[]> vx, Vector<Double> vy, Parameters parameters) {
this.parameters = parameters;
this.param = parameters.getParam();
load(vx, vy);
model = svm.svm_train(prob, param);
parameters.setParam(param);
}
public XValResult xval(Vector<svm_node[]> vx, Vector<Double> vy, Parameters parameters,
int nrfold, double cMin, double cMax, int cSteps, double gMin, double gMax, int gSteps) {
this.parameters = parameters;
this.param = parameters.getParam();
load(vx, vy);
double[][] errors = new double[cSteps][gSteps];
double cRange = cMax - cMin;
double gRange = gMax - gMin;
double cStep = cRange / cSteps;
double gStep = gRange / gSteps;
double bestC = 0;
double bestG = 0;
double min = Double.POSITIVE_INFINITY;
for (int i = 0; i < cSteps; i++) {
for (int j = 0; j < gSteps; j++) {
param.C = cMin + cStep * i;
param.gamma = gMin + gStep * j;
errors[i][j] = do_cross_validation(nrfold);
if (errors[i][j] < min) {
min = errors[i][j];
bestC = param.C;
bestG = param.gamma;
}
}
}
return new XValResult(errors, bestC, bestG);
}
public double[] apply(Vector<svm_node[]> x) {
return predict(x, null, 0);
}
public Parameters getParameters() {
return parameters;
}
public svm_node[][] getSupportVectors() {
return model.SV;
}
public double[][] getCoefs() {
return model.sv_coef;
}
public void save(String directory, String filename) throws IOException {
File dir = new File(directory);
if (!dir.exists())
dir.mkdirs();
svm.svm_save_model(directory + filename, model);
}
public void save(String filename) throws IOException {
svm.svm_save_model(filename, model);
}
public void load(String filename) throws IOException {
mountModel(LibSvmIO.loadModel(filename));
}
public void print() {
System.out.println("Support vectors (" + model.SV.length + "):");
LibSvmConsole.print(model.SV);
System.out.println("Coefficients (" + model.sv_coef.length + "):");
LibSvmConsole.print(model.sv_coef);
}
public Parameters getDefaultParameters() {
return new Parameters();
}
/*******************************************************/
protected void mountModel(svm_model model) {
this.model = model;
this.parameters = new Parameters(model.param);
}
protected void load(Vector<svm_node[]> vx, Vector<Double> vy) {
int max_index = 0;
prob = new svm_problem();
prob.l = vy.size();
prob.x = new svm_node[prob.l][];
for (int i = 0; i < prob.l; i++)
prob.x[i] = vx.elementAt(i);
prob.y = new double[prob.l];
for (int i = 0; i < prob.l; i++)
prob.y[i] = vy.elementAt(i);
if (param.gamma == 0 && max_index > 0)
param.gamma = 1.0 / max_index;
if (param.kernel_type == svm_parameter.PRECOMPUTED)
for (int i = 0; i < prob.l; i++) {
if (prob.x[i][0].index != 0) {
System.err.print("Wrong kernel matrix: first column must be 0:sample_serial_number\n");
}
if ((int) prob.x[i][0].value <= 0 || (int) prob.x[i][0].value > max_index) {
System.err.print("Wrong input format: sample_serial_number out of range\n");
}
}
}
protected double[] predict(Vector<svm_node[]> x, double[] targets, int predict_probability) {
double[] output = new double[x.size()];
int correct = 0;
int total = 0;
double error = 0;
double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
int svm_type = svm.svm_get_svm_type(model);
int nr_class = svm.svm_get_nr_class(model);
double[] prob_estimates = null;
if (predict_probability == 1) {
if (svm_type == svm_parameter.EPSILON_SVR || svm_type == svm_parameter.NU_SVR) {
System.out.print(
"Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma="
+ svm.svm_get_svr_probability(model) + "\n");
} else {
int[] labels = new int[nr_class];
svm.svm_get_labels(model, labels);
prob_estimates = new double[nr_class];
// output.writeBytes("labels");
// for(int j=0;j<nr_class;j++)
// output.writeBytes(" "+labels[j]);
// output.writeBytes("\n");
}
}
for (int i = 0; i < x.size(); i++) {
double v;
if (predict_probability == 1
&& (svm_type == svm_parameter.C_SVC || svm_type == svm_parameter.NU_SVC)) {
v = svm.svm_predict_probability(model, x.get(i), prob_estimates);
// output.writeBytes(v+" ");
// for(int j=0;j<nr_class;j++)
// output.writeBytes(prob_estimates[j]+" ");
// output.writeBytes("\n");
} else {
v = svm.svm_predict(model, x.get(i));
// output.writeBytes(v+"\n");
// System.out.println(x.get(i)[0].value + " " + x.get(i)[1].value + " " + v);
}
output[i] = v;
if (targets != null) {
if (v == targets[i])
++correct;
error += (v - targets[i]) * (v - targets[i]);
sumv += v;
sumy += targets[i];
sumvv += v * v;
sumyy += targets[i] * targets[i];
sumvy += v * targets[i];
++total;
}
}
if (true) {
if (svm_type == svm_parameter.EPSILON_SVR || svm_type == svm_parameter.NU_SVR) {
System.out.print("Mean squared error = " + error / total + " (regression)\n");
System.out.print("Squared correlation coefficient = "
+ ((total * sumvy - sumv * sumy) * (total * sumvy - sumv * sumy))
/ ((total * sumvv - sumv * sumv) * (total * sumyy - sumy * sumy))
+ " (regression)\n");
} else
System.out.print("Accuracy = " + (double) correct / total * 100 + "% (" + correct + "/"
+ total + ") (classification)\n");
}
return output;
}
protected double do_cross_validation(int nr_fold) {
int i;
int total_correct = 0;
double total_error = 0;
double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
double[] target = new double[prob.l];
svm.svm_cross_validation(prob, param, nr_fold, target);
if (param.svm_type == svm_parameter.EPSILON_SVR || param.svm_type == svm_parameter.NU_SVR) {
for (i = 0; i < prob.l; i++) {
double y = prob.y[i];
double v = target[i];
total_error += (v - y) * (v - y);
sumv += v;
sumy += y;
sumvv += v * v;
sumyy += y * y;
sumvy += v * y;
}
System.out.print("Cross Validation Mean squared error = " + total_error / prob.l + "\n");
System.out.print("Cross Validation Squared correlation coefficient = "
+ ((prob.l * sumvy - sumv * sumy) * (prob.l * sumvy - sumv * sumy))
/ ((prob.l * sumvv - sumv * sumv) * (prob.l * sumyy - sumy * sumy))
+ "\n");
return total_error;
} else {
for (i = 0; i < prob.l; i++)
if (target[i] == prob.y[i])
++total_correct;
System.out.print("Cross Validation Accuracy = " + 100.0 * total_correct / prob.l + "%\n");
return 100.0 * total_correct / prob.l;
}
}
/****************************/
protected svm_parameter param;
protected svm_problem prob;
protected svm_model model;
// protected int cross_validation;
// protected int nr_fold;
protected Parameters parameters;
}

38
jzy3d-tools-libsvm/src/instantsvm/org/instantsvm/XValResult.java
View File

@ -1,19 +1,19 @@
package org.instantsvm;
public class XValResult {
public XValResult(double[][] errors, double bestC, double bestG) {
this.errors = errors;
this.bestC = bestC;
this.bestG = bestG;
}
@Override
public String toString() {
return "C = " + bestC + " gamma=" + bestG;
}
public double bestC;
public double bestG;
public double[][] errors;
}
package org.instantsvm;
public class XValResult {
public XValResult(double[][] errors, double bestC, double bestG) {
this.errors = errors;
this.bestC = bestC;
this.bestG = bestG;
}
@Override
public String toString() {
return "C = " + bestC + " gamma=" + bestG;
}
public double bestC;
public double bestG;
public double[][] errors;
}

110
jzy3d-tools-libsvm/src/instantsvm/org/instantsvm/regression/RegressionInputs.java
View File

@ -1,55 +1,55 @@
package org.instantsvm.regression;
import java.io.IOException;
import java.util.Vector;
import org.instantsvm.utils.LibSvmIO;
import libsvm.svm_node;
public class RegressionInputs {
public RegressionInputs() {}
public RegressionInputs(String filename) throws IOException {
load(filename);
}
public RegressionInputs(Vector<svm_node[]> x, Vector<Double> y) {
setValues(x, y);
}
public void setValues(Vector<svm_node[]> x, Vector<Double> y) {
this.x = x;
this.y = y;
}
public Vector<svm_node[]> getX() {
return x;
}
public Vector<Double> getY() {
return y;
}
public void scale(float xfact, float yfact, float zfact) {
for (int i = 0; i < x.size(); i++) {
x.get(i)[0].value *= xfact;
x.get(i)[1].value *= yfact;
if (zfact != 1)
y.set(i, y.get(i) * zfact);
}
}
/** @see {@link LibSvmIO.loadRegression(String filename). */
public void load(String filename) throws IOException {
RegressionInputs i = LibSvmIO.loadRegression(filename);
x = i.x;
y = i.y;
i = null;
}
protected Vector<svm_node[]> x;
protected Vector<Double> y;
}
package org.instantsvm.regression;
import java.io.IOException;
import java.util.Vector;
import org.instantsvm.utils.LibSvmIO;
import libsvm.svm_node;
public class RegressionInputs {
public RegressionInputs() {}
public RegressionInputs(String filename) throws IOException {
load(filename);
}
public RegressionInputs(Vector<svm_node[]> x, Vector<Double> y) {
setValues(x, y);
}
public void setValues(Vector<svm_node[]> x, Vector<Double> y) {
this.x = x;
this.y = y;
}
public Vector<svm_node[]> getX() {
return x;
}
public Vector<Double> getY() {
return y;
}
public void scale(float xfact, float yfact, float zfact) {
for (int i = 0; i < x.size(); i++) {
x.get(i)[0].value *= xfact;
x.get(i)[1].value *= yfact;
if (zfact != 1)
y.set(i, y.get(i) * zfact);
}
}
/** @see {@link LibSvmIO.loadRegression(String filename). */
public void load(String filename) throws IOException {
RegressionInputs i = LibSvmIO.loadRegression(filename);
x = i.x;
y = i.y;
i = null;
}
protected Vector<svm_node[]> x;
protected Vector<Double> y;
}

50
jzy3d-tools-libsvm/src/instantsvm/org/instantsvm/regression/RegressionParameters.java
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@ -1,25 +1,25 @@
package org.instantsvm.regression;
import org.instantsvm.Parameters;
import libsvm.svm_parameter;
public class RegressionParameters extends Parameters {
public RegressionParameters() {
super();
}
public RegressionParameters(double eps, double C, double gamma) {
super(eps, C, gamma);
}
@Override
protected void defaults() {
super.defaults();
param.svm_type = svm_parameter.NU_SVR; // epsilon-SVR, nu-SVR
param.kernel_type = 2;
param.gamma = 0.5;
param.C = 10;
param.eps = 0.1;
}
}
package org.instantsvm.regression;
import org.instantsvm.Parameters;
import libsvm.svm_parameter;
public class RegressionParameters extends Parameters {
public RegressionParameters() {
super();
}
public RegressionParameters(double eps, double C, double gamma) {
super(eps, C, gamma);
}
@Override
protected void defaults() {
super.defaults();
param.svm_type = svm_parameter.NU_SVR; // epsilon-SVR, nu-SVR
param.kernel_type = 2;
param.gamma = 0.5;
param.C = 10;
param.eps = 0.1;
}
}

70
jzy3d-tools-libsvm/src/instantsvm/org/instantsvm/regression/RegressionSVM.java
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@ -1,35 +1,35 @@
package org.instantsvm.regression;
import java.io.IOException;
import org.instantsvm.Parameters;
import org.instantsvm.SVM;
import libsvm.svm_model;
public class RegressionSVM extends SVM {
public RegressionSVM() {
super();
}
public RegressionSVM(String filename) throws IOException {
super(filename);
}
public RegressionSVM(svm_model model) {
super(model);
}
public void train(RegressionInputs inputs, Parameters parameters) {
train(inputs.getX(), inputs.getY(), parameters);
}
public void train(RegressionInputs inputs) {
train(inputs.getX(), inputs.getY());
}
@Override
public Parameters getDefaultParameters() {
return new RegressionParameters();
}
}
package org.instantsvm.regression;
import java.io.IOException;
import org.instantsvm.Parameters;
import org.instantsvm.SVM;
import libsvm.svm_model;
public class RegressionSVM extends SVM {
public RegressionSVM() {
super();
}
public RegressionSVM(String filename) throws IOException {
super(filename);
}
public RegressionSVM(svm_model model) {
super(model);
}
public void train(RegressionInputs inputs, Parameters parameters) {
train(inputs.getX(), inputs.getY(), parameters);
}
public void train(RegressionInputs inputs) {
train(inputs.getX(), inputs.getY());
}
@Override
public Parameters getDefaultParameters() {
return new RegressionParameters();
}
}

56
jzy3d-tools-libsvm/src/instantsvm/org/instantsvm/utils/Conversion.java
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@ -1,28 +1,28 @@
package org.instantsvm.utils;
import java.util.Vector;
import libsvm.svm_node;
public class Conversion {
public static Vector<svm_node[]> toDataset(svm_node[] node) {
Vector<svm_node[]> v = new Vector<svm_node[]>(1);
v.add(node);
return v;
}
public static svm_node[] toVector(double x, double y) {
svm_node[] vec = new svm_node[2];
vec[0] = toVector(0, x);
vec[1] = toVector(1, y);
return vec;
}
public static svm_node toVector(int index, double value) {
svm_node node = new svm_node();
node.index = index;
node.value = value;
return node;
}
}
package org.instantsvm.utils;
import java.util.Vector;
import libsvm.svm_node;
public class Conversion {
public static Vector<svm_node[]> toDataset(svm_node[] node) {
Vector<svm_node[]> v = new Vector<svm_node[]>(1);
v.add(node);
return v;
}
public static svm_node[] toVector(double x, double y) {
svm_node[] vec = new svm_node[2];
vec[0] = toVector(0, x);
vec[1] = toVector(1, y);
return vec;
}
public static svm_node toVector(int index, double value) {
svm_node node = new svm_node();
node.index = index;
node.value = value;
return node;
}
}

96
jzy3d-tools-libsvm/src/instantsvm/org/instantsvm/utils/LazySVM.java
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@ -1,48 +1,48 @@
package org.instantsvm.utils;
import java.io.File;
import java.io.IOException;
import org.instantsvm.Parameters;
import org.instantsvm.regression.RegressionInputs;
import org.instantsvm.regression.RegressionSVM;
/**
* LazySVM either reload an existing model, or train and save a model in InstantSVM library.
*
* @author Martin Pernollet
*/
public class LazySVM {
public static RegressionSVM loadOrTrainRegression(String name, Parameters params)
throws IOException {
return loadOrTrainRegression(name, params,
new RegressionInputs(LibSvmIO.defaultInputPath(name)));
}
public static RegressionSVM loadOrTrainRegression(String name, Parameters params,
RegressionInputs inputs) throws IOException {
String modelFile = LibSvmIO.defaultModelPath(name);
File f = new File(modelFile);
if (f.exists())
return new RegressionSVM(modelFile);
else
return trainAndSaveRegression(name, inputs);
}
public static RegressionSVM trainAndSaveRegression(String name, RegressionInputs inputs)
throws IOException {
RegressionSVM svm = new RegressionSVM();
svm.train(inputs);
svm.save(LibSvmIO.DIR_MODELS + name + "/", LibSvmIO.DEF_MODEL_NAME);
return svm;
}
public static RegressionSVM trainAndSaveRegression(String name, Parameters params,
RegressionInputs inputs) throws IOException {
RegressionSVM svm = new RegressionSVM();
svm.train(inputs, params);
svm.save(LibSvmIO.DIR_MODELS + name + "/", LibSvmIO.DEF_MODEL_NAME);
return svm;
}
}
package org.instantsvm.utils;
import java.io.File;
import java.io.IOException;
import org.instantsvm.Parameters;
import org.instantsvm.regression.RegressionInputs;
import org.instantsvm.regression.RegressionSVM;
/**
* LazySVM either reload an existing model, or train and save a model in InstantSVM library.
*
* @author Martin Pernollet
*/
public class LazySVM {
public static RegressionSVM loadOrTrainRegression(String name, Parameters params)
throws IOException {
return loadOrTrainRegression(name, params,
new RegressionInputs(LibSvmIO.defaultInputPath(name)));
}
public static RegressionSVM loadOrTrainRegression(String name, Parameters params,
RegressionInputs inputs) throws IOException {
String modelFile = LibSvmIO.defaultModelPath(name);
File f = new File(modelFile);
if (f.exists())
return new RegressionSVM(modelFile);
else
return trainAndSaveRegression(name, inputs);
}
public static RegressionSVM trainAndSaveRegression(String name, RegressionInputs inputs)
throws IOException {
RegressionSVM svm = new RegressionSVM();
svm.train(inputs);
svm.save(LibSvmIO.DIR_MODELS + name + "/", LibSvmIO.DEF_MODEL_NAME);
return svm;
}
public static RegressionSVM trainAndSaveRegression(String name, Parameters params,
RegressionInputs inputs) throws IOException {
RegressionSVM svm = new RegressionSVM();
svm.train(inputs, params);
svm.save(LibSvmIO.DIR_MODELS + name + "/", LibSvmIO.DEF_MODEL_NAME);
return svm;
}
}

212
jzy3d-tools-libsvm/src/instantsvm/org/instantsvm/utils/LibSvmConsole.java
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@ -1,106 +1,106 @@
package org.instantsvm.utils;
import libsvm.svm_node;
import libsvm.svm_parameter;
public class LibSvmConsole {
public static void print(svm_parameter param) {
System.out.println("svm_type = " + param.svm_type);
System.out.println("kernel_type = " + param.kernel_type);
System.out.println("degree = " + param.degree);
System.out.println("gamma = " + param.gamma); // 1/num_features
System.out.println("coef0 = " + param.coef0);
System.out.println("nu = " + param.nu);
System.out.println("cache_size = " + param.cache_size);
System.out.println("C = " + param.C);
System.out.println("eps = " + param.eps);
System.out.println("p = " + param.p);
System.out.println("shrinking = " + param.shrinking);
System.out.println("probability = " + param.probability);
System.out.println("nr_weight = " + param.nr_weight);
System.out.print("weight_label = ");
if (param.weight_label != null)
LibSvmConsole.print(param.weight_label);
else
System.out.println("null");
System.out.print("weight = ");
if (param.weight != null)
LibSvmConsole.print(param.weight);
else
System.out.println("null");
}
public static void print(svm_node[][] nodes, double value) {
for (int i = 0; i < nodes.length; i++)
print(nodes[i]);
System.out.println("| " + value);
}
public static void print(svm_node[][] nodes) {
for (int i = 0; i < nodes.length; i++)
println(nodes[i]);
}
public static void println(svm_node[] nodes) {
print(nodes);
System.out.println();
}
public static void print(svm_node[] nodes) {
for (int i = 0; i < nodes.length; i++)
System.out.print(nodes[i].value + " | ");
}
/*********************************************************************/
public static void print(double input[]) {
for (int i = 0; i < input.length; i++) {
System.out.print(input[i] + "|");
}
System.out.println("");
}
public static void print(float input[]) {
for (int i = 0; i < input.length; i++) {
System.out.print(input[i] + "|");
}
System.out.println("");
}
public static void print(int input[]) {
for (int i = 0; i < input.length; i++) {
System.out.print(input[i] + "|");
}
System.out.println("");
}
public static void print(double input[][]) {
for (int i = 0; i < input.length; i++) {
for (int j = 0; j < input[i].length; j++) {
System.out.print(input[i][j] + "\t");
}
System.out.println();
}
}
public static void print(float input[][]) {
for (int i = 0; i < input.length; i++) {
for (int j = 0; j < input[i].length; j++) {
System.out.print(input[i][j] + "\t");
}
System.out.println();
}
}
public static void print(int input[][]) {
for (int i = 0; i < input.length; i++) {
for (int j = 0; j < input[i].length; j++) {
System.out.print(input[i][j] + "\t");
}
System.out.println();
}
}
}
package org.instantsvm.utils;
import libsvm.svm_node;
import libsvm.svm_parameter;
public class LibSvmConsole {
public static void print(svm_parameter param) {
System.out.println("svm_type = " + param.svm_type);
System.out.println("kernel_type = " + param.kernel_type);
System.out.println("degree = " + param.degree);
System.out.println("gamma = " + param.gamma); // 1/num_features
System.out.println("coef0 = " + param.coef0);
System.out.println("nu = " + param.nu);
System.out.println("cache_size = " + param.cache_size);
System.out.println("C = " + param.C);
System.out.println("eps = " + param.eps);
System.out.println("p = " + param.p);
System.out.println("shrinking = " + param.shrinking);
System.out.println("probability = " + param.probability);
System.out.println("nr_weight = " + param.nr_weight);
System.out.print("weight_label = ");
if (param.weight_label != null)
LibSvmConsole.print(param.weight_label);
else
System.out.println("null");
System.out.print("weight = ");
if (param.weight != null)
LibSvmConsole.print(param.weight);
else
System.out.println("null");
}
public static void print(svm_node[][] nodes, double value) {
for (int i = 0; i < nodes.length; i++)
print(nodes[i]);
System.out.println("| " + value);
}
public static void print(svm_node[][] nodes) {
for (int i = 0; i < nodes.length; i++)
println(nodes[i]);
}
public static void println(svm_node[] nodes) {
print(nodes);
System.out.println();
}
public static void print(svm_node[] nodes) {
for (int i = 0; i < nodes.length; i++)
System.out.print(nodes[i].value + " | ");
}
/*********************************************************************/
public static void print(double input[]) {
for (int i = 0; i < input.length; i++) {
System.out.print(input[i] + "|");
}
System.out.println("");
}
public static void print(float input[]) {
for (int i = 0; i < input.length; i++) {
System.out.print(input[i] + "|");
}
System.out.println("");
}
public static void print(int input[]) {
for (int i = 0; i < input.length; i++) {
System.out.print(input[i] + "|");
}
System.out.println("");
}
public static void print(double input[][]) {
for (int i = 0; i < input.length; i++) {
for (int j = 0; j < input[i].length; j++) {
System.out.print(input[i][j] + "\t");
}
System.out.println();
}
}
public static void print(float input[][]) {
for (int i = 0; i < input.length; i++) {
for (int j = 0; j < input[i].length; j++) {
System.out.print(input[i][j] + "\t");
}
System.out.println();
}
}
public static void print(int input[][]) {
for (int i = 0; i < input.length; i++) {
for (int j = 0; j < input[i].length; j++) {
System.out.print(input[i][j] + "\t");
}
System.out.println();
}
}
}

264
jzy3d-tools-libsvm/src/instantsvm/org/instantsvm/utils/LibSvmIO.java
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@ -1,132 +1,132 @@
package org.instantsvm.utils;
import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.util.StringTokenizer;
import java.util.Vector;
import org.instantsvm.regression.RegressionInputs;
import au.com.bytecode.opencsv.CSVReader;
import libsvm.svm;
import libsvm.svm_model;
import libsvm.svm_node;
public class LibSvmIO {
public static final String DIR_DATASETS = "data/datasets/";
public static final String DIR_MODELS = "data/models/";
public static final String DEF_INPUT_NAME = "inputs.lsvm";
public static final String DEF_CSV_INPUT_NAME = "inputs.csv";
public static final String DEF_MODEL_NAME = "model.lsvm";
public static String defaultInputPath(String name) {
return defaultInputPath(name, false);
}
public static String defaultInputPath(String name, boolean isCsv) {
if (isCsv)
return defaultInputDirectory(name) + LibSvmIO.DEF_CSV_INPUT_NAME;
else
return defaultInputDirectory(name) + LibSvmIO.DEF_INPUT_NAME;
}
public static String defaultModelPath(String name) {
return defaultModelDirectory(name) + LibSvmIO.DEF_MODEL_NAME;
}
public static String defaultModelDirectory(String name) {
return LibSvmIO.DIR_MODELS + name + "/";
}
public static String defaultInputDirectory(String name) {
return LibSvmIO.DIR_DATASETS + name + "/";
}
/** Load CSV format if file ends with ".csv", or LIBSVM data format otherwise. */
public static RegressionInputs loadRegression(String filename) throws IOException {
if (filename.endsWith(".csv")) {
return loadRegressionCsv(filename);
} else {
return loadRegressionOriginalFormat(filename);
}
}
/**************************************************************/
public static RegressionInputs loadRegressionOriginalFormat(String filename) throws IOException {
BufferedReader fp = new BufferedReader(new FileReader(filename));
Vector<Double> vy = new Vector<Double>();
Vector<svm_node[]> vx = new Vector<svm_node[]>();
while (true) {
String line = fp.readLine();
if (line == null)
break;
StringTokenizer st = new StringTokenizer(line, " \t\n\r\f:");
// load y value
vy.addElement(atof(st.nextToken()));
// load x vector
int m = st.countTokens() / 2;
svm_node[] x = new svm_node[m];
for (int j = 0; j < m; j++) {
x[j] = new svm_node();
x[j].index = atoi(st.nextToken());
x[j].value = atof(st.nextToken());
}
vx.addElement(x);
}
fp.close();
return new RegressionInputs(vx, vy);
}
public static svm_model loadModel(String filename) throws IOException {
return svm.svm_load_model(filename);
}
protected static double atof(String s) {
double d = Double.valueOf(s).doubleValue();
if (Double.isNaN(d) || Double.isInfinite(d))
System.err.print("NaN or Infinity in input\n");
return (d);
}
protected static int atoi(String s) {
return Integer.parseInt(s);
}
/******************************************************/
public static RegressionInputs loadRegressionCsv(String filename) throws IOException {
CSVReader reader = new CSVReader(new FileReader(filename));
String[] nextLine;
Vector<svm_node[]> x = new Vector<svm_node[]>();
Vector<Double> y = new Vector<Double>();
while ((nextLine = reader.readNext()) != null) {
int m = nextLine.length;
svm_node[] xk = new svm_node[m - 1];
for (int i = 0; i < m - 1; i++)
xk[i] = Conversion.toVector(i, Double.parseDouble(nextLine[i]));
x.add(xk);
y.add(Double.parseDouble(nextLine[m - 1]));
}
reader.close();
return new RegressionInputs(x, y);
}
protected static int readNLines(String filename) throws IOException {
CSVReader reader = new CSVReader(new FileReader(filename));
int n = 0;
while (reader.readNext() != null)
n++;
reader.close();
return n;
}
}
package org.instantsvm.utils;
import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.util.StringTokenizer;
import java.util.Vector;
import org.instantsvm.regression.RegressionInputs;
import au.com.bytecode.opencsv.CSVReader;
import libsvm.svm;
import libsvm.svm_model;
import libsvm.svm_node;
public class LibSvmIO {
public static final String DIR_DATASETS = "data/datasets/";
public static final String DIR_MODELS = "data/models/";
public static final String DEF_INPUT_NAME = "inputs.lsvm";
public static final String DEF_CSV_INPUT_NAME = "inputs.csv";
public static final String DEF_MODEL_NAME = "model.lsvm";
public static String defaultInputPath(String name) {
return defaultInputPath(name, false);
}
public static String defaultInputPath(String name, boolean isCsv) {
if (isCsv)
return defaultInputDirectory(name) + LibSvmIO.DEF_CSV_INPUT_NAME;
else
return defaultInputDirectory(name) + LibSvmIO.DEF_INPUT_NAME;
}
public static String defaultModelPath(String name) {
return defaultModelDirectory(name) + LibSvmIO.DEF_MODEL_NAME;
}
public static String defaultModelDirectory(String name) {
return LibSvmIO.DIR_MODELS + name + "/";
}
public static String defaultInputDirectory(String name) {
return LibSvmIO.DIR_DATASETS + name + "/";
}
/** Load CSV format if file ends with ".csv", or LIBSVM data format otherwise. */
public static RegressionInputs loadRegression(String filename) throws IOException {
if (filename.endsWith(".csv")) {
return loadRegressionCsv(filename);
} else {
return loadRegressionOriginalFormat(filename);
}
}
/**************************************************************/
public static RegressionInputs loadRegressionOriginalFormat(String filename) throws IOException {
BufferedReader fp = new BufferedReader(new FileReader(filename));
Vector<Double> vy = new Vector<Double>();
Vector<svm_node[]> vx = new Vector<svm_node[]>();
while (true) {
String line = fp.readLine();
if (line == null)
break;
StringTokenizer st = new StringTokenizer(line, " \t\n\r\f:");
// load y value
vy.addElement(atof(st.nextToken()));
// load x vector
int m = st.countTokens() / 2;
svm_node[] x = new svm_node[m];
for (int j = 0; j < m; j++) {
x[j] = new svm_node();
x[j].index = atoi(st.nextToken());
x[j].value = atof(st.nextToken());
}
vx.addElement(x);
}
fp.close();
return new RegressionInputs(vx, vy);
}
public static svm_model loadModel(String filename) throws IOException {
return svm.svm_load_model(filename);
}
protected static double atof(String s) {
double d = Double.valueOf(s).doubleValue();
if (Double.isNaN(d) || Double.isInfinite(d))
System.err.print("NaN or Infinity in input\n");
return (d);
}
protected static int atoi(String s) {
return Integer.parseInt(s);
}
/******************************************************/
public static RegressionInputs loadRegressionCsv(String filename) throws IOException {
CSVReader reader = new CSVReader(new FileReader(filename));
String[] nextLine;
Vector<svm_node[]> x = new Vector<svm_node[]>();
Vector<Double> y = new Vector<Double>();
while ((nextLine = reader.readNext()) != null) {
int m = nextLine.length;
svm_node[] xk = new svm_node[m - 1];
for (int i = 0; i < m - 1; i++)
xk[i] = Conversion.toVector(i, Double.parseDouble(nextLine[i]));
x.add(xk);
y.add(Double.parseDouble(nextLine[m - 1]));
}
reader.close();
return new RegressionInputs(x, y);
}
protected static int readNLines(String filename) throws IOException {
CSVReader reader = new CSVReader(new FileReader(filename));
int n = 0;
while (reader.readNext() != null)
n++;
reader.close();
return n;
}
}

100
jzy3d-tutorials/src/main/java/org/jzy3d/demos/volume/BasicVolumeDemo.java
View File

@ -1,50 +1,50 @@
package org.jzy3d.demos.volume;
import java.nio.ByteBuffer;
import org.jzy3d.analysis.AWTAbstractAnalysis;
import org.jzy3d.analysis.AnalysisLauncher;
import org.jzy3d.chart.factories.AWTChartFactory;
import org.jzy3d.colors.Color;
import org.jzy3d.colors.ColorMapper;
import org.jzy3d.colors.colormaps.ColorMapRainbow;
import org.jzy3d.maths.BoundingBox3d;
import org.jzy3d.plot3d.primitives.volume.Texture3D;
import org.jzy3d.plot3d.rendering.canvas.Quality;
import com.jogamp.opengl.util.GLBuffers;
/**
*
* @author Jacok Filik
*
*/
public class BasicVolumeDemo extends AWTAbstractAnalysis {
public static void main(String[] args) throws Exception {
AnalysisLauncher.open(new BasicVolumeDemo());
}
@Override
public void init() {
ColorMapper colorMapper = new ColorMapper(new ColorMapRainbow(), 0, 1, new Color(1, 1, 1, .5f));
ByteBuffer buffer = GLBuffers.newDirectByteBuffer(10 * 10 * 10 * 4);
// make some kind of volume
for (float x = 0; x < 2; x += 0.2) {
for (float y = 0; y < 2; y += 0.2) {
for (float z = 0; z < 2; z += 0.2) {
buffer.putFloat((float) Math.sin(x * y * z));
}
}
}
Texture3D volume = new Texture3D(buffer, new int[] {10, 10, 10}, (float) 0, (float) 1,
colorMapper, new BoundingBox3d(1, 10, 1, 10, 1, 10));
// Create a chart
chart = AWTChartFactory.chart(Quality.Intermediate);
chart.getScene().getGraph().add(volume);
chart.getView();
}
}
package org.jzy3d.demos.volume;
import java.nio.ByteBuffer;
import org.jzy3d.analysis.AWTAbstractAnalysis;
import org.jzy3d.analysis.AnalysisLauncher;
import org.jzy3d.chart.factories.AWTChartFactory;
import org.jzy3d.colors.Color;
import org.jzy3d.colors.ColorMapper;
import org.jzy3d.colors.colormaps.ColorMapRainbow;
import org.jzy3d.maths.BoundingBox3d;
import org.jzy3d.plot3d.primitives.volume.Texture3D;
import org.jzy3d.plot3d.rendering.canvas.Quality;
import com.jogamp.opengl.util.GLBuffers;
/**
*
* @author Jacok Filik
*
*/
public class BasicVolumeDemo extends AWTAbstractAnalysis {
public static void main(String[] args) throws Exception {
AnalysisLauncher.open(new BasicVolumeDemo());
}
@Override
public void init() {
ColorMapper colorMapper = new ColorMapper(new ColorMapRainbow(), 0, 1, new Color(1, 1, 1, .5f));
ByteBuffer buffer = GLBuffers.newDirectByteBuffer(10 * 10 * 10 * 4);
// make some kind of volume
for (float x = 0; x < 2; x += 0.2) {
for (float y = 0; y < 2; y += 0.2) {
for (float z = 0; z < 2; z += 0.2) {
buffer.putFloat((float) Math.sin(x * y * z));
}
}
}
Texture3D volume = new Texture3D(buffer, new int[] {10, 10, 10}, (float) 0, (float) 1,
colorMapper, new BoundingBox3d(1, 10, 1, 10, 1, 10));
// Create a chart
chart = AWTChartFactory.chart(Quality.Intermediate);
chart.getScene().getGraph().add(volume);
chart.getView();
}
}

138
jzy3d-tutorials/src/main/java/org/jzy3d/demos/waterfall/BigWaterfallDemo.java
View File

@ -1,69 +1,69 @@
package org.jzy3d.demos.waterfall;
import org.jzy3d.analysis.AWTAbstractAnalysis;
import org.jzy3d.analysis.AnalysisLauncher;
import org.jzy3d.chart.factories.AWTChartFactory;
import org.jzy3d.colors.Color;
import org.jzy3d.colors.ColorMapper;
import org.jzy3d.colors.colormaps.ColorMapRainbow;
import org.jzy3d.plot3d.primitives.vbo.ShaderWaterfallDrawableVBO;
import org.jzy3d.plot3d.primitives.vbo.ShaderWaterfallVBOBuilder;
import org.jzy3d.plot3d.rendering.canvas.Quality;
/**
* Demonstrate how to use vertex and fragment shaders.
*
* @author Jacok Filik
*/
public class BigWaterfallDemo extends AWTAbstractAnalysis {
public static void main(String[] args) throws Exception {
AnalysisLauncher.open(new BigWaterfallDemo());
}
@Override
public void init() {
float[] x = new float[1000];
for (int i = 0; i < x.length; i++) {
x[i] = -3f + 6f * ((float) i / (x.length - 1));
}
float[] y = new float[100];
for (int i = 0; i < y.length; i++) {
y[i] = -3f + 2f * ((float) i / (y.length - 1));
}
float[] z = getZ(x, y);
ColorMapper colorMapper =
new ColorMapper(new ColorMapRainbow(), -2, 2, new Color(1, 1, 1, .5f));
ShaderWaterfallVBOBuilder builder = new ShaderWaterfallVBOBuilder(x, y, z,
new ColorMapper(new ColorMapRainbow(), -1, 1, new Color(1, 1, 1, .5f)));
ShaderWaterfallDrawableVBO shape = new ShaderWaterfallDrawableVBO(builder, colorMapper);
builder.earlyInitalise(shape);
// Create a chart
chart = AWTChartFactory.chart(Quality.Intermediate);
chart.getScene().getGraph().add(shape);
chart.getView();
}
private float[] getZ(float[] x, float[] y) {
float[] z = new float[x.length * y.length];
for (int i = 0; i < y.length; i++) {
for (int j = 0; j < x.length; j++) {
z[j + (x.length * i)] = (float) f((double) x[j], (double) y[i]);
}
}
return z;
}
private double f(double x, double y) {
return x * Math.sin(x * y);
}
}
package org.jzy3d.demos.waterfall;
import org.jzy3d.analysis.AWTAbstractAnalysis;
import org.jzy3d.analysis.AnalysisLauncher;
import org.jzy3d.chart.factories.AWTChartFactory;
import org.jzy3d.colors.Color;
import org.jzy3d.colors.ColorMapper;
import org.jzy3d.colors.colormaps.ColorMapRainbow;
import org.jzy3d.plot3d.primitives.vbo.ShaderWaterfallDrawableVBO;
import org.jzy3d.plot3d.primitives.vbo.ShaderWaterfallVBOBuilder;
import org.jzy3d.plot3d.rendering.canvas.Quality;
/**
* Demonstrate how to use vertex and fragment shaders.
*
* @author Jacok Filik
*/
public class BigWaterfallDemo extends AWTAbstractAnalysis {
public static void main(String[] args) throws Exception {
AnalysisLauncher.open(new BigWaterfallDemo());
}
@Override
public void init() {
float[] x = new float[1000];
for (int i = 0; i < x.length; i++) {
x[i] = -3f + 6f * ((float) i / (x.length - 1));
}
float[] y = new float[100];
for (int i = 0; i < y.length; i++) {
y[i] = -3f + 2f * ((float) i / (y.length - 1));
}
float[] z = getZ(x, y);
ColorMapper colorMapper =
new ColorMapper(new ColorMapRainbow(), -2, 2, new Color(1, 1, 1, .5f));
ShaderWaterfallVBOBuilder builder = new ShaderWaterfallVBOBuilder(x, y, z,
new ColorMapper(new ColorMapRainbow(), -1, 1, new Color(1, 1, 1, .5f)));
ShaderWaterfallDrawableVBO shape = new ShaderWaterfallDrawableVBO(builder, colorMapper);
builder.earlyInitalise(shape);
// Create a chart
chart = AWTChartFactory.chart(Quality.Intermediate);
chart.getScene().getGraph().add(shape);
chart.getView();
}
private float[] getZ(float[] x, float[] y) {
float[] z = new float[x.length * y.length];
for (int i = 0; i < y.length; i++) {
for (int j = 0; j < x.length; j++) {
z[j + (x.length * i)] = (float) f((double) x[j], (double) y[i]);
}
}
return z;
}
private double f(double x, double y) {
return x * Math.sin(x * y);
}
}