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Ricardo Montañana Gómez
2025-06-22 00:31:33 +02:00
parent a52c20d1fb
commit 4bdbcad256
110 changed files with 31991 additions and 1 deletions
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26
libsvm-3.36/java/Makefile Normal file
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.SUFFIXES: .class .java
FILES = libsvm/svm.class libsvm/svm_model.class libsvm/svm_node.class \
libsvm/svm_parameter.class libsvm/svm_problem.class \
libsvm/svm_print_interface.class \
svm_train.class svm_predict.class svm_toy.class svm_scale.class
#JAVAC = jikes
JAVAC_FLAGS = --release 11
JAVAC = javac
# JAVAC_FLAGS =
export CLASSPATH := .:$(CLASSPATH)
all: $(FILES)
jar cvf libsvm.jar *.class libsvm/*.class
.java.class:
$(JAVAC) $(JAVAC_FLAGS) $<
libsvm/svm.java: libsvm/svm.m4
m4 libsvm/svm.m4 > libsvm/svm.java
clean:
rm -f libsvm/*.class *.class *.jar libsvm/*~ *~ libsvm/svm.java
dist: clean all
rm *.class libsvm/*.class

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libsvm-3.36/java/libsvm.jar Normal file
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libsvm-3.36/java/libsvm/svm.java Normal file
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libsvm-3.36/java/libsvm/svm.m4 Normal file
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libsvm-3.36/java/libsvm/svm_model.java Normal file
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//
// svm_model
//
package libsvm;
public class svm_model implements java.io.Serializable
{
public svm_parameter param; // parameter
public int nr_class; // number of classes, = 2 in regression/one class svm
public int l; // total #SV
public svm_node[][] SV; // SVs (SV[l])
public double[][] sv_coef; // coefficients for SVs in decision functions (sv_coef[k-1][l])
public double[] rho; // constants in decision functions (rho[k*(k-1)/2])
public double[] probA; // pariwise probability information
public double[] probB;
public double[] prob_density_marks; // probability information for ONE_CLASS
public int[] sv_indices; // sv_indices[0,...,nSV-1] are values in [1,...,num_traning_data] to indicate SVs in the training set
// for classification only
public int[] label; // label of each class (label[k])
public int[] nSV; // number of SVs for each class (nSV[k])
// nSV[0] + nSV[1] + ... + nSV[k-1] = l
};

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libsvm-3.36/java/libsvm/svm_node.java Normal file
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package libsvm;
public class svm_node implements java.io.Serializable
{
public int index;
public double value;
}

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libsvm-3.36/java/libsvm/svm_parameter.java Normal file
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package libsvm;
public class svm_parameter implements Cloneable,java.io.Serializable
{
/* svm_type */
public static final int C_SVC = 0;
public static final int NU_SVC = 1;
public static final int ONE_CLASS = 2;
public static final int EPSILON_SVR = 3;
public static final int NU_SVR = 4;
/* kernel_type */
public static final int LINEAR = 0;
public static final int POLY = 1;
public static final int RBF = 2;
public static final int SIGMOID = 3;
public static final int PRECOMPUTED = 4;
public int svm_type;
public int kernel_type;
public int degree; // for poly
public double gamma; // for poly/rbf/sigmoid
public double coef0; // for poly/sigmoid
// these are for training only
public double cache_size; // in MB
public double eps; // stopping criteria
public double C; // for C_SVC, EPSILON_SVR and NU_SVR
public int nr_weight; // for C_SVC
public int[] weight_label; // for C_SVC
public double[] weight; // for C_SVC
public double nu; // for NU_SVC, ONE_CLASS, and NU_SVR
public double p; // for EPSILON_SVR
public int shrinking; // use the shrinking heuristics
public int probability; // do probability estimates
public Object clone()
{
try
{
return super.clone();
} catch (CloneNotSupportedException e)
{
return null;
}
}
}

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libsvm-3.36/java/libsvm/svm_print_interface.java Normal file
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package libsvm;
public interface svm_print_interface
{
public void print(String s);
}

7
libsvm-3.36/java/libsvm/svm_problem.java Normal file
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package libsvm;
public class svm_problem implements java.io.Serializable
{
public int l;
public double[] y;
public svm_node[][] x;
}

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libsvm-3.36/java/svm_predict.java Normal file
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import libsvm.*;
import java.io.*;
import java.util.*;
class svm_predict {
private static svm_print_interface svm_print_null = new svm_print_interface()
{
public void print(String s) {}
};
private static svm_print_interface svm_print_stdout = new svm_print_interface()
{
public void print(String s)
{
System.out.print(s);
}
};
private static svm_print_interface svm_print_string = svm_print_stdout;
static void info(String s)
{
svm_print_string.print(s);
}
private static double atof(String s)
{
return Double.valueOf(s).doubleValue();
}
private static int atoi(String s)
{
return Integer.parseInt(s);
}
private static void predict(BufferedReader input, DataOutputStream output, svm_model model, int predict_probability) throws IOException
{
int correct = 0;
int total = 0;
double error = 0;
double sump = 0, sumt = 0, sumpp = 0, sumtt = 0, sumpt = 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)
{
svm_predict.info("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 if(svm_type == svm_parameter.ONE_CLASS)
{
// nr_class = 2 for ONE_CLASS
prob_estimates = new double[nr_class];
output.writeBytes("label normal outlier\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");
}
}
while(true)
{
String line = input.readLine();
if(line == null) break;
StringTokenizer st = new StringTokenizer(line," \t\n\r\f:");
double target_label = atof(st.nextToken());
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());
}
double predict_label;
if (predict_probability==1 && (svm_type==svm_parameter.C_SVC || svm_type==svm_parameter.NU_SVC || svm_type==svm_parameter.ONE_CLASS))
{
predict_label = svm.svm_predict_probability(model,x,prob_estimates);
output.writeBytes(predict_label+" ");
for(int j=0;j<nr_class;j++)
output.writeBytes(prob_estimates[j]+" ");
output.writeBytes("\n");
}
else
{
predict_label = svm.svm_predict(model,x);
output.writeBytes(predict_label+"\n");
}
if(predict_label == target_label)
++correct;
error += (predict_label-target_label)*(predict_label-target_label);
sump += predict_label;
sumt += target_label;
sumpp += predict_label*predict_label;
sumtt += target_label*target_label;
sumpt += predict_label*target_label;
++total;
}
if(svm_type == svm_parameter.EPSILON_SVR ||
svm_type == svm_parameter.NU_SVR)
{
svm_predict.info("Mean squared error = "+error/total+" (regression)\n");
svm_predict.info("Squared correlation coefficient = "+
((total*sumpt-sump*sumt)*(total*sumpt-sump*sumt))/
((total*sumpp-sump*sump)*(total*sumtt-sumt*sumt))+
" (regression)\n");
}
else
svm_predict.info("Accuracy = "+(double)correct/total*100+
"% ("+correct+"/"+total+") (classification)\n");
}
private static void exit_with_help()
{
System.err.print("usage: svm_predict [options] test_file model_file output_file\n"
+"options:\n"
+"-b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); one-class SVM not supported yet\n"
+"-q : quiet mode (no outputs)\n");
System.exit(1);
}
public static void main(String argv[]) throws IOException
{
int i, predict_probability=0;
svm_print_string = svm_print_stdout;
// parse options
for(i=0;i<argv.length;i++)
{
if(argv[i].charAt(0) != '-') break;
++i;
switch(argv[i-1].charAt(1))
{
case 'b':
predict_probability = atoi(argv[i]);
break;
case 'q':
svm_print_string = svm_print_null;
i--;
break;
default:
System.err.print("Unknown option: " + argv[i-1] + "\n");
exit_with_help();
}
}
if(i>=argv.length-2)
exit_with_help();
try
{
BufferedReader input = new BufferedReader(new FileReader(argv[i]));
DataOutputStream output = new DataOutputStream(new BufferedOutputStream(new FileOutputStream(argv[i+2])));
svm_model model = svm.svm_load_model(argv[i+1]);
if (model == null)
{
System.err.print("can't open model file "+argv[i+1]+"\n");
System.exit(1);
}
if(predict_probability == 1)
{
if(svm.svm_check_probability_model(model)==0)
{
System.err.print("Model does not support probabiliy estimates\n");
System.exit(1);
}
}
else
{
if(svm.svm_check_probability_model(model)!=0)
{
svm_predict.info("Model supports probability estimates, but disabled in prediction.\n");
}
}
predict(input,output,model,predict_probability);
input.close();
output.close();
}
catch(FileNotFoundException e)
{
exit_with_help();
}
catch(ArrayIndexOutOfBoundsException e)
{
exit_with_help();
}
}
}

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libsvm-3.36/java/svm_scale.java Normal file
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import libsvm.*;
import java.io.*;
import java.util.*;
import java.text.DecimalFormat;
class svm_scale
{
private String line = null;
private double lower = -1.0;
private double upper = 1.0;
private double y_lower;
private double y_upper;
private boolean y_scaling = false;
private double[] feature_max;
private double[] feature_min;
private double y_max = -Double.MAX_VALUE;
private double y_min = Double.MAX_VALUE;
private int max_index;
private long num_nonzeros = 0;
private long new_num_nonzeros = 0;
private static void exit_with_help()
{
System.out.print(
"Usage: svm-scale [options] data_filename\n"
+"options:\n"
+"-l lower : x scaling lower limit (default -1)\n"
+"-u upper : x scaling upper limit (default +1)\n"
+"-y y_lower y_upper : y scaling limits (default: no y scaling)\n"
+"-s save_filename : save scaling parameters to save_filename\n"
+"-r restore_filename : restore scaling parameters from restore_filename\n"
);
System.exit(1);
}
private BufferedReader rewind(BufferedReader fp, String filename) throws IOException
{
fp.close();
return new BufferedReader(new FileReader(filename));
}
private void output_target(double value)
{
if(y_scaling)
{
if(value == y_min)
value = y_lower;
else if(value == y_max)
value = y_upper;
else
value = y_lower + (y_upper-y_lower) *
(value-y_min) / (y_max-y_min);
}
System.out.print(value + " ");
}
private void output(int index, double value)
{
/* skip single-valued attribute */
if(feature_max[index] == feature_min[index])
return;
if(value == feature_min[index])
value = lower;
else if(value == feature_max[index])
value = upper;
else
value = lower + (upper-lower) *
(value-feature_min[index])/
(feature_max[index]-feature_min[index]);
if(value != 0)
{
System.out.print(index + ":" + value + " ");
new_num_nonzeros++;
}
}
private String readline(BufferedReader fp) throws IOException
{
line = fp.readLine();
return line;
}
private void run(String []argv) throws IOException
{
int i,index;
BufferedReader fp = null, fp_restore = null;
String save_filename = null;
String restore_filename = null;
String data_filename = null;
for(i=0;i<argv.length;i++)
{
if (argv[i].charAt(0) != '-') break;
++i;
switch(argv[i-1].charAt(1))
{
case 'l': lower = Double.parseDouble(argv[i]); break;
case 'u': upper = Double.parseDouble(argv[i]); break;
case 'y':
y_lower = Double.parseDouble(argv[i]);
++i;
y_upper = Double.parseDouble(argv[i]);
y_scaling = true;
break;
case 's': save_filename = argv[i]; break;
case 'r': restore_filename = argv[i]; break;
default:
System.err.println("unknown option");
exit_with_help();
}
}
if(!(upper > lower) || (y_scaling && !(y_upper > y_lower)))
{
System.err.println("inconsistent lower/upper specification");
System.exit(1);
}
if(restore_filename != null && save_filename != null)
{
System.err.println("cannot use -r and -s simultaneously");
System.exit(1);
}
if(argv.length != i+1)
exit_with_help();
data_filename = argv[i];
try {
fp = new BufferedReader(new FileReader(data_filename));
} catch (Exception e) {
System.err.println("can't open file " + data_filename);
System.exit(1);
}
/* assumption: min index of attributes is 1 */
/* pass 1: find out max index of attributes */
max_index = 0;
if(restore_filename != null)
{
int idx, c;
try {
fp_restore = new BufferedReader(new FileReader(restore_filename));
}
catch (Exception e) {
System.err.println("can't open file " + restore_filename);
System.exit(1);
}
if((c = fp_restore.read()) == 'y')
{
fp_restore.readLine();
fp_restore.readLine();
fp_restore.readLine();
}
fp_restore.readLine();
fp_restore.readLine();
String restore_line = null;
while((restore_line = fp_restore.readLine())!=null)
{
StringTokenizer st2 = new StringTokenizer(restore_line);
idx = Integer.parseInt(st2.nextToken());
max_index = Math.max(max_index, idx);
}
fp_restore = rewind(fp_restore, restore_filename);
}
while (readline(fp) != null)
{
StringTokenizer st = new StringTokenizer(line," \t\n\r\f:");
st.nextToken();
while(st.hasMoreTokens())
{
index = Integer.parseInt(st.nextToken());
max_index = Math.max(max_index, index);
st.nextToken();
num_nonzeros++;
}
}
try {
feature_max = new double[(max_index+1)];
feature_min = new double[(max_index+1)];
} catch(OutOfMemoryError e) {
System.err.println("can't allocate enough memory");
System.exit(1);
}
for(i=0;i<=max_index;i++)
{
feature_max[i] = -Double.MAX_VALUE;
feature_min[i] = Double.MAX_VALUE;
}
fp = rewind(fp, data_filename);
/* pass 2: find out min/max value */
while(readline(fp) != null)
{
int next_index = 1;
double target;
double value;
StringTokenizer st = new StringTokenizer(line," \t\n\r\f:");
target = Double.parseDouble(st.nextToken());
y_max = Math.max(y_max, target);
y_min = Math.min(y_min, target);
while (st.hasMoreTokens())
{
index = Integer.parseInt(st.nextToken());
value = Double.parseDouble(st.nextToken());
for (i = next_index; i<index; i++)
{
feature_max[i] = Math.max(feature_max[i], 0);
feature_min[i] = Math.min(feature_min[i], 0);
}
feature_max[index] = Math.max(feature_max[index], value);
feature_min[index] = Math.min(feature_min[index], value);
next_index = index + 1;
}
for(i=next_index;i<=max_index;i++)
{
feature_max[i] = Math.max(feature_max[i], 0);
feature_min[i] = Math.min(feature_min[i], 0);
}
}
fp = rewind(fp, data_filename);
/* pass 2.5: save/restore feature_min/feature_max */
if(restore_filename != null)
{
// fp_restore rewinded in finding max_index
int idx, c;
double fmin, fmax;
fp_restore.mark(2); // for reset
if((c = fp_restore.read()) == 'y')
{
fp_restore.readLine(); // pass the '\n' after 'y'
StringTokenizer st = new StringTokenizer(fp_restore.readLine());
y_lower = Double.parseDouble(st.nextToken());
y_upper = Double.parseDouble(st.nextToken());
st = new StringTokenizer(fp_restore.readLine());
y_min = Double.parseDouble(st.nextToken());
y_max = Double.parseDouble(st.nextToken());
y_scaling = true;
}
else
fp_restore.reset();
if(fp_restore.read() == 'x') {
fp_restore.readLine(); // pass the '\n' after 'x'
StringTokenizer st = new StringTokenizer(fp_restore.readLine());
lower = Double.parseDouble(st.nextToken());
upper = Double.parseDouble(st.nextToken());
String restore_line = null;
while((restore_line = fp_restore.readLine())!=null)
{
StringTokenizer st2 = new StringTokenizer(restore_line);
idx = Integer.parseInt(st2.nextToken());
fmin = Double.parseDouble(st2.nextToken());
fmax = Double.parseDouble(st2.nextToken());
if (idx <= max_index)
{
feature_min[idx] = fmin;
feature_max[idx] = fmax;
}
}
}
fp_restore.close();
}
if(save_filename != null)
{
Formatter formatter = new Formatter(new StringBuilder());
BufferedWriter fp_save = null;
try {
fp_save = new BufferedWriter(new FileWriter(save_filename));
} catch(IOException e) {
System.err.println("can't open file " + save_filename);
System.exit(1);
}
if(y_scaling)
{
formatter.format("y\n");
formatter.format("%.16g %.16g\n", y_lower, y_upper);
formatter.format("%.16g %.16g\n", y_min, y_max);
}
formatter.format("x\n");
formatter.format("%.16g %.16g\n", lower, upper);
for(i=1;i<=max_index;i++)
{
if(feature_min[i] != feature_max[i])
formatter.format("%d %.16g %.16g\n", i, feature_min[i], feature_max[i]);
}
fp_save.write(formatter.toString());
fp_save.close();
}
/* pass 3: scale */
while(readline(fp) != null)
{
int next_index = 1;
double target;
double value;
StringTokenizer st = new StringTokenizer(line," \t\n\r\f:");
target = Double.parseDouble(st.nextToken());
output_target(target);
while(st.hasMoreElements())
{
index = Integer.parseInt(st.nextToken());
value = Double.parseDouble(st.nextToken());
for (i = next_index; i<index; i++)
output(i, 0);
output(index, value);
next_index = index + 1;
}
for(i=next_index;i<= max_index;i++)
output(i, 0);
System.out.print("\n");
}
if (new_num_nonzeros > num_nonzeros)
System.err.print(
"WARNING: original #nonzeros " + num_nonzeros+"\n"
+" new #nonzeros " + new_num_nonzeros+"\n"
+"Use -l 0 if many original feature values are zeros\n");
fp.close();
}
public static void main(String argv[]) throws IOException
{
svm_scale s = new svm_scale();
s.run(argv);
}
}

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libsvm-3.36/java/svm_toy.java Normal file
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import libsvm.*;
import java.awt.*;
import java.util.*;
import java.awt.event.*;
import java.io.*;
public class svm_toy {
public static void main(String[] args) {
svm_toy_frame frame = new svm_toy_frame("svm_toy", 500, 500+50);
}
}
class svm_toy_frame extends Frame {
static final String DEFAULT_PARAM="-t 2 -c 100";
int XLEN;
int YLEN;
// off-screen buffer
Image buffer;
Graphics buffer_gc;
// pre-allocated colors
final static Color colors[] =
{
new Color(0,0,0),
new Color(0,120,120),
new Color(120,120,0),
new Color(120,0,120),
new Color(0,200,200),
new Color(200,200,0),
new Color(200,0,200)
};
class point {
point(double x, double y, byte value)
{
this.x = x;
this.y = y;
this.value = value;
}
double x, y;
byte value;
}
Vector<point> point_list = new Vector<point>();
byte current_value = 1;
svm_toy_frame(String title, int width, int height)
{
super(title);
this.addWindowListener(new WindowAdapter() {
public void windowClosing(WindowEvent e) {
System.exit(0);
}
});
this.init();
this.setSize(width, height);
XLEN = width;
YLEN = height-50;
this.clear_all();
this.setVisible(true);
}
void init()
{
final Button button_change = new Button("Change");
Button button_run = new Button("Run");
Button button_clear = new Button("Clear");
Button button_save = new Button("Save");
Button button_load = new Button("Load");
final TextField input_line = new TextField(DEFAULT_PARAM);
BorderLayout layout = new BorderLayout();
this.setLayout(layout);
Panel p = new Panel();
GridBagLayout gridbag = new GridBagLayout();
p.setLayout(gridbag);
GridBagConstraints c = new GridBagConstraints();
c.fill = GridBagConstraints.HORIZONTAL;
c.weightx = 1;
c.gridwidth = 1;
gridbag.setConstraints(button_change,c);
gridbag.setConstraints(button_run,c);
gridbag.setConstraints(button_clear,c);
gridbag.setConstraints(button_save,c);
gridbag.setConstraints(button_load,c);
c.weightx = 5;
c.gridwidth = 5;
gridbag.setConstraints(input_line,c);
button_change.setBackground(colors[current_value]);
p.add(button_change);
p.add(button_run);
p.add(button_clear);
p.add(button_save);
p.add(button_load);
p.add(input_line);
this.add(p,BorderLayout.SOUTH);
button_change.addActionListener(new ActionListener()
{ public void actionPerformed (ActionEvent e)
{ button_change_clicked(); button_change.setBackground(colors[current_value]); }});
button_run.addActionListener(new ActionListener()
{ public void actionPerformed (ActionEvent e)
{ button_run_clicked(input_line.getText()); }});
button_clear.addActionListener(new ActionListener()
{ public void actionPerformed (ActionEvent e)
{ button_clear_clicked(); }});
button_save.addActionListener(new ActionListener()
{ public void actionPerformed (ActionEvent e)
{ button_save_clicked(input_line.getText()); }});
button_load.addActionListener(new ActionListener()
{ public void actionPerformed (ActionEvent e)
{ button_load_clicked(); }});
input_line.addActionListener(new ActionListener()
{ public void actionPerformed (ActionEvent e)
{ button_run_clicked(input_line.getText()); }});
this.enableEvents(AWTEvent.MOUSE_EVENT_MASK);
}
void draw_point(point p)
{
Color c = colors[p.value+3];
Graphics window_gc = getGraphics();
buffer_gc.setColor(c);
buffer_gc.fillRect((int)(p.x*XLEN),(int)(p.y*YLEN),4,4);
window_gc.setColor(c);
window_gc.fillRect((int)(p.x*XLEN),(int)(p.y*YLEN),4,4);
}
void clear_all()
{
point_list.removeAllElements();
if(buffer != null)
{
buffer_gc.setColor(colors[0]);
buffer_gc.fillRect(0,0,XLEN,YLEN);
}
repaint();
}
void draw_all_points()
{
int n = point_list.size();
for(int i=0;i<n;i++)
draw_point(point_list.elementAt(i));
}
void button_change_clicked()
{
++current_value;
if(current_value > 3) current_value = 1;
}
private static double atof(String s)
{
return Double.valueOf(s).doubleValue();
}
private static int atoi(String s)
{
return Integer.parseInt(s);
}
void button_run_clicked(String args)
{
// guard
if(point_list.isEmpty()) return;
svm_parameter param = new svm_parameter();
// default values
param.svm_type = svm_parameter.C_SVC;
param.kernel_type = svm_parameter.RBF;
param.degree = 3;
param.gamma = 0;
param.coef0 = 0;
param.nu = 0.5;
param.cache_size = 40;
param.C = 1;
param.eps = 1e-3;
param.p = 0.1;
param.shrinking = 1;
param.probability = 0;
param.nr_weight = 0;
param.weight_label = new int[0];
param.weight = new double[0];
// parse options
StringTokenizer st = new StringTokenizer(args);
String[] argv = new String[st.countTokens()];
for(int i=0;i<argv.length;i++)
argv[i] = st.nextToken();
for(int i=0;i<argv.length;i++)
{
if(argv[i].charAt(0) != '-') break;
if(++i>=argv.length)
{
System.err.print("unknown option\n");
break;
}
switch(argv[i-1].charAt(1))
{
case 's':
param.svm_type = atoi(argv[i]);
break;
case 't':
param.kernel_type = atoi(argv[i]);
break;
case 'd':
param.degree = atoi(argv[i]);
break;
case 'g':
param.gamma = atof(argv[i]);
break;
case 'r':
param.coef0 = atof(argv[i]);
break;
case 'n':
param.nu = atof(argv[i]);
break;
case 'm':
param.cache_size = atof(argv[i]);
break;
case 'c':
param.C = atof(argv[i]);
break;
case 'e':
param.eps = atof(argv[i]);
break;
case 'p':
param.p = atof(argv[i]);
break;
case 'h':
param.shrinking = atoi(argv[i]);
break;
case 'b':
param.probability = atoi(argv[i]);
break;
case 'w':
++param.nr_weight;
{
int[] old = param.weight_label;
param.weight_label = new int[param.nr_weight];
System.arraycopy(old,0,param.weight_label,0,param.nr_weight-1);
}
{
double[] old = param.weight;
param.weight = new double[param.nr_weight];
System.arraycopy(old,0,param.weight,0,param.nr_weight-1);
}
param.weight_label[param.nr_weight-1] = atoi(argv[i-1].substring(2));
param.weight[param.nr_weight-1] = atof(argv[i]);
break;
default:
System.err.print("unknown option\n");
}
}
// build problem
svm_problem prob = new svm_problem();
prob.l = point_list.size();
prob.y = new double[prob.l];
if(param.kernel_type == svm_parameter.PRECOMPUTED)
{
}
else if(param.svm_type == svm_parameter.EPSILON_SVR ||
param.svm_type == svm_parameter.NU_SVR)
{
if(param.gamma == 0) param.gamma = 1;
prob.x = new svm_node[prob.l][1];
for(int i=0;i<prob.l;i++)
{
point p = point_list.elementAt(i);
prob.x[i][0] = new svm_node();
prob.x[i][0].index = 1;
prob.x[i][0].value = p.x;
prob.y[i] = p.y;
}
// build model & classify
svm_model model = svm.svm_train(prob, param);
svm_node[] x = new svm_node[1];
x[0] = new svm_node();
x[0].index = 1;
int[] j = new int[XLEN];
Graphics window_gc = getGraphics();
for (int i = 0; i < XLEN; i++)
{
x[0].value = (double) i / XLEN;
j[i] = (int)(YLEN*svm.svm_predict(model, x));
}
buffer_gc.setColor(colors[0]);
buffer_gc.drawLine(0,0,0,YLEN-1);
window_gc.setColor(colors[0]);
window_gc.drawLine(0,0,0,YLEN-1);
int p = (int)(param.p * YLEN);
for(int i=1;i<XLEN;i++)
{
buffer_gc.setColor(colors[0]);
buffer_gc.drawLine(i,0,i,YLEN-1);
window_gc.setColor(colors[0]);
window_gc.drawLine(i,0,i,YLEN-1);
buffer_gc.setColor(colors[5]);
window_gc.setColor(colors[5]);
buffer_gc.drawLine(i-1,j[i-1],i,j[i]);
window_gc.drawLine(i-1,j[i-1],i,j[i]);
if(param.svm_type == svm_parameter.EPSILON_SVR)
{
buffer_gc.setColor(colors[2]);
window_gc.setColor(colors[2]);
buffer_gc.drawLine(i-1,j[i-1]+p,i,j[i]+p);
window_gc.drawLine(i-1,j[i-1]+p,i,j[i]+p);
buffer_gc.setColor(colors[2]);
window_gc.setColor(colors[2]);
buffer_gc.drawLine(i-1,j[i-1]-p,i,j[i]-p);
window_gc.drawLine(i-1,j[i-1]-p,i,j[i]-p);
}
}
}
else
{
if(param.gamma == 0) param.gamma = 0.5;
prob.x = new svm_node [prob.l][2];
for(int i=0;i<prob.l;i++)
{
point p = point_list.elementAt(i);
prob.x[i][0] = new svm_node();
prob.x[i][0].index = 1;
prob.x[i][0].value = p.x;
prob.x[i][1] = new svm_node();
prob.x[i][1].index = 2;
prob.x[i][1].value = p.y;
prob.y[i] = p.value;
}
// build model & classify
svm_model model = svm.svm_train(prob, param);
svm_node[] x = new svm_node[2];
x[0] = new svm_node();
x[1] = new svm_node();
x[0].index = 1;
x[1].index = 2;
Graphics window_gc = getGraphics();
for (int i = 0; i < XLEN; i++)
for (int j = 0; j < YLEN ; j++) {
x[0].value = (double) i / XLEN;
x[1].value = (double) j / YLEN;
double d = svm.svm_predict(model, x);
if (param.svm_type == svm_parameter.ONE_CLASS && d<0) d=2;
buffer_gc.setColor(colors[(int)d]);
window_gc.setColor(colors[(int)d]);
buffer_gc.drawLine(i,j,i,j);
window_gc.drawLine(i,j,i,j);
}
}
draw_all_points();
}
void button_clear_clicked()
{
clear_all();
}
void button_save_clicked(String args)
{
FileDialog dialog = new FileDialog(new Frame(),"Save",FileDialog.SAVE);
dialog.setVisible(true);
String filename = dialog.getDirectory() + dialog.getFile();
if (filename == null) return;
try {
DataOutputStream fp = new DataOutputStream(new BufferedOutputStream(new FileOutputStream(filename)));
int svm_type = svm_parameter.C_SVC;
int svm_type_idx = args.indexOf("-s ");
if(svm_type_idx != -1)
{
StringTokenizer svm_str_st = new StringTokenizer(args.substring(svm_type_idx+2).trim());
svm_type = atoi(svm_str_st.nextToken());
}
int n = point_list.size();
if(svm_type == svm_parameter.EPSILON_SVR || svm_type == svm_parameter.NU_SVR)
{
for(int i=0;i<n;i++)
{
point p = point_list.elementAt(i);
fp.writeBytes(p.y+" 1:"+p.x+"\n");
}
}
else
{
for(int i=0;i<n;i++)
{
point p = point_list.elementAt(i);
fp.writeBytes(p.value+" 1:"+p.x+" 2:"+p.y+"\n");
}
}
fp.close();
} catch (IOException e) { System.err.print(e); }
}
void button_load_clicked()
{
FileDialog dialog = new FileDialog(new Frame(),"Load",FileDialog.LOAD);
dialog.setVisible(true);
String filename = dialog.getDirectory() + dialog.getFile();
if (filename == null) return;
clear_all();
try {
BufferedReader fp = new BufferedReader(new FileReader(filename));
String line;
while((line = fp.readLine()) != null)
{
StringTokenizer st = new StringTokenizer(line," \t\n\r\f:");
if(st.countTokens() == 5)
{
byte value = (byte)atoi(st.nextToken());
st.nextToken();
double x = atof(st.nextToken());
st.nextToken();
double y = atof(st.nextToken());
point_list.addElement(new point(x,y,value));
}
else if(st.countTokens() == 3)
{
double y = atof(st.nextToken());
st.nextToken();
double x = atof(st.nextToken());
point_list.addElement(new point(x,y,current_value));
}else
break;
}
fp.close();
} catch (IOException e) { System.err.print(e); }
draw_all_points();
}
protected void processMouseEvent(MouseEvent e)
{
if(e.getID() == MouseEvent.MOUSE_PRESSED)
{
if(e.getX() >= XLEN || e.getY() >= YLEN) return;
point p = new point((double)e.getX()/XLEN,
(double)e.getY()/YLEN,
current_value);
point_list.addElement(p);
draw_point(p);
}
}
public void paint(Graphics g)
{
// create buffer first time
if(buffer == null) {
buffer = this.createImage(XLEN,YLEN);
buffer_gc = buffer.getGraphics();
buffer_gc.setColor(colors[0]);
buffer_gc.fillRect(0,0,XLEN,YLEN);
}
g.drawImage(buffer,0,0,this);
}
}

318
libsvm-3.36/java/svm_train.java Normal file
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import libsvm.*;
import java.io.*;
import java.util.*;
class svm_train {
private svm_parameter param; // set by parse_command_line
private svm_problem prob; // set by read_problem
private svm_model model;
private String input_file_name; // set by parse_command_line
private String model_file_name; // set by parse_command_line
private String error_msg;
private int cross_validation;
private int nr_fold;
private static svm_print_interface svm_print_null = new svm_print_interface()
{
public void print(String s) {}
};
private static void exit_with_help()
{
System.out.print(
"Usage: svm_train [options] training_set_file [model_file]\n"
+"options:\n"
+"-s svm_type : set type of SVM (default 0)\n"
+" 0 -- C-SVC (multi-class classification)\n"
+" 1 -- nu-SVC (multi-class classification)\n"
+" 2 -- one-class SVM\n"
+" 3 -- epsilon-SVR (regression)\n"
+" 4 -- nu-SVR (regression)\n"
+"-t kernel_type : set type of kernel function (default 2)\n"
+" 0 -- linear: u'*v\n"
+" 1 -- polynomial: (gamma*u'*v + coef0)^degree\n"
+" 2 -- radial basis function: exp(-gamma*|u-v|^2)\n"
+" 3 -- sigmoid: tanh(gamma*u'*v + coef0)\n"
+" 4 -- precomputed kernel (kernel values in training_set_file)\n"
+"-d degree : set degree in kernel function (default 3)\n"
+"-g gamma : set gamma in kernel function (default 1/num_features)\n"
+"-r coef0 : set coef0 in kernel function (default 0)\n"
+"-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)\n"
+"-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)\n"
+"-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)\n"
+"-m cachesize : set cache memory size in MB (default 100)\n"
+"-e epsilon : set tolerance of termination criterion (default 0.001)\n"
+"-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)\n"
+"-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)\n"
+"-wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)\n"
+"-v n : n-fold cross validation mode\n"
+"-q : quiet mode (no outputs)\n"
);
System.exit(1);
}
private void do_cross_validation()
{
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"
);
}
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");
}
}
private void run(String argv[]) throws IOException
{
parse_command_line(argv);
read_problem();
error_msg = svm.svm_check_parameter(prob,param);
if(error_msg != null)
{
System.err.print("ERROR: "+error_msg+"\n");
System.exit(1);
}
if(cross_validation != 0)
{
do_cross_validation();
}
else
{
model = svm.svm_train(prob,param);
svm.svm_save_model(model_file_name,model);
}
}
public static void main(String argv[]) throws IOException
{
svm_train t = new svm_train();
t.run(argv);
}
private 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");
System.exit(1);
}
return(d);
}
private static int atoi(String s)
{
return Integer.parseInt(s);
}
private void parse_command_line(String argv[])
{
int i;
svm_print_interface print_func = null; // default printing to stdout
param = new svm_parameter();
// default values
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 = 1;
param.probability = 0;
param.nr_weight = 0;
param.weight_label = new int[0];
param.weight = new double[0];
cross_validation = 0;
// parse options
for(i=0;i<argv.length;i++)
{
if(argv[i].charAt(0) != '-') break;
if(++i>=argv.length)
exit_with_help();
switch(argv[i-1].charAt(1))
{
case 's':
param.svm_type = atoi(argv[i]);
break;
case 't':
param.kernel_type = atoi(argv[i]);
break;
case 'd':
param.degree = atoi(argv[i]);
break;
case 'g':
param.gamma = atof(argv[i]);
break;
case 'r':
param.coef0 = atof(argv[i]);
break;
case 'n':
param.nu = atof(argv[i]);
break;
case 'm':
param.cache_size = atof(argv[i]);
break;
case 'c':
param.C = atof(argv[i]);
break;
case 'e':
param.eps = atof(argv[i]);
break;
case 'p':
param.p = atof(argv[i]);
break;
case 'h':
param.shrinking = atoi(argv[i]);
break;
case 'b':
param.probability = atoi(argv[i]);
break;
case 'q':
print_func = svm_print_null;
i--;
break;
case 'v':
cross_validation = 1;
nr_fold = atoi(argv[i]);
if(nr_fold < 2)
{
System.err.print("n-fold cross validation: n must >= 2\n");
exit_with_help();
}
break;
case 'w':
++param.nr_weight;
{
int[] old = param.weight_label;
param.weight_label = new int[param.nr_weight];
System.arraycopy(old,0,param.weight_label,0,param.nr_weight-1);
}
{
double[] old = param.weight;
param.weight = new double[param.nr_weight];
System.arraycopy(old,0,param.weight,0,param.nr_weight-1);
}
param.weight_label[param.nr_weight-1] = atoi(argv[i-1].substring(2));
param.weight[param.nr_weight-1] = atof(argv[i]);
break;
default:
System.err.print("Unknown option: " + argv[i-1] + "\n");
exit_with_help();
}
}
svm.svm_set_print_string_function(print_func);
// determine filenames
if(i>=argv.length)
exit_with_help();
input_file_name = argv[i];
if(i<argv.length-1)
model_file_name = argv[i+1];
else
{
int p = argv[i].lastIndexOf('/');
++p; // whew...
model_file_name = argv[i].substring(p)+".model";
}
}
// read in a problem (in svmlight format)
private void read_problem() throws IOException
{
BufferedReader fp = new BufferedReader(new FileReader(input_file_name));
Vector<Double> vy = new Vector<Double>();
Vector<svm_node[]> vx = new Vector<svm_node[]>();
int max_index = 0;
while(true)
{
String line = fp.readLine();
if(line == null) break;
StringTokenizer st = new StringTokenizer(line," \t\n\r\f:");
vy.addElement(atof(st.nextToken()));
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());
}
if(m>0) max_index = Math.max(max_index, x[m-1].index);
vx.addElement(x);
}
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");
System.exit(1);
}
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");
System.exit(1);
}
}
fp.close();
}
}