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@@ -0,0 +1,2878 @@
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+package opennlp.tools.svm.libsvm;
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+
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+import java.io.*;
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+import java.util.Random;
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+import java.util.StringTokenizer;
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+
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+
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+//
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+// Kernel Cache
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+//
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+// l is the number of total data items
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+// size is the cache size limit in bytes
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+//
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+class Cache {
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+ private final int l;
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+ private long size;
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+ private static final class head_t
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+ {
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+ head_t prev, next; // a cicular list
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+ float[] data;
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+ int len; // data[0,len) is cached in this entry
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+ }
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+ private final head_t[] head;
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+ private head_t lru_head;
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+
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+ Cache(int l_, long size_)
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+ {
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+ l = l_;
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+ size = size_;
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+ head = new head_t[l];
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+ for(int i=0;i<l;i++) head[i] = new head_t();
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+ size /= 4;
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+ size -= times4(l);
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+ size = Math.max(size, 2* (long) l); // cache must be large enough for two columns
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+ lru_head = new head_t();
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+ lru_head.next = lru_head.prev = lru_head;
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+ }
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+
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+ private long times4(long val) {
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+ return val * 4;
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+ }
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+
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+
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+ private void lru_delete(head_t h)
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+ {
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+ // delete from current location
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+ h.prev.next = h.next;
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+ h.next.prev = h.prev;
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+ }
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+
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+ private void lru_insert(head_t h)
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+ {
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+ // insert to last position
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+ h.next = lru_head;
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+ h.prev = lru_head.prev;
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+ h.prev.next = h;
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+ h.next.prev = h;
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+ }
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+
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+ // request data [0,len)
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+ // return some position p where [p,len) need to be filled
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+ // (p >= len if nothing needs to be filled)
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+ // java: simulate pointer using single-element array
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+ int get_data(int index, float[][] data, int len)
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+ {
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+ head_t h = head[index];
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+ if(h.len > 0) lru_delete(h);
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+ int more = len - h.len;
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+
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+ if(more > 0)
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+ {
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+ // free old space
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+ while(size < more)
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+ {
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+ head_t old = lru_head.next;
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+ lru_delete(old);
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+ size += old.len;
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+ old.data = null;
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+ old.len = 0;
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+ }
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+
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+ // allocate new space
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+ float[] new_data = new float[len];
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+ if(h.data != null) System.arraycopy(h.data,0,new_data,0,h.len);
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+ h.data = new_data;
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+ size -= more;
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+ do {int tmp = h.len; h.len=len; len = tmp;} while(false);
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+ }
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+
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+ lru_insert(h);
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+ data[0] = h.data;
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+ return len;
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+ }
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+
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+ void swap_index(int i, int j)
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+ {
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+ if(i==j) return;
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+
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+ if(head[i].len > 0) lru_delete(head[i]);
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+ if(head[j].len > 0) lru_delete(head[j]);
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+ do {float[] tmp = head[i].data; head[i].data=head[j].data; head[j].data = tmp;} while(false);
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+ do {int tmp = head[i].len; head[i].len=head[j].len; head[j].len = tmp;} while(false);
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+ if(head[i].len > 0) lru_insert(head[i]);
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+ if(head[j].len > 0) lru_insert(head[j]);
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+
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+ if(i>j) do {int tmp = i; i=j; j = tmp;} while(false);
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+ for(head_t h = lru_head.next; h!=lru_head; h=h.next)
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+ {
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+ if(h.len > i)
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+ {
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+ if(h.len > j)
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+ do {float tmp = h.data[i]; h.data[i]=h.data[j]; h.data[j] = tmp;} while(false);
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+ else
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+ {
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+ // give up
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+ lru_delete(h);
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+ size += h.len;
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+ h.data = null;
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+ h.len = 0;
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+ }
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+ }
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+ }
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+ }
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+}
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+
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+
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+//
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+// Kernel evaluation
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+//
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+// the static method k_function is for doing single kernel evaluation
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+// the constructor of Kernel prepares to calculate the l*l kernel matrix
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+// the member function get_Q is for getting one column from the Q Matrix
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+//
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+abstract class QMatrix {
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+ abstract float[] get_Q(int column, int len);
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+ abstract double[] get_QD();
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+ abstract void swap_index(int i, int j);
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+}
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+
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+
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+abstract class Kernel extends QMatrix {
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+ private SupportVectorMachineNode[][] x;
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+ private final double[] x_square;
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+
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+ // svm_parameter
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+ private final int kernel_type;
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+ private final int degree;
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+ private final double gamma;
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+ private final double coef0;
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+
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+ abstract float[] get_Q(int column, int len);
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+ abstract double[] get_QD();
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+
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+ void swap_index(int i, int j)
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+ {
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+ do {SupportVectorMachineNode[] tmp = x[i]; x[i]=x[j]; x[j] = tmp;} while(false);
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+ if(x_square != null) do {double tmp = x_square[i]; x_square[i]=x_square[j]; x_square[j] = tmp;} while(false);
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+ }
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+
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+ private static double powi(double base, int times)
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+ {
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+ double tmp = base, ret = 1.0;
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+
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+ for(int t = times; t>0; t/=2)
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+ {
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+ if( isOdd(t) ) ret *= tmp;
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+ tmp = tmp * tmp;
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+ }
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+ return ret;
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+ }
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+
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+ private static boolean isOdd(int t) {
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+ return Math.abs(t) % 2 == 1;
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+ }
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+
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+ double kernel_function(int i, int j)
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+ {
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+ switch(kernel_type)
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+ {
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+ case svm_parameter.LINEAR:
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+ return dot(x[i],x[j]);
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+ case svm_parameter.POLY:
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+ return powi(gamma*dot(x[i],x[j])+coef0,degree);
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+ case svm_parameter.RBF:
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+ return Math.exp(-gamma*(x_square[i]+x_square[j]-2*dot(x[i],x[j])));
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+ case svm_parameter.SIGMOID:
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+ return Math.tanh(gamma*dot(x[i],x[j])+coef0);
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+ case svm_parameter.PRECOMPUTED:
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+ return x[i][(int)(x[j][0].value)].value;
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+ default:
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+ return 0; // java
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+ }
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+ }
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+
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+ Kernel(int l, SupportVectorMachineNode[][] x_, svm_parameter param)
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+ {
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+ this.kernel_type = param.kernel_type;
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+ this.degree = param.degree;
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+ this.gamma = param.gamma;
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+ this.coef0 = param.coef0;
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+
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+ x = x_.clone();
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+
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+ if(kernel_type == svm_parameter.RBF)
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+ {
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+ x_square = new double[l];
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+ for(int i=0;i<l;i++)
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+ x_square[i] = dot(x[i],x[i]);
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+ }
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+ else x_square = null;
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+ }
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+
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+ static double dot(SupportVectorMachineNode[] x, SupportVectorMachineNode[] y)
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+ {
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+ double sum = 0;
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+ int xlen = x.length;
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+ int ylen = y.length;
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+ int i = 0;
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+ int j = 0;
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+ while(i < xlen && j < ylen)
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+ {
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+ if(x[i].index == y[j].index)
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+ sum += x[i++].value * y[j++].value;
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+ else
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+ {
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+ if(x[i].index > y[j].index)
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+ ++j;
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+ else
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+ ++i;
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+ }
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+ }
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+ return sum;
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+ }
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+
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+ static double k_function(SupportVectorMachineNode[] x, SupportVectorMachineNode[] y,
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+ svm_parameter param)
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+ {
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+ switch(param.kernel_type)
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+ {
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+ case svm_parameter.LINEAR:
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+ return dot(x,y);
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+ case svm_parameter.POLY:
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+ return powi(param.gamma*dot(x,y)+param.coef0,param.degree);
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+ case svm_parameter.RBF:
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+ {
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+ double sum = 0;
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+ int xlen = x.length;
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+ int ylen = y.length;
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+ int i = 0;
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+ int j = 0;
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+ while(i < xlen && j < ylen)
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+ {
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+ if(x[i].index == y[j].index)
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+ {
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+ double d = x[i++].value - y[j++].value;
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+ sum += d*d;
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+ }
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+ else if(x[i].index > y[j].index)
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+ {
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+ sum += y[j].value * y[j].value;
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+ ++j;
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+ }
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+ else
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+ {
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+ sum += x[i].value * x[i].value;
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+ ++i;
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+ }
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+ }
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+
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+ while(i < xlen)
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+ {
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+ sum += x[i].value * x[i].value;
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+ ++i;
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+ }
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+
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+ while(j < ylen)
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+ {
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+ sum += y[j].value * y[j].value;
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+ ++j;
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+ }
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+
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+ return Math.exp(-param.gamma*sum);
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+ }
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+ case svm_parameter.SIGMOID:
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+ return Math.tanh(param.gamma*dot(x,y)+param.coef0);
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+ case svm_parameter.PRECOMPUTED:
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+ return x[(int)(y[0].value)].value;
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+ default:
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+ return 0; // java
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+ }
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+ }
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+}
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+
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+
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+// An SMO algorithm in Fan et al., JMLR 6(2005), p. 1889--1918
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+// Solves:
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+//
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+// min 0.5(\alpha^T Q \alpha) + p^T \alpha
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+//
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+// y^T \alpha = \delta
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+// y_i = +1 or -1
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+// 0 <= alpha_i <= Cp for y_i = 1
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+// 0 <= alpha_i <= Cn for y_i = -1
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+//
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+// Given:
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+//
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+// Q, p, y, Cp, Cn, and an initial feasible point \alpha
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+// l is the size of vectors and matrices
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+// eps is the stopping tolerance
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+//
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+// solution will be put in \alpha, objective value will be put in obj
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+//
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+class Solver {
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+ int active_size;
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+ byte[] y;
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+ double[] G; // gradient of objective function
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+ static final byte LOWER_BOUND = 0;
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+ static final byte UPPER_BOUND = 1;
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+ static final byte FREE = 2;
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+ byte[] alpha_status; // LOWER_BOUND, UPPER_BOUND, FREE
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+ double[] alpha;
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+ QMatrix Q;
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+ double[] QD;
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+ double eps;
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+ double Cp,Cn;
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+ double[] p;
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+ int[] active_set;
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+ double[] G_bar; // gradient, if we treat free variables as 0
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+ int l;
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+ boolean unshrink; // XXX
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+
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+ static final double INF = Double.POSITIVE_INFINITY;
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+
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+ double get_C(int i)
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+ {
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+ return (y[i] > 0)? Cp : Cn;
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+ }
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+ void update_alpha_status(int i)
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+ {
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+ if(alpha[i] >= get_C(i))
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+ alpha_status[i] = UPPER_BOUND;
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+ else if(alpha[i] <= 0)
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+ alpha_status[i] = LOWER_BOUND;
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+ else alpha_status[i] = FREE;
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+ }
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+ boolean is_upper_bound(int i) { return alpha_status[i] == UPPER_BOUND; }
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+ boolean is_lower_bound(int i) { return alpha_status[i] == LOWER_BOUND; }
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+ boolean is_free(int i) { return alpha_status[i] == FREE; }
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+
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+ // java: information about solution except alpha,
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+ // because we cannot return multiple values otherwise...
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+ static class SolutionInfo {
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+ double obj;
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+ double rho;
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+ double upper_bound_p;
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+ double upper_bound_n;
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+ double r; // for Solver_NU
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+ }
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+
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+ void swap_index(int i, int j)
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+ {
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+ Q.swap_index(i,j);
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+ do {byte tmp = y[i]; y[i]=y[j]; y[j] = tmp;} while(false);
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+ do {double tmp = G[i]; G[i]=G[j]; G[j] = tmp;} while(false);
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+ do {byte tmp = alpha_status[i]; alpha_status[i]=alpha_status[j]; alpha_status[j] = tmp;} while(false);
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+ do {double tmp = alpha[i]; alpha[i]=alpha[j]; alpha[j] = tmp;} while(false);
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+ do {double tmp = p[i]; p[i]=p[j]; p[j] = tmp;} while(false);
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+ do {int tmp = active_set[i]; active_set[i]=active_set[j]; active_set[j] = tmp;} while(false);
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+ do {double tmp = G_bar[i]; G_bar[i]=G_bar[j]; G_bar[j] = tmp;} while(false);
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+ }
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+
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+ void reconstruct_gradient()
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+ {
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+ // reconstruct inactive elements of G from G_bar and free variables
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+
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+ if(active_size == l) return;
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+
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+ int i,j;
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+ int nr_free = 0;
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+
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+ for(j=active_size;j<l;j++)
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+ G[j] = G_bar[j] + p[j];
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+
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+ for(j=0;j<active_size;j++)
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+ if(is_free(j))
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+ nr_free++;
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+
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+ if(2*nr_free < active_size)
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+ SupportVectorMachine.info("\nWARNING: using -h 0 may be faster\n");
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+
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+ if (nr_free*l > 2*active_size*(l-active_size))
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+ {
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+ for(i=active_size;i<l;i++)
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+ {
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+ float[] Q_i = Q.get_Q(i,active_size);
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+ for(j=0;j<active_size;j++)
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+ if(is_free(j))
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+ G[i] += alpha[j] * Q_i[j];
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+ }
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+ }
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+ else
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+ {
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+ for(i=0;i<active_size;i++)
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+ if(is_free(i))
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+ {
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+ float[] Q_i = Q.get_Q(i,l);
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+ double alpha_i = alpha[i];
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+ for(j=active_size;j<l;j++)
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+ G[j] += alpha_i * Q_i[j];
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+ }
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+ }
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+ }
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+
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+ void Solve(int l, QMatrix Q, double[] p_, byte[] y_,
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+ double[] alpha_, double Cp, double Cn, double eps, SolutionInfo si, int shrinking)
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+ {
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+ this.l = l;
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+ this.Q = Q;
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+ QD = Q.get_QD();
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+ p = p_.clone();
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+ y = y_.clone();
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+ alpha = alpha_.clone();
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+ this.Cp = Cp;
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+ this.Cn = Cn;
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+ this.eps = eps;
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+ this.unshrink = false;
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+
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+ // initialize alpha_status
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+ {
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+ alpha_status = new byte[l];
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+ for(int i=0;i<l;i++)
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+ update_alpha_status(i);
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+ }
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+
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+ // initialize active set (for shrinking)
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+ {
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+ active_set = new int[l];
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+ for(int i=0;i<l;i++)
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+ active_set[i] = i;
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+ active_size = l;
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+ }
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+
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+ // initialize gradient
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+ {
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+ G = new double[l];
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+ G_bar = new double[l];
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+ int i;
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+ for(i=0;i<l;i++)
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+ {
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+ G[i] = p[i];
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+ G_bar[i] = 0;
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+ }
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+ for(i=0;i<l;i++)
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+ if(!is_lower_bound(i))
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+ {
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+ float[] Q_i = Q.get_Q(i,l);
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+ double alpha_i = alpha[i];
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+ int j;
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+ for(j=0;j<l;j++)
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+ G[j] += alpha_i*Q_i[j];
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+ if(is_upper_bound(i))
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+ for(j=0;j<l;j++)
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+ G_bar[j] += get_C(i) * Q_i[j];
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+ }
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+ }
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+
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+ // optimization step
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+
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+ int iter = 0;
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+ int max_iter = Math.max(10000000, l>Integer.MAX_VALUE/100 ? Integer.MAX_VALUE : 100*l);
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+ int counter = Math.min(l,1000)+1;
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+ int[] working_set = new int[2];
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+
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+ while(iter < max_iter)
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+ {
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+ // show progress and do shrinking
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+
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+ if(--counter == 0)
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+ {
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+ counter = Math.min(l,1000);
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+ if(shrinking!=0) do_shrinking();
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+ SupportVectorMachine.info(".");
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+ }
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+
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+ if(select_working_set(working_set)!=0)
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+ {
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+ // reconstruct the whole gradient
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+ reconstruct_gradient();
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+ // reset active set size and check
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+ active_size = l;
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+ SupportVectorMachine.info("*");
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+ if(select_working_set(working_set)!=0)
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+ break;
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+ else
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+ counter = 1; // do shrinking next iteration
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+ }
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+
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+ int i = working_set[0];
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+ int j = working_set[1];
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+
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+ ++iter;
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+
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+ // update alpha[i] and alpha[j], handle bounds carefully
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+
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+ float[] Q_i = Q.get_Q(i,active_size);
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+ float[] Q_j = Q.get_Q(j,active_size);
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+
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+ double C_i = get_C(i);
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+ double C_j = get_C(j);
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+
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+ double old_alpha_i = alpha[i];
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+ double old_alpha_j = alpha[j];
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+
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+ if(y[i]!=y[j])
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+ {
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+ double quad_coef = QD[i]+QD[j]+2*Q_i[j];
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+ if (quad_coef <= 0)
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+ quad_coef = 1e-12;
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+ double delta = (-G[i]-G[j])/quad_coef;
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+ double diff = alpha[i] - alpha[j];
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+ alpha[i] += delta;
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+ alpha[j] += delta;
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+
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+ if(diff > 0)
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+ {
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+ if(alpha[j] < 0)
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+ {
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+ alpha[j] = 0;
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+ alpha[i] = diff;
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|
|
+ }
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|
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+ }
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+ else
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+ {
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+ if(alpha[i] < 0)
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+ {
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+ alpha[i] = 0;
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+ alpha[j] = -diff;
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|
|
+ }
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|
|
+ }
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|
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+ if(diff > C_i - C_j)
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+ {
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|
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+ if(alpha[i] > C_i)
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+ {
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|
+ alpha[i] = C_i;
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|
+ alpha[j] = C_i - diff;
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|
|
+ }
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|
|
+ }
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|
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+ else
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+ {
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+ if(alpha[j] > C_j)
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+ {
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|
+ alpha[j] = C_j;
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|
+ alpha[i] = C_j + diff;
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|
|
+ }
|
|
|
+ }
|
|
|
+ }
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|
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+ else
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|
+ {
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|
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+ double quad_coef = QD[i]+QD[j]-2*Q_i[j];
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|
|
+ if (quad_coef <= 0)
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|
|
+ quad_coef = 1e-12;
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|
+ double delta = (G[i]-G[j])/quad_coef;
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|
+ double sum = alpha[i] + alpha[j];
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|
+ alpha[i] -= delta;
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|
+ alpha[j] += delta;
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|
+
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|
|
+ if(sum > C_i)
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+ {
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|
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+ if(alpha[i] > C_i)
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|
|
+ {
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|
|
+ alpha[i] = C_i;
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|
+ alpha[j] = sum - C_i;
|
|
|
+ }
|
|
|
+ }
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|
|
+ else
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|
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+ {
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|
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+ if(alpha[j] < 0)
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|
+ {
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|
+ alpha[j] = 0;
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|
|
+ alpha[i] = sum;
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|
|
+ }
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|
|
+ }
|
|
|
+ if(sum > C_j)
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|
|
+ {
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|
|
+ if(alpha[j] > C_j)
|
|
|
+ {
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|
|
+ alpha[j] = C_j;
|
|
|
+ alpha[i] = sum - C_j;
|
|
|
+ }
|
|
|
+ }
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|
|
+ else
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|
|
+ {
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|
|
+ if(alpha[i] < 0)
|
|
|
+ {
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|
|
+ alpha[i] = 0;
|
|
|
+ alpha[j] = sum;
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ // update G
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|
|
+
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|
|
+ double delta_alpha_i = alpha[i] - old_alpha_i;
|
|
|
+ double delta_alpha_j = alpha[j] - old_alpha_j;
|
|
|
+
|
|
|
+ for(int k=0;k<active_size;k++)
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|
|
+ {
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|
|
+ G[k] += Q_i[k]*delta_alpha_i + Q_j[k]*delta_alpha_j;
|
|
|
+ }
|
|
|
+
|
|
|
+ // update alpha_status and G_bar
|
|
|
+
|
|
|
+ {
|
|
|
+ boolean ui = is_upper_bound(i);
|
|
|
+ boolean uj = is_upper_bound(j);
|
|
|
+ update_alpha_status(i);
|
|
|
+ update_alpha_status(j);
|
|
|
+ int k;
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|
|
+ if(ui != is_upper_bound(i))
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|
|
+ {
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|
|
+ Q_i = Q.get_Q(i,l);
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|
|
+ if(ui)
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|
+ for(k=0;k<l;k++)
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|
|
+ G_bar[k] -= C_i * Q_i[k];
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|
|
+ else
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|
|
+ for(k=0;k<l;k++)
|
|
|
+ G_bar[k] += C_i * Q_i[k];
|
|
|
+ }
|
|
|
+
|
|
|
+ if(uj != is_upper_bound(j))
|
|
|
+ {
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|
|
+ Q_j = Q.get_Q(j,l);
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|
|
+ if(uj)
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|
|
+ for(k=0;k<l;k++)
|
|
|
+ G_bar[k] -= C_j * Q_j[k];
|
|
|
+ else
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|
|
+ for(k=0;k<l;k++)
|
|
|
+ G_bar[k] += C_j * Q_j[k];
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ }
|
|
|
+
|
|
|
+ if(iter >= max_iter)
|
|
|
+ {
|
|
|
+ if(active_size < l)
|
|
|
+ {
|
|
|
+ // reconstruct the whole gradient to calculate objective value
|
|
|
+ reconstruct_gradient();
|
|
|
+ active_size = l;
|
|
|
+ SupportVectorMachine.info("*");
|
|
|
+ }
|
|
|
+ System.err.print("\nWARNING: reaching max number of iterations\n");
|
|
|
+ }
|
|
|
+
|
|
|
+ // calculate rho
|
|
|
+
|
|
|
+ si.rho = calculate_rho();
|
|
|
+
|
|
|
+ // calculate objective value
|
|
|
+ {
|
|
|
+ double v = 0;
|
|
|
+ int i;
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ v += alpha[i] * (G[i] + p[i]);
|
|
|
+
|
|
|
+ si.obj = v/2;
|
|
|
+ }
|
|
|
+
|
|
|
+ // put back the solution
|
|
|
+ {
|
|
|
+ for(int i=0;i<l;i++)
|
|
|
+ alpha_[active_set[i]] = alpha[i];
|
|
|
+ }
|
|
|
+
|
|
|
+ si.upper_bound_p = Cp;
|
|
|
+ si.upper_bound_n = Cn;
|
|
|
+
|
|
|
+ SupportVectorMachine.info("\noptimization finished, #iter = "+iter+"\n");
|
|
|
+ }
|
|
|
+
|
|
|
+ // return 1 if already optimal, return 0 otherwise
|
|
|
+ int select_working_set(int[] working_set)
|
|
|
+ {
|
|
|
+ // return i,j such that
|
|
|
+ // i: maximizes -y_i * grad(f)_i, i in I_up(\alpha)
|
|
|
+ // j: mimimizes the decrease of obj value
|
|
|
+ // (if quadratic coefficeint <= 0, replace it with tau)
|
|
|
+ // -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha)
|
|
|
+
|
|
|
+ double Gmax = -INF;
|
|
|
+ double Gmax2 = -INF;
|
|
|
+ int Gmax_idx = -1;
|
|
|
+ int Gmin_idx = -1;
|
|
|
+ double obj_diff_min = INF;
|
|
|
+
|
|
|
+ for(int t=0;t<active_size;t++)
|
|
|
+ if(y[t]==+1)
|
|
|
+ {
|
|
|
+ if(!is_upper_bound(t))
|
|
|
+ if(-G[t] >= Gmax)
|
|
|
+ {
|
|
|
+ Gmax = -G[t];
|
|
|
+ Gmax_idx = t;
|
|
|
+ }
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ if(!is_lower_bound(t))
|
|
|
+ if(G[t] >= Gmax)
|
|
|
+ {
|
|
|
+ Gmax = G[t];
|
|
|
+ Gmax_idx = t;
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ int i = Gmax_idx;
|
|
|
+ float[] Q_i = null;
|
|
|
+ if(i != -1) // null Q_i not accessed: Gmax=-INF if i=-1
|
|
|
+ Q_i = Q.get_Q(i,active_size);
|
|
|
+
|
|
|
+ for(int j=0;j<active_size;j++)
|
|
|
+ {
|
|
|
+ if(y[j]==+1)
|
|
|
+ {
|
|
|
+ if (!is_lower_bound(j))
|
|
|
+ {
|
|
|
+ double grad_diff=Gmax+G[j];
|
|
|
+ if (G[j] >= Gmax2)
|
|
|
+ Gmax2 = G[j];
|
|
|
+ if (grad_diff > 0)
|
|
|
+ {
|
|
|
+ double obj_diff;
|
|
|
+ double quad_coef = QD[i]+QD[j]-2.0*y[i]*Q_i[j];
|
|
|
+ if (quad_coef > 0)
|
|
|
+ obj_diff = -(grad_diff*grad_diff)/quad_coef;
|
|
|
+ else
|
|
|
+ obj_diff = -(grad_diff*grad_diff)/1e-12;
|
|
|
+
|
|
|
+ if (obj_diff <= obj_diff_min)
|
|
|
+ {
|
|
|
+ Gmin_idx=j;
|
|
|
+ obj_diff_min = obj_diff;
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ if (!is_upper_bound(j))
|
|
|
+ {
|
|
|
+ double grad_diff= Gmax-G[j];
|
|
|
+ if (-G[j] >= Gmax2)
|
|
|
+ Gmax2 = -G[j];
|
|
|
+ if (grad_diff > 0)
|
|
|
+ {
|
|
|
+ double obj_diff;
|
|
|
+ double quad_coef = QD[i]+QD[j]+2.0*y[i]*Q_i[j];
|
|
|
+ if (quad_coef > 0)
|
|
|
+ obj_diff = -(grad_diff*grad_diff)/quad_coef;
|
|
|
+ else
|
|
|
+ obj_diff = -(grad_diff*grad_diff)/1e-12;
|
|
|
+
|
|
|
+ if (obj_diff <= obj_diff_min)
|
|
|
+ {
|
|
|
+ Gmin_idx=j;
|
|
|
+ obj_diff_min = obj_diff;
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ if(Gmax+Gmax2 < eps)
|
|
|
+ return 1;
|
|
|
+
|
|
|
+ working_set[0] = Gmax_idx;
|
|
|
+ working_set[1] = Gmin_idx;
|
|
|
+ return 0;
|
|
|
+ }
|
|
|
+
|
|
|
+ private boolean be_shrunk(int i, double Gmax1, double Gmax2)
|
|
|
+ {
|
|
|
+ if(is_upper_bound(i))
|
|
|
+ {
|
|
|
+ if(y[i]==+1)
|
|
|
+ return(-G[i] > Gmax1);
|
|
|
+ else
|
|
|
+ return(-G[i] > Gmax2);
|
|
|
+ }
|
|
|
+ else if(is_lower_bound(i))
|
|
|
+ {
|
|
|
+ if(y[i]==+1)
|
|
|
+ return(G[i] > Gmax2);
|
|
|
+ else
|
|
|
+ return(G[i] > Gmax1);
|
|
|
+ }
|
|
|
+ else
|
|
|
+ return(false);
|
|
|
+ }
|
|
|
+
|
|
|
+ void do_shrinking()
|
|
|
+ {
|
|
|
+ int i;
|
|
|
+ double Gmax1 = -INF; // max { -y_i * grad(f)_i | i in I_up(\alpha) }
|
|
|
+ double Gmax2 = -INF; // max { y_i * grad(f)_i | i in I_low(\alpha) }
|
|
|
+
|
|
|
+ // findNode maximal violating pair first
|
|
|
+ for(i=0;i<active_size;i++)
|
|
|
+ {
|
|
|
+ if(y[i]==+1)
|
|
|
+ {
|
|
|
+ if(!is_upper_bound(i))
|
|
|
+ {
|
|
|
+ if(-G[i] >= Gmax1)
|
|
|
+ Gmax1 = -G[i];
|
|
|
+ }
|
|
|
+ if(!is_lower_bound(i))
|
|
|
+ {
|
|
|
+ if(G[i] >= Gmax2)
|
|
|
+ Gmax2 = G[i];
|
|
|
+ }
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ if(!is_upper_bound(i))
|
|
|
+ {
|
|
|
+ if(-G[i] >= Gmax2)
|
|
|
+ Gmax2 = -G[i];
|
|
|
+ }
|
|
|
+ if(!is_lower_bound(i))
|
|
|
+ {
|
|
|
+ if(G[i] >= Gmax1)
|
|
|
+ Gmax1 = G[i];
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ if(unshrink == false && Gmax1 + Gmax2 <= eps*10)
|
|
|
+ {
|
|
|
+ unshrink = true;
|
|
|
+ reconstruct_gradient();
|
|
|
+ active_size = l;
|
|
|
+ }
|
|
|
+
|
|
|
+ for(i=0;i<active_size;i++)
|
|
|
+ if (be_shrunk(i, Gmax1, Gmax2))
|
|
|
+ {
|
|
|
+ active_size--;
|
|
|
+ while (active_size > i)
|
|
|
+ {
|
|
|
+ if (!be_shrunk(active_size, Gmax1, Gmax2))
|
|
|
+ {
|
|
|
+ swap_index(i,active_size);
|
|
|
+ break;
|
|
|
+ }
|
|
|
+ active_size--;
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ double calculate_rho()
|
|
|
+ {
|
|
|
+ double r;
|
|
|
+ int nr_free = 0;
|
|
|
+ double ub = INF, lb = -INF, sum_free = 0;
|
|
|
+ for(int i=0;i<active_size;i++)
|
|
|
+ {
|
|
|
+ double yG = y[i]*G[i];
|
|
|
+
|
|
|
+ if(is_lower_bound(i))
|
|
|
+ {
|
|
|
+ if(y[i] > 0)
|
|
|
+ ub = Math.min(ub,yG);
|
|
|
+ else
|
|
|
+ lb = Math.max(lb,yG);
|
|
|
+ }
|
|
|
+ else if(is_upper_bound(i))
|
|
|
+ {
|
|
|
+ if(y[i] < 0)
|
|
|
+ ub = Math.min(ub,yG);
|
|
|
+ else
|
|
|
+ lb = Math.max(lb,yG);
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ ++nr_free;
|
|
|
+ sum_free += yG;
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ if(nr_free>0)
|
|
|
+ r = sum_free/nr_free;
|
|
|
+ else
|
|
|
+ r = (ub+lb)/2;
|
|
|
+
|
|
|
+ return r;
|
|
|
+ }
|
|
|
+
|
|
|
+}
|
|
|
+
|
|
|
+
|
|
|
+//
|
|
|
+// Solver for nu-svm classification and regression
|
|
|
+//
|
|
|
+// additional constraint: e^T \alpha = constant
|
|
|
+//
|
|
|
+final class Solver_NU extends Solver
|
|
|
+{
|
|
|
+ private SolutionInfo si;
|
|
|
+
|
|
|
+ void Solve(int l, QMatrix Q, double[] p, byte[] y,
|
|
|
+ double[] alpha, double Cp, double Cn, double eps,
|
|
|
+ SolutionInfo si, int shrinking)
|
|
|
+ {
|
|
|
+ this.si = si;
|
|
|
+ super.Solve(l,Q,p,y,alpha,Cp,Cn,eps,si,shrinking);
|
|
|
+ }
|
|
|
+
|
|
|
+ // return 1 if already optimal, return 0 otherwise
|
|
|
+ int select_working_set(int[] working_set)
|
|
|
+ {
|
|
|
+ // return i,j such that y_i = y_j and
|
|
|
+ // i: maximizes -y_i * grad(f)_i, i in I_up(\alpha)
|
|
|
+ // j: minimizes the decrease of obj value
|
|
|
+ // (if quadratic coefficeint <= 0, replace it with tau)
|
|
|
+ // -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha)
|
|
|
+
|
|
|
+ double Gmaxp = -INF;
|
|
|
+ double Gmaxp2 = -INF;
|
|
|
+ int Gmaxp_idx = -1;
|
|
|
+
|
|
|
+ double Gmaxn = -INF;
|
|
|
+ double Gmaxn2 = -INF;
|
|
|
+ int Gmaxn_idx = -1;
|
|
|
+
|
|
|
+ int Gmin_idx = -1;
|
|
|
+ double obj_diff_min = INF;
|
|
|
+
|
|
|
+ for(int t=0;t<active_size;t++)
|
|
|
+ if(y[t]==+1)
|
|
|
+ {
|
|
|
+ if(!is_upper_bound(t))
|
|
|
+ if(-G[t] >= Gmaxp)
|
|
|
+ {
|
|
|
+ Gmaxp = -G[t];
|
|
|
+ Gmaxp_idx = t;
|
|
|
+ }
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ if(!is_lower_bound(t))
|
|
|
+ if(G[t] >= Gmaxn)
|
|
|
+ {
|
|
|
+ Gmaxn = G[t];
|
|
|
+ Gmaxn_idx = t;
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ int ip = Gmaxp_idx;
|
|
|
+ int in = Gmaxn_idx;
|
|
|
+ float[] Q_ip = null;
|
|
|
+ float[] Q_in = null;
|
|
|
+ if(ip != -1) // null Q_ip not accessed: Gmaxp=-INF if ip=-1
|
|
|
+ Q_ip = Q.get_Q(ip,active_size);
|
|
|
+ if(in != -1)
|
|
|
+ Q_in = Q.get_Q(in,active_size);
|
|
|
+
|
|
|
+ for(int j=0;j<active_size;j++)
|
|
|
+ {
|
|
|
+ if(y[j]==+1)
|
|
|
+ {
|
|
|
+ if (!is_lower_bound(j))
|
|
|
+ {
|
|
|
+ double grad_diff=Gmaxp+G[j];
|
|
|
+ if (G[j] >= Gmaxp2)
|
|
|
+ Gmaxp2 = G[j];
|
|
|
+ if (grad_diff > 0)
|
|
|
+ {
|
|
|
+ double obj_diff;
|
|
|
+ double quad_coef = QD[ip]+QD[j]-2*Q_ip[j];
|
|
|
+ if (quad_coef > 0)
|
|
|
+ obj_diff = -(grad_diff*grad_diff)/quad_coef;
|
|
|
+ else
|
|
|
+ obj_diff = -(grad_diff*grad_diff)/1e-12;
|
|
|
+
|
|
|
+ if (obj_diff <= obj_diff_min)
|
|
|
+ {
|
|
|
+ Gmin_idx=j;
|
|
|
+ obj_diff_min = obj_diff;
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ if (!is_upper_bound(j))
|
|
|
+ {
|
|
|
+ double grad_diff=Gmaxn-G[j];
|
|
|
+ if (-G[j] >= Gmaxn2)
|
|
|
+ Gmaxn2 = -G[j];
|
|
|
+ if (grad_diff > 0)
|
|
|
+ {
|
|
|
+ double obj_diff;
|
|
|
+ double quad_coef = QD[in]+QD[j]-2*Q_in[j];
|
|
|
+ if (quad_coef > 0)
|
|
|
+ obj_diff = -(grad_diff*grad_diff)/quad_coef;
|
|
|
+ else
|
|
|
+ obj_diff = -(grad_diff*grad_diff)/1e-12;
|
|
|
+
|
|
|
+ if (obj_diff <= obj_diff_min)
|
|
|
+ {
|
|
|
+ Gmin_idx=j;
|
|
|
+ obj_diff_min = obj_diff;
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ if(Math.max(Gmaxp+Gmaxp2,Gmaxn+Gmaxn2) < eps)
|
|
|
+ return 1;
|
|
|
+
|
|
|
+ if(y[Gmin_idx] == +1)
|
|
|
+ working_set[0] = Gmaxp_idx;
|
|
|
+ else
|
|
|
+ working_set[0] = Gmaxn_idx;
|
|
|
+ working_set[1] = Gmin_idx;
|
|
|
+
|
|
|
+ return 0;
|
|
|
+ }
|
|
|
+
|
|
|
+ private boolean be_shrunk(int i, double Gmax1, double Gmax2, double Gmax3, double Gmax4)
|
|
|
+ {
|
|
|
+ if(is_upper_bound(i))
|
|
|
+ {
|
|
|
+ if(y[i]==+1)
|
|
|
+ return(-G[i] > Gmax1);
|
|
|
+ else
|
|
|
+ return(-G[i] > Gmax4);
|
|
|
+ }
|
|
|
+ else if(is_lower_bound(i))
|
|
|
+ {
|
|
|
+ if(y[i]==+1)
|
|
|
+ return(G[i] > Gmax2);
|
|
|
+ else
|
|
|
+ return(G[i] > Gmax3);
|
|
|
+ }
|
|
|
+ else
|
|
|
+ return(false);
|
|
|
+ }
|
|
|
+
|
|
|
+ void do_shrinking()
|
|
|
+ {
|
|
|
+ double Gmax1 = -INF; // max { -y_i * grad(f)_i | y_i = +1, i in I_up(\alpha) }
|
|
|
+ double Gmax2 = -INF; // max { y_i * grad(f)_i | y_i = +1, i in I_low(\alpha) }
|
|
|
+ double Gmax3 = -INF; // max { -y_i * grad(f)_i | y_i = -1, i in I_up(\alpha) }
|
|
|
+ double Gmax4 = -INF; // max { y_i * grad(f)_i | y_i = -1, i in I_low(\alpha) }
|
|
|
+
|
|
|
+ // findNode maximal violating pair first
|
|
|
+ int i;
|
|
|
+ for(i=0;i<active_size;i++)
|
|
|
+ {
|
|
|
+ if(!is_upper_bound(i))
|
|
|
+ {
|
|
|
+ if(y[i]==+1)
|
|
|
+ {
|
|
|
+ if(-G[i] > Gmax1) Gmax1 = -G[i];
|
|
|
+ }
|
|
|
+ else if(-G[i] > Gmax4) Gmax4 = -G[i];
|
|
|
+ }
|
|
|
+ if(!is_lower_bound(i))
|
|
|
+ {
|
|
|
+ if(y[i]==+1)
|
|
|
+ {
|
|
|
+ if(G[i] > Gmax2) Gmax2 = G[i];
|
|
|
+ }
|
|
|
+ else if(G[i] > Gmax3) Gmax3 = G[i];
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ if(unshrink == false && Math.max(Gmax1+Gmax2,Gmax3+Gmax4) <= eps*10)
|
|
|
+ {
|
|
|
+ unshrink = true;
|
|
|
+ reconstruct_gradient();
|
|
|
+ active_size = l;
|
|
|
+ }
|
|
|
+
|
|
|
+ for(i=0;i<active_size;i++)
|
|
|
+ if (be_shrunk(i, Gmax1, Gmax2, Gmax3, Gmax4))
|
|
|
+ {
|
|
|
+ active_size--;
|
|
|
+ while (active_size > i)
|
|
|
+ {
|
|
|
+ if (!be_shrunk(active_size, Gmax1, Gmax2, Gmax3, Gmax4))
|
|
|
+ {
|
|
|
+ swap_index(i,active_size);
|
|
|
+ break;
|
|
|
+ }
|
|
|
+ active_size--;
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ double calculate_rho()
|
|
|
+ {
|
|
|
+ int nr_free1 = 0,nr_free2 = 0;
|
|
|
+ double ub1 = INF, ub2 = INF;
|
|
|
+ double lb1 = -INF, lb2 = -INF;
|
|
|
+ double sum_free1 = 0, sum_free2 = 0;
|
|
|
+
|
|
|
+ for(int i=0;i<active_size;i++)
|
|
|
+ {
|
|
|
+ if(y[i]==+1)
|
|
|
+ {
|
|
|
+ if(is_lower_bound(i))
|
|
|
+ ub1 = Math.min(ub1,G[i]);
|
|
|
+ else if(is_upper_bound(i))
|
|
|
+ lb1 = Math.max(lb1,G[i]);
|
|
|
+ else
|
|
|
+ {
|
|
|
+ ++nr_free1;
|
|
|
+ sum_free1 += G[i];
|
|
|
+ }
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ if(is_lower_bound(i))
|
|
|
+ ub2 = Math.min(ub2,G[i]);
|
|
|
+ else if(is_upper_bound(i))
|
|
|
+ lb2 = Math.max(lb2,G[i]);
|
|
|
+ else
|
|
|
+ {
|
|
|
+ ++nr_free2;
|
|
|
+ sum_free2 += G[i];
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ double r1,r2;
|
|
|
+ if(nr_free1 > 0)
|
|
|
+ r1 = sum_free1/nr_free1;
|
|
|
+ else
|
|
|
+ r1 = (ub1+lb1)/2;
|
|
|
+
|
|
|
+ if(nr_free2 > 0)
|
|
|
+ r2 = sum_free2/nr_free2;
|
|
|
+ else
|
|
|
+ r2 = (ub2+lb2)/2;
|
|
|
+
|
|
|
+ si.r = (r1+r2)/2;
|
|
|
+ return (r1-r2)/2;
|
|
|
+ }
|
|
|
+}
|
|
|
+
|
|
|
+
|
|
|
+//
|
|
|
+// Q matrices for various formulations
|
|
|
+//
|
|
|
+class SVC_Q extends Kernel
|
|
|
+{
|
|
|
+ private final byte[] y;
|
|
|
+ private final Cache cache;
|
|
|
+ private final double[] QD;
|
|
|
+
|
|
|
+ SVC_Q(svm_problem prob, svm_parameter param, byte[] y_)
|
|
|
+ {
|
|
|
+ super(prob.l, prob.x, param);
|
|
|
+ y = y_.clone();
|
|
|
+ cache = new Cache(prob.l,(long)(param.cache_size*(1<<20)));
|
|
|
+ QD = new double[prob.l];
|
|
|
+ for(int i=0;i<prob.l;i++)
|
|
|
+ QD[i] = kernel_function(i,i);
|
|
|
+ }
|
|
|
+
|
|
|
+ float[] get_Q(int i, int len)
|
|
|
+ {
|
|
|
+ float[][] data = new float[1][];
|
|
|
+ int start, j;
|
|
|
+ if((start = cache.get_data(i,data,len)) < len)
|
|
|
+ {
|
|
|
+ for(j=start;j<len;j++)
|
|
|
+ data[0][j] = (float)(y[i]*y[j]*kernel_function(i,j));
|
|
|
+ }
|
|
|
+ return data[0];
|
|
|
+ }
|
|
|
+
|
|
|
+ double[] get_QD()
|
|
|
+ {
|
|
|
+ return QD;
|
|
|
+ }
|
|
|
+
|
|
|
+ void swap_index(int i, int j)
|
|
|
+ {
|
|
|
+ cache.swap_index(i,j);
|
|
|
+ super.swap_index(i,j);
|
|
|
+ do {byte tmp = y[i]; y[i]=y[j]; y[j] = tmp;} while(false);
|
|
|
+ do {double tmp = QD[i]; QD[i]=QD[j]; QD[j] = tmp;} while(false);
|
|
|
+ }
|
|
|
+}
|
|
|
+
|
|
|
+
|
|
|
+class ONE_CLASS_Q extends Kernel
|
|
|
+{
|
|
|
+ private final Cache cache;
|
|
|
+ private final double[] QD;
|
|
|
+
|
|
|
+ ONE_CLASS_Q(svm_problem prob, svm_parameter param)
|
|
|
+ {
|
|
|
+ super(prob.l, prob.x, param);
|
|
|
+ cache = new Cache(prob.l,(long)(param.cache_size*(1<<20)));
|
|
|
+ QD = new double[prob.l];
|
|
|
+ for(int i=0;i<prob.l;i++)
|
|
|
+ QD[i] = kernel_function(i,i);
|
|
|
+ }
|
|
|
+
|
|
|
+ float[] get_Q(int i, int len)
|
|
|
+ {
|
|
|
+ float[][] data = new float[1][];
|
|
|
+ int start, j;
|
|
|
+ if((start = cache.get_data(i,data,len)) < len)
|
|
|
+ {
|
|
|
+ for(j=start;j<len;j++)
|
|
|
+ data[0][j] = (float)kernel_function(i,j);
|
|
|
+ }
|
|
|
+ return data[0];
|
|
|
+ }
|
|
|
+
|
|
|
+ double[] get_QD()
|
|
|
+ {
|
|
|
+ return QD;
|
|
|
+ }
|
|
|
+
|
|
|
+ void swap_index(int i, int j)
|
|
|
+ {
|
|
|
+ cache.swap_index(i,j);
|
|
|
+ super.swap_index(i,j);
|
|
|
+ do {double tmp = QD[i]; QD[i]=QD[j]; QD[j] = tmp;} while(false);
|
|
|
+ }
|
|
|
+}
|
|
|
+
|
|
|
+
|
|
|
+class SVR_Q extends Kernel
|
|
|
+{
|
|
|
+ private final int l;
|
|
|
+ private final Cache cache;
|
|
|
+ private final byte[] sign;
|
|
|
+ private final int[] index;
|
|
|
+ private int next_buffer;
|
|
|
+ private float[][] buffer;
|
|
|
+ private final double[] QD;
|
|
|
+
|
|
|
+ SVR_Q(svm_problem prob, svm_parameter param)
|
|
|
+ {
|
|
|
+ super(prob.l, prob.x, param);
|
|
|
+ l = prob.l;
|
|
|
+ cache = new Cache(l,(long)(param.cache_size*(1<<20)));
|
|
|
+ QD = new double[2*l];
|
|
|
+ sign = new byte[2*l];
|
|
|
+ index = new int[2*l];
|
|
|
+ for(int k=0;k<l;k++)
|
|
|
+ {
|
|
|
+ sign[k] = 1;
|
|
|
+ sign[k+l] = -1;
|
|
|
+ index[k] = k;
|
|
|
+ index[k+l] = k;
|
|
|
+ QD[k] = kernel_function(k,k);
|
|
|
+ QD[k+l] = QD[k];
|
|
|
+ }
|
|
|
+ buffer = new float[2][2*l];
|
|
|
+ next_buffer = 0;
|
|
|
+ }
|
|
|
+
|
|
|
+ void swap_index(int i, int j)
|
|
|
+ {
|
|
|
+ do {byte tmp = sign[i]; sign[i]=sign[j]; sign[j] = tmp;} while(false);
|
|
|
+ do {int tmp = index[i]; index[i]=index[j]; index[j] = tmp;} while(false);
|
|
|
+ do {double tmp = QD[i]; QD[i]=QD[j]; QD[j] = tmp;} while(false);
|
|
|
+ }
|
|
|
+
|
|
|
+ float[] get_Q(int i, int len)
|
|
|
+ {
|
|
|
+ float[][] data = new float[1][];
|
|
|
+ int j, real_i = index[i];
|
|
|
+ if(cache.get_data(real_i,data,l) < l)
|
|
|
+ {
|
|
|
+ for(j=0;j<l;j++)
|
|
|
+ data[0][j] = (float)kernel_function(real_i,j);
|
|
|
+ }
|
|
|
+
|
|
|
+ // reorder and copy
|
|
|
+ float buf[] = buffer[next_buffer];
|
|
|
+ next_buffer = 1 - next_buffer;
|
|
|
+ byte si = sign[i];
|
|
|
+ for(j=0;j<len;j++)
|
|
|
+ buf[j] = (float) si * sign[j] * data[0][index[j]];
|
|
|
+ return buf;
|
|
|
+ }
|
|
|
+
|
|
|
+ double[] get_QD()
|
|
|
+ {
|
|
|
+ return QD;
|
|
|
+ }
|
|
|
+}
|
|
|
+
|
|
|
+
|
|
|
+public class SupportVectorMachine {
|
|
|
+ //
|
|
|
+ // construct and solve various formulations
|
|
|
+ //
|
|
|
+ public static final int LIBSVM_VERSION=320;
|
|
|
+ public static final Random rand = new Random();
|
|
|
+
|
|
|
+ private static svm_print_interface svm_print_stdout = new svm_print_interface()
|
|
|
+ {
|
|
|
+ public void print(String s)
|
|
|
+ {
|
|
|
+ System.out.print(s);
|
|
|
+ System.out.flush();
|
|
|
+ }
|
|
|
+ };
|
|
|
+
|
|
|
+ private static svm_print_interface svm_print_string = svm_print_stdout;
|
|
|
+
|
|
|
+ static void info(String s)
|
|
|
+ {
|
|
|
+ svm_print_string.print(s);
|
|
|
+ }
|
|
|
+
|
|
|
+ private static void solve_c_svc(svm_problem prob, svm_parameter param,
|
|
|
+ double[] alpha, Solver.SolutionInfo si,
|
|
|
+ double Cp, double Cn)
|
|
|
+ {
|
|
|
+ int l = prob.l;
|
|
|
+ double[] minus_ones = new double[l];
|
|
|
+ byte[] y = new byte[l];
|
|
|
+
|
|
|
+ int i;
|
|
|
+
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ {
|
|
|
+ alpha[i] = 0;
|
|
|
+ minus_ones[i] = -1;
|
|
|
+ if(prob.y[i] > 0) y[i] = +1; else y[i] = -1;
|
|
|
+ }
|
|
|
+
|
|
|
+ Solver s = new Solver();
|
|
|
+ s.Solve(l, new SVC_Q(prob,param,y), minus_ones, y,
|
|
|
+ alpha, Cp, Cn, param.eps, si, param.shrinking);
|
|
|
+
|
|
|
+ double sum_alpha=0;
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ sum_alpha += alpha[i];
|
|
|
+
|
|
|
+ if (Cp==Cn)
|
|
|
+ SupportVectorMachine.info("nu = "+sum_alpha/(Cp*prob.l)+"\n");
|
|
|
+
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ alpha[i] *= y[i];
|
|
|
+ }
|
|
|
+
|
|
|
+ private static void solve_nu_svc(svm_problem prob, svm_parameter param,
|
|
|
+ double[] alpha, Solver.SolutionInfo si)
|
|
|
+ {
|
|
|
+ int i;
|
|
|
+ int l = prob.l;
|
|
|
+ double nu = param.nu;
|
|
|
+
|
|
|
+ byte[] y = new byte[l];
|
|
|
+
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ if(prob.y[i]>0)
|
|
|
+ y[i] = +1;
|
|
|
+ else
|
|
|
+ y[i] = -1;
|
|
|
+
|
|
|
+ double sum_pos = nu*l/2;
|
|
|
+ double sum_neg = nu*l/2;
|
|
|
+
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ if(y[i] == +1)
|
|
|
+ {
|
|
|
+ alpha[i] = Math.min(1.0,sum_pos);
|
|
|
+ sum_pos -= alpha[i];
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ alpha[i] = Math.min(1.0,sum_neg);
|
|
|
+ sum_neg -= alpha[i];
|
|
|
+ }
|
|
|
+
|
|
|
+ double[] zeros = new double[l];
|
|
|
+
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ zeros[i] = 0;
|
|
|
+
|
|
|
+ Solver_NU s = new Solver_NU();
|
|
|
+ s.Solve(l, new SVC_Q(prob,param,y), zeros, y,
|
|
|
+ alpha, 1.0, 1.0, param.eps, si, param.shrinking);
|
|
|
+ double r = si.r;
|
|
|
+
|
|
|
+ SupportVectorMachine.info("C = "+1/r+"\n");
|
|
|
+
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ alpha[i] *= y[i]/r;
|
|
|
+
|
|
|
+ si.rho /= r;
|
|
|
+ si.obj /= (r*r);
|
|
|
+ si.upper_bound_p = 1/r;
|
|
|
+ si.upper_bound_n = 1/r;
|
|
|
+ }
|
|
|
+
|
|
|
+ private static void solve_one_class(svm_problem prob, svm_parameter param,
|
|
|
+ double[] alpha, Solver.SolutionInfo si)
|
|
|
+ {
|
|
|
+ int l = prob.l;
|
|
|
+ double[] zeros = new double[l];
|
|
|
+ byte[] ones = new byte[l];
|
|
|
+ int i;
|
|
|
+
|
|
|
+ int n = (int)(param.nu*prob.l); // # of alpha's at upper bound
|
|
|
+
|
|
|
+ for(i=0;i<n;i++)
|
|
|
+ alpha[i] = 1;
|
|
|
+ if(n<prob.l)
|
|
|
+ alpha[n] = param.nu * prob.l - n;
|
|
|
+ for(i=n+1;i<l;i++)
|
|
|
+ alpha[i] = 0;
|
|
|
+
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ {
|
|
|
+ zeros[i] = 0;
|
|
|
+ ones[i] = 1;
|
|
|
+ }
|
|
|
+
|
|
|
+ Solver s = new Solver();
|
|
|
+ s.Solve(l, new ONE_CLASS_Q(prob,param), zeros, ones,
|
|
|
+ alpha, 1.0, 1.0, param.eps, si, param.shrinking);
|
|
|
+ }
|
|
|
+
|
|
|
+ private static void solve_epsilon_svr(svm_problem prob, svm_parameter param,
|
|
|
+ double[] alpha, Solver.SolutionInfo si)
|
|
|
+ {
|
|
|
+ int l = prob.l;
|
|
|
+ double[] alpha2 = new double[2*l];
|
|
|
+ double[] linear_term = new double[2*l];
|
|
|
+ byte[] y = new byte[2*l];
|
|
|
+ int i;
|
|
|
+
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ {
|
|
|
+ alpha2[i] = 0;
|
|
|
+ linear_term[i] = param.p - prob.y[i];
|
|
|
+ y[i] = 1;
|
|
|
+
|
|
|
+ alpha2[i+l] = 0;
|
|
|
+ linear_term[i+l] = param.p + prob.y[i];
|
|
|
+ y[i+l] = -1;
|
|
|
+ }
|
|
|
+
|
|
|
+ Solver s = new Solver();
|
|
|
+ s.Solve(2*l, new SVR_Q(prob,param), linear_term, y,
|
|
|
+ alpha2, param.C, param.C, param.eps, si, param.shrinking);
|
|
|
+
|
|
|
+ double sum_alpha = 0;
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ {
|
|
|
+ alpha[i] = alpha2[i] - alpha2[i+l];
|
|
|
+ sum_alpha += Math.abs(alpha[i]);
|
|
|
+ }
|
|
|
+ SupportVectorMachine.info("nu = "+sum_alpha/(param.C*l)+"\n");
|
|
|
+ }
|
|
|
+
|
|
|
+ private static void solve_nu_svr(svm_problem prob, svm_parameter param,
|
|
|
+ double[] alpha, Solver.SolutionInfo si)
|
|
|
+ {
|
|
|
+ int l = prob.l;
|
|
|
+ double C = param.C;
|
|
|
+ double[] alpha2 = new double[2*l];
|
|
|
+ double[] linear_term = new double[2*l];
|
|
|
+ byte[] y = new byte[2*l];
|
|
|
+ int i;
|
|
|
+
|
|
|
+ double sum = C * param.nu * l / 2;
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ {
|
|
|
+ alpha2[i] = alpha2[i+l] = Math.min(sum,C);
|
|
|
+ sum -= alpha2[i];
|
|
|
+
|
|
|
+ linear_term[i] = - prob.y[i];
|
|
|
+ y[i] = 1;
|
|
|
+
|
|
|
+ linear_term[i+l] = prob.y[i];
|
|
|
+ y[i+l] = -1;
|
|
|
+ }
|
|
|
+
|
|
|
+ Solver_NU s = new Solver_NU();
|
|
|
+ s.Solve(2*l, new SVR_Q(prob,param), linear_term, y,
|
|
|
+ alpha2, C, C, param.eps, si, param.shrinking);
|
|
|
+
|
|
|
+ SupportVectorMachine.info("epsilon = "+(-si.r)+"\n");
|
|
|
+
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ alpha[i] = alpha2[i] - alpha2[i+l];
|
|
|
+ }
|
|
|
+
|
|
|
+ //
|
|
|
+ // decision_function
|
|
|
+ //
|
|
|
+ static class decision_function
|
|
|
+ {
|
|
|
+ double[] alpha;
|
|
|
+ double rho;
|
|
|
+ }
|
|
|
+
|
|
|
+ static decision_function svm_train_one(
|
|
|
+ svm_problem prob, svm_parameter param,
|
|
|
+ double Cp, double Cn)
|
|
|
+ {
|
|
|
+ double[] alpha = new double[prob.l];
|
|
|
+ Solver.SolutionInfo si = new Solver.SolutionInfo();
|
|
|
+ switch(param.svm_type)
|
|
|
+ {
|
|
|
+ case svm_parameter.C_SVC:
|
|
|
+ solve_c_svc(prob,param,alpha,si,Cp,Cn);
|
|
|
+ break;
|
|
|
+ case svm_parameter.NU_SVC:
|
|
|
+ solve_nu_svc(prob,param,alpha,si);
|
|
|
+ break;
|
|
|
+ case svm_parameter.ONE_CLASS:
|
|
|
+ solve_one_class(prob,param,alpha,si);
|
|
|
+ break;
|
|
|
+ case svm_parameter.EPSILON_SVR:
|
|
|
+ solve_epsilon_svr(prob,param,alpha,si);
|
|
|
+ break;
|
|
|
+ case svm_parameter.NU_SVR:
|
|
|
+ solve_nu_svr(prob,param,alpha,si);
|
|
|
+ break;
|
|
|
+ default:
|
|
|
+ break;
|
|
|
+ }
|
|
|
+
|
|
|
+ SupportVectorMachine.info("obj = "+si.obj+", rho = "+si.rho+"\n");
|
|
|
+
|
|
|
+ // output SVs
|
|
|
+
|
|
|
+ int nSV = 0;
|
|
|
+ int nBSV = 0;
|
|
|
+ for(int i=0;i<prob.l;i++)
|
|
|
+ {
|
|
|
+ if(Math.abs(alpha[i]) > 0)
|
|
|
+ {
|
|
|
+ ++nSV;
|
|
|
+ if(prob.y[i] > 0)
|
|
|
+ {
|
|
|
+ if(Math.abs(alpha[i]) >= si.upper_bound_p)
|
|
|
+ ++nBSV;
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ if(Math.abs(alpha[i]) >= si.upper_bound_n)
|
|
|
+ ++nBSV;
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ SupportVectorMachine.info("nSV = "+nSV+", nBSV = "+nBSV+"\n");
|
|
|
+
|
|
|
+ decision_function f = new decision_function();
|
|
|
+ f.alpha = alpha;
|
|
|
+ f.rho = si.rho;
|
|
|
+ return f;
|
|
|
+ }
|
|
|
+
|
|
|
+ // Platt's binary SVM Probablistic Output: an improvement from Lin et al.
|
|
|
+ private static void sigmoid_train(int l, double[] dec_values, double[] labels,
|
|
|
+ double[] probAB)
|
|
|
+ {
|
|
|
+ double A, B;
|
|
|
+ double prior1=0, prior0 = 0;
|
|
|
+ int i;
|
|
|
+
|
|
|
+ for (i=0;i<l;i++)
|
|
|
+ if (labels[i] > 0) prior1+=1;
|
|
|
+ else prior0+=1;
|
|
|
+
|
|
|
+ int max_iter=100; // Maximal number of iterations
|
|
|
+ double min_step=1e-10; // Minimal step taken in line search
|
|
|
+ double sigma=1e-12; // For numerically strict PD of Hessian
|
|
|
+ double eps=1e-5;
|
|
|
+ double hiTarget=(prior1+1.0)/(prior1+2.0);
|
|
|
+ double loTarget=1/(prior0+2.0);
|
|
|
+ double[] t= new double[l];
|
|
|
+ double fApB,p,q,h11,h22,h21,g1,g2,det,dA,dB,gd,stepsize;
|
|
|
+ double newA,newB,newf,d1,d2;
|
|
|
+ int iter;
|
|
|
+
|
|
|
+ // Initial Point and Initial Fun Value
|
|
|
+ A=0.0; B=Math.log((prior0+1.0)/(prior1+1.0));
|
|
|
+ double fval = 0.0;
|
|
|
+
|
|
|
+ for (i=0;i<l;i++)
|
|
|
+ {
|
|
|
+ if (labels[i]>0) t[i]=hiTarget;
|
|
|
+ else t[i]=loTarget;
|
|
|
+ fApB = dec_values[i]*A+B;
|
|
|
+ if (fApB>=0)
|
|
|
+ fval += t[i]*fApB + Math.log(1+Math.exp(-fApB));
|
|
|
+ else
|
|
|
+ fval += (t[i] - 1)*fApB +Math.log(1+Math.exp(fApB));
|
|
|
+ }
|
|
|
+ for (iter=0;iter<max_iter;iter++)
|
|
|
+ {
|
|
|
+ // Update Gradient and Hessian (use H' = H + sigma I)
|
|
|
+ h11=sigma; // numerically ensures strict PD
|
|
|
+ h22=sigma;
|
|
|
+ h21=0.0;g1=0.0;g2=0.0;
|
|
|
+ for (i=0;i<l;i++)
|
|
|
+ {
|
|
|
+ fApB = dec_values[i]*A+B;
|
|
|
+ if (fApB >= 0)
|
|
|
+ {
|
|
|
+ p=Math.exp(-fApB)/(1.0+Math.exp(-fApB));
|
|
|
+ q=1.0/(1.0+Math.exp(-fApB));
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ p=1.0/(1.0+Math.exp(fApB));
|
|
|
+ q=Math.exp(fApB)/(1.0+Math.exp(fApB));
|
|
|
+ }
|
|
|
+ d2=p*q;
|
|
|
+ h11+=dec_values[i]*dec_values[i]*d2;
|
|
|
+ h22+=d2;
|
|
|
+ h21+=dec_values[i]*d2;
|
|
|
+ d1=t[i]-p;
|
|
|
+ g1+=dec_values[i]*d1;
|
|
|
+ g2+=d1;
|
|
|
+ }
|
|
|
+
|
|
|
+ // Stopping Criteria
|
|
|
+ if (Math.abs(g1)<eps && Math.abs(g2)<eps)
|
|
|
+ break;
|
|
|
+
|
|
|
+ // Finding Newton direction: -inv(H') * g
|
|
|
+ det=h11*h22-h21*h21;
|
|
|
+ dA=-(h22*g1 - h21 * g2) / det;
|
|
|
+ dB=-(-h21*g1+ h11 * g2) / det;
|
|
|
+ gd=g1*dA+g2*dB;
|
|
|
+
|
|
|
+
|
|
|
+ stepsize = 1; // Line Search
|
|
|
+ while (stepsize >= min_step)
|
|
|
+ {
|
|
|
+ newA = A + stepsize * dA;
|
|
|
+ newB = B + stepsize * dB;
|
|
|
+
|
|
|
+ // New function value
|
|
|
+ newf = 0.0;
|
|
|
+ for (i=0;i<l;i++)
|
|
|
+ {
|
|
|
+ fApB = dec_values[i]*newA+newB;
|
|
|
+ if (fApB >= 0)
|
|
|
+ newf += t[i]*fApB + Math.log(1+Math.exp(-fApB));
|
|
|
+ else
|
|
|
+ newf += (t[i] - 1)*fApB +Math.log(1+Math.exp(fApB));
|
|
|
+ }
|
|
|
+ // Check sufficient decrease
|
|
|
+ if (newf<fval+0.0001*stepsize*gd)
|
|
|
+ {
|
|
|
+ A=newA;B=newB;fval=newf;
|
|
|
+ break;
|
|
|
+ }
|
|
|
+ else
|
|
|
+ stepsize = stepsize / 2.0;
|
|
|
+ }
|
|
|
+
|
|
|
+ if (stepsize < min_step)
|
|
|
+ {
|
|
|
+ SupportVectorMachine.info("Line search fails in two-class probability estimates\n");
|
|
|
+ break;
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ if (iter>=max_iter)
|
|
|
+ SupportVectorMachine.info("Reaching maximal iterations in two-class probability estimates\n");
|
|
|
+ probAB[0]=A;probAB[1]=B;
|
|
|
+ }
|
|
|
+
|
|
|
+ private static double sigmoid_predict(double decision_value, double A, double B)
|
|
|
+ {
|
|
|
+ double fApB = decision_value*A+B;
|
|
|
+ if (fApB >= 0)
|
|
|
+ return Math.exp(-fApB)/(1.0+Math.exp(-fApB));
|
|
|
+ else
|
|
|
+ return 1.0/(1+Math.exp(fApB)) ;
|
|
|
+ }
|
|
|
+
|
|
|
+ // Method 2 from the multiclass_prob paper by Wu, Lin, and Weng
|
|
|
+ private static void multiclass_probability(int k, double[][] r, double[] p)
|
|
|
+ {
|
|
|
+ int t,j;
|
|
|
+ int iter = 0, max_iter=Math.max(100,k);
|
|
|
+ double[][] Q=new double[k][k];
|
|
|
+ double[] Qp=new double[k];
|
|
|
+ double pQp, eps=0.005/k;
|
|
|
+
|
|
|
+ for (t=0;t<k;t++)
|
|
|
+ {
|
|
|
+ p[t]=1.0/k; // Valid if k = 1
|
|
|
+ Q[t][t]=0;
|
|
|
+ for (j=0;j<t;j++)
|
|
|
+ {
|
|
|
+ Q[t][t]+=r[j][t]*r[j][t];
|
|
|
+ Q[t][j]=Q[j][t];
|
|
|
+ }
|
|
|
+ for (j=t+1;j<k;j++)
|
|
|
+ {
|
|
|
+ Q[t][t]+=r[j][t]*r[j][t];
|
|
|
+ Q[t][j]=-r[j][t]*r[t][j];
|
|
|
+ }
|
|
|
+ }
|
|
|
+ for (iter=0;iter<max_iter;iter++)
|
|
|
+ {
|
|
|
+ // stopping condition, recalculate QP,pQP for numerical accuracy
|
|
|
+ pQp=0;
|
|
|
+ for (t=0;t<k;t++)
|
|
|
+ {
|
|
|
+ Qp[t]=0;
|
|
|
+ for (j=0;j<k;j++)
|
|
|
+ Qp[t]+=Q[t][j]*p[j];
|
|
|
+ pQp+=p[t]*Qp[t];
|
|
|
+ }
|
|
|
+ double max_error=0;
|
|
|
+ for (t=0;t<k;t++)
|
|
|
+ {
|
|
|
+ double error=Math.abs(Qp[t]-pQp);
|
|
|
+ if (error>max_error)
|
|
|
+ max_error=error;
|
|
|
+ }
|
|
|
+ if (max_error<eps) break;
|
|
|
+
|
|
|
+ for (t=0;t<k;t++)
|
|
|
+ {
|
|
|
+ double diff=(-Qp[t]+pQp)/Q[t][t];
|
|
|
+ p[t]+=diff;
|
|
|
+ pQp=(pQp+diff*(diff*Q[t][t]+2*Qp[t]))/(1+diff)/(1+diff);
|
|
|
+ for (j=0;j<k;j++)
|
|
|
+ {
|
|
|
+ Qp[j]=(Qp[j]+diff*Q[t][j])/(1+diff);
|
|
|
+ p[j]/=(1+diff);
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+ if (iter>=max_iter)
|
|
|
+ SupportVectorMachine.info("Exceeds max_iter in multiclass_prob\n");
|
|
|
+ }
|
|
|
+
|
|
|
+ // Cross-validation decision values for probability estimates
|
|
|
+ private static void svm_binary_svc_probability(svm_problem prob, svm_parameter param, double Cp, double Cn, double[] probAB)
|
|
|
+ {
|
|
|
+ int i;
|
|
|
+ int nr_fold = 5;
|
|
|
+ int[] perm = new int[prob.l];
|
|
|
+ double[] dec_values = new double[prob.l];
|
|
|
+
|
|
|
+ // naive shuffle
|
|
|
+ for(i=0;i<prob.l;i++) perm[i]=i;
|
|
|
+ for(i=0;i<prob.l;i++)
|
|
|
+ {
|
|
|
+ int j = i+rand.nextInt(prob.l-i);
|
|
|
+ do {int tmp = perm[i]; perm[i]=perm[j]; perm[j] = tmp;} while(false);
|
|
|
+ }
|
|
|
+ for(i=0;i<nr_fold;i++)
|
|
|
+ {
|
|
|
+ int begin = i*prob.l/nr_fold;
|
|
|
+ int end = (i+1)*prob.l/nr_fold;
|
|
|
+ int j,k;
|
|
|
+ svm_problem subprob = new svm_problem();
|
|
|
+
|
|
|
+ subprob.l = prob.l-(end-begin);
|
|
|
+ subprob.x = new SupportVectorMachineNode[subprob.l][];
|
|
|
+ subprob.y = new double[subprob.l];
|
|
|
+
|
|
|
+ k=0;
|
|
|
+ for(j=0;j<begin;j++)
|
|
|
+ {
|
|
|
+ subprob.x[k] = prob.x[perm[j]];
|
|
|
+ subprob.y[k] = prob.y[perm[j]];
|
|
|
+ ++k;
|
|
|
+ }
|
|
|
+ for(j=end;j<prob.l;j++)
|
|
|
+ {
|
|
|
+ subprob.x[k] = prob.x[perm[j]];
|
|
|
+ subprob.y[k] = prob.y[perm[j]];
|
|
|
+ ++k;
|
|
|
+ }
|
|
|
+ int p_count=0,n_count=0;
|
|
|
+ for(j=0;j<k;j++)
|
|
|
+ if(subprob.y[j]>0)
|
|
|
+ p_count++;
|
|
|
+ else
|
|
|
+ n_count++;
|
|
|
+
|
|
|
+ if(p_count==0 && n_count==0)
|
|
|
+ for(j=begin;j<end;j++)
|
|
|
+ dec_values[perm[j]] = 0;
|
|
|
+ else if(p_count > 0 && n_count == 0)
|
|
|
+ for(j=begin;j<end;j++)
|
|
|
+ dec_values[perm[j]] = 1;
|
|
|
+ else if(p_count == 0 && n_count > 0)
|
|
|
+ for(j=begin;j<end;j++)
|
|
|
+ dec_values[perm[j]] = -1;
|
|
|
+ else
|
|
|
+ {
|
|
|
+ svm_parameter subparam = param.makeCopy();
|
|
|
+ subparam.probability=0;
|
|
|
+ subparam.C=1.0;
|
|
|
+ subparam.nr_weight=2;
|
|
|
+ subparam.weight_label = new int[2];
|
|
|
+ subparam.weight = new double[2];
|
|
|
+ subparam.weight_label[0]=+1;
|
|
|
+ subparam.weight_label[1]=-1;
|
|
|
+ subparam.weight[0]=Cp;
|
|
|
+ subparam.weight[1]=Cn;
|
|
|
+ svm_model submodel = svm_train(subprob,subparam);
|
|
|
+ for(j=begin;j<end;j++)
|
|
|
+ {
|
|
|
+ double[] dec_value=new double[1];
|
|
|
+ svm_predict_values(submodel,prob.x[perm[j]],dec_value);
|
|
|
+ dec_values[perm[j]]=dec_value[0];
|
|
|
+ // ensure +1 -1 order; reason not using CV subroutine
|
|
|
+ dec_values[perm[j]] *= submodel.label[0];
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+ sigmoid_train(prob.l,dec_values,prob.y,probAB);
|
|
|
+ }
|
|
|
+
|
|
|
+ // Return parameter of a Laplace distribution
|
|
|
+ private static double svm_svr_probability(svm_problem prob, svm_parameter param)
|
|
|
+ {
|
|
|
+ int i;
|
|
|
+ int nr_fold = 5;
|
|
|
+ double[] ymv = new double[prob.l];
|
|
|
+ double mae = 0;
|
|
|
+
|
|
|
+ svm_parameter newparam = param.makeCopy();
|
|
|
+ newparam.probability = 0;
|
|
|
+ svm_cross_validation(prob,newparam,nr_fold,ymv);
|
|
|
+ for(i=0;i<prob.l;i++)
|
|
|
+ {
|
|
|
+ ymv[i]=prob.y[i]-ymv[i];
|
|
|
+ mae += Math.abs(ymv[i]);
|
|
|
+ }
|
|
|
+ mae /= prob.l;
|
|
|
+ double std=Math.sqrt(2*mae*mae);
|
|
|
+ int count=0;
|
|
|
+ mae=0;
|
|
|
+ for(i=0;i<prob.l;i++)
|
|
|
+ if (Math.abs(ymv[i]) > 5*std)
|
|
|
+ count=count+1;
|
|
|
+ else
|
|
|
+ mae+=Math.abs(ymv[i]);
|
|
|
+ mae /= (prob.l-count);
|
|
|
+ SupportVectorMachine.info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma="+mae+"\n");
|
|
|
+ return mae;
|
|
|
+ }
|
|
|
+
|
|
|
+ // label: label name, start: begin of each class, count: #data of classes, perm: indices to the original data
|
|
|
+ // perm, length l, must be allocated before calling this subroutine
|
|
|
+ private static void svm_group_classes(svm_problem prob, int[] nr_class_ret, int[][] label_ret, int[][] start_ret, int[][] count_ret, int[] perm)
|
|
|
+ {
|
|
|
+ int l = prob.l;
|
|
|
+ int max_nr_class = 16;
|
|
|
+ int nr_class = 0;
|
|
|
+ int[] label = new int[max_nr_class];
|
|
|
+ int[] count = new int[max_nr_class];
|
|
|
+ int[] data_label = new int[l];
|
|
|
+ int i;
|
|
|
+
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ {
|
|
|
+ int this_label = (int)(prob.y[i]);
|
|
|
+ int j;
|
|
|
+ for(j=0;j<nr_class;j++)
|
|
|
+ {
|
|
|
+ if(this_label == label[j])
|
|
|
+ {
|
|
|
+ ++count[j];
|
|
|
+ break;
|
|
|
+ }
|
|
|
+ }
|
|
|
+ data_label[i] = j;
|
|
|
+ if(j == nr_class)
|
|
|
+ {
|
|
|
+ if(nr_class == max_nr_class)
|
|
|
+ {
|
|
|
+ max_nr_class *= 2;
|
|
|
+ int[] new_data = new int[max_nr_class];
|
|
|
+ System.arraycopy(label,0,new_data,0,label.length);
|
|
|
+ label = new_data;
|
|
|
+ new_data = new int[max_nr_class];
|
|
|
+ System.arraycopy(count,0,new_data,0,count.length);
|
|
|
+ count = new_data;
|
|
|
+ }
|
|
|
+ label[nr_class] = this_label;
|
|
|
+ count[nr_class] = 1;
|
|
|
+ ++nr_class;
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ //
|
|
|
+ // Labels are ordered by their first occurrence in the train set.
|
|
|
+ // However, for two-class sets with -1/+1 labels and -1 appears first,
|
|
|
+ // we swap labels to ensure that internally the binary SVM has positive data corresponding to the +1 instances.
|
|
|
+ //
|
|
|
+ if (nr_class == 2 && label[0] == -1 && label[1] == +1)
|
|
|
+ {
|
|
|
+ do {int tmp = label[0]; label[0]=label[1]; label[1] = tmp;} while(false);
|
|
|
+ do {int tmp = count[0]; count[0]=count[1]; count[1] = tmp;} while(false);
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ {
|
|
|
+ if(data_label[i] == 0)
|
|
|
+ data_label[i] = 1;
|
|
|
+ else
|
|
|
+ data_label[i] = 0;
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ int[] start = new int[nr_class];
|
|
|
+ start[0] = 0;
|
|
|
+ for(i=1;i<nr_class;i++)
|
|
|
+ start[i] = start[i-1]+count[i-1];
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ {
|
|
|
+ perm[start[data_label[i]]] = i;
|
|
|
+ ++start[data_label[i]];
|
|
|
+ }
|
|
|
+ start[0] = 0;
|
|
|
+ for(i=1;i<nr_class;i++)
|
|
|
+ start[i] = start[i-1]+count[i-1];
|
|
|
+
|
|
|
+ nr_class_ret[0] = nr_class;
|
|
|
+ label_ret[0] = label;
|
|
|
+ start_ret[0] = start;
|
|
|
+ count_ret[0] = count;
|
|
|
+ }
|
|
|
+
|
|
|
+ //
|
|
|
+ // Interface functions
|
|
|
+ //
|
|
|
+ public static svm_model svm_train(svm_problem prob, svm_parameter param)
|
|
|
+ {
|
|
|
+ svm_model model = new svm_model();
|
|
|
+ model.param = param;
|
|
|
+
|
|
|
+ if(param.svm_type == svm_parameter.ONE_CLASS ||
|
|
|
+ param.svm_type == svm_parameter.EPSILON_SVR ||
|
|
|
+ param.svm_type == svm_parameter.NU_SVR)
|
|
|
+ {
|
|
|
+ // regression or one-class-svm
|
|
|
+ model.nr_class = 2;
|
|
|
+ model.label = null;
|
|
|
+ model.nSV = null;
|
|
|
+ model.probA = null; model.probB = null;
|
|
|
+ model.sv_coef = new double[1][];
|
|
|
+
|
|
|
+ if(param.probability == 1 &&
|
|
|
+ (param.svm_type == svm_parameter.EPSILON_SVR ||
|
|
|
+ param.svm_type == svm_parameter.NU_SVR))
|
|
|
+ {
|
|
|
+ model.probA = new double[1];
|
|
|
+ model.probA[0] = svm_svr_probability(prob,param);
|
|
|
+ }
|
|
|
+
|
|
|
+ decision_function f = svm_train_one(prob,param,0,0);
|
|
|
+ model.rho = new double[1];
|
|
|
+ model.rho[0] = f.rho;
|
|
|
+
|
|
|
+ int nSV = 0;
|
|
|
+ int i;
|
|
|
+ for(i=0;i<prob.l;i++)
|
|
|
+ if(Math.abs(f.alpha[i]) > 0) ++nSV;
|
|
|
+ model.l = nSV;
|
|
|
+ model.SV = new SupportVectorMachineNode[nSV][];
|
|
|
+ model.sv_coef[0] = new double[nSV];
|
|
|
+ model.sv_indices = new int[nSV];
|
|
|
+ int j = 0;
|
|
|
+ for(i=0;i<prob.l;i++)
|
|
|
+ if(Math.abs(f.alpha[i]) > 0)
|
|
|
+ {
|
|
|
+ model.SV[j] = prob.x[i];
|
|
|
+ model.sv_coef[0][j] = f.alpha[i];
|
|
|
+ model.sv_indices[j] = i+1;
|
|
|
+ ++j;
|
|
|
+ }
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ // classification
|
|
|
+ int l = prob.l;
|
|
|
+ int[] tmp_nr_class = new int[1];
|
|
|
+ int[][] tmp_label = new int[1][];
|
|
|
+ int[][] tmp_start = new int[1][];
|
|
|
+ int[][] tmp_count = new int[1][];
|
|
|
+ int[] perm = new int[l];
|
|
|
+
|
|
|
+ // group train data of the same class
|
|
|
+ svm_group_classes(prob,tmp_nr_class,tmp_label,tmp_start,tmp_count,perm);
|
|
|
+ int nr_class = tmp_nr_class[0];
|
|
|
+ int[] label = tmp_label[0];
|
|
|
+ int[] start = tmp_start[0];
|
|
|
+ int[] count = tmp_count[0];
|
|
|
+
|
|
|
+ if(nr_class == 1)
|
|
|
+ SupportVectorMachine.info("WARNING: training data in only one class. See README for details.\n");
|
|
|
+
|
|
|
+ SupportVectorMachineNode[][] x = new SupportVectorMachineNode[l][];
|
|
|
+ int i;
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ x[i] = prob.x[perm[i]];
|
|
|
+
|
|
|
+ // calculate weighted C
|
|
|
+
|
|
|
+ double[] weighted_C = new double[nr_class];
|
|
|
+ for(i=0;i<nr_class;i++)
|
|
|
+ weighted_C[i] = param.C;
|
|
|
+ for(i=0;i<param.nr_weight;i++)
|
|
|
+ {
|
|
|
+ int j;
|
|
|
+ for(j=0;j<nr_class;j++)
|
|
|
+ if(param.weight_label[i] == label[j])
|
|
|
+ break;
|
|
|
+ if(j == nr_class)
|
|
|
+ System.err.print("WARNING: class label "+param.weight_label[i]+" specified in weight is not found\n");
|
|
|
+ else
|
|
|
+ weighted_C[j] *= param.weight[i];
|
|
|
+ }
|
|
|
+
|
|
|
+ // train k*(k-1)/2 models
|
|
|
+
|
|
|
+ boolean[] nonzero = new boolean[l];
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ nonzero[i] = false;
|
|
|
+ decision_function[] f = new decision_function[nr_class*(nr_class-1)/2];
|
|
|
+
|
|
|
+ double[] probA=null,probB=null;
|
|
|
+ if (param.probability == 1)
|
|
|
+ {
|
|
|
+ probA=new double[nr_class*(nr_class-1)/2];
|
|
|
+ probB=new double[nr_class*(nr_class-1)/2];
|
|
|
+ }
|
|
|
+
|
|
|
+ int p = 0;
|
|
|
+ for(i=0;i<nr_class;i++)
|
|
|
+ for(int j=i+1;j<nr_class;j++)
|
|
|
+ {
|
|
|
+ svm_problem sub_prob = new svm_problem();
|
|
|
+ int si = start[i], sj = start[j];
|
|
|
+ int ci = count[i], cj = count[j];
|
|
|
+ sub_prob.l = ci+cj;
|
|
|
+ sub_prob.x = new SupportVectorMachineNode[sub_prob.l][];
|
|
|
+ sub_prob.y = new double[sub_prob.l];
|
|
|
+ int k;
|
|
|
+ for(k=0;k<ci;k++)
|
|
|
+ {
|
|
|
+ sub_prob.x[k] = x[si+k];
|
|
|
+ sub_prob.y[k] = +1;
|
|
|
+ }
|
|
|
+ for(k=0;k<cj;k++)
|
|
|
+ {
|
|
|
+ sub_prob.x[ci+k] = x[sj+k];
|
|
|
+ sub_prob.y[ci+k] = -1;
|
|
|
+ }
|
|
|
+
|
|
|
+ if(param.probability == 1)
|
|
|
+ {
|
|
|
+ double[] probAB=new double[2];
|
|
|
+ svm_binary_svc_probability(sub_prob,param,weighted_C[i],weighted_C[j],probAB);
|
|
|
+ probA[p]=probAB[0];
|
|
|
+ probB[p]=probAB[1];
|
|
|
+ }
|
|
|
+
|
|
|
+ f[p] = svm_train_one(sub_prob,param,weighted_C[i],weighted_C[j]);
|
|
|
+ for(k=0;k<ci;k++)
|
|
|
+ if(!nonzero[si+k] && Math.abs(f[p].alpha[k]) > 0)
|
|
|
+ nonzero[si+k] = true;
|
|
|
+ for(k=0;k<cj;k++)
|
|
|
+ if(!nonzero[sj+k] && Math.abs(f[p].alpha[ci+k]) > 0)
|
|
|
+ nonzero[sj+k] = true;
|
|
|
+ ++p;
|
|
|
+ }
|
|
|
+
|
|
|
+ // build output
|
|
|
+
|
|
|
+ model.nr_class = nr_class;
|
|
|
+
|
|
|
+ model.label = new int[nr_class];
|
|
|
+ for(i=0;i<nr_class;i++)
|
|
|
+ model.label[i] = label[i];
|
|
|
+
|
|
|
+ model.rho = new double[nr_class*(nr_class-1)/2];
|
|
|
+ for(i=0;i<nr_class*(nr_class-1)/2;i++)
|
|
|
+ model.rho[i] = f[i].rho;
|
|
|
+
|
|
|
+ if(param.probability == 1)
|
|
|
+ {
|
|
|
+ model.probA = new double[nr_class*(nr_class-1)/2];
|
|
|
+ model.probB = new double[nr_class*(nr_class-1)/2];
|
|
|
+ for(i=0;i<nr_class*(nr_class-1)/2;i++)
|
|
|
+ {
|
|
|
+ model.probA[i] = probA[i];
|
|
|
+ model.probB[i] = probB[i];
|
|
|
+ }
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ model.probA=null;
|
|
|
+ model.probB=null;
|
|
|
+ }
|
|
|
+
|
|
|
+ int nnz = 0;
|
|
|
+ int[] nz_count = new int[nr_class];
|
|
|
+ model.nSV = new int[nr_class];
|
|
|
+ for(i=0;i<nr_class;i++)
|
|
|
+ {
|
|
|
+ int nSV = 0;
|
|
|
+ for(int j=0;j<count[i];j++)
|
|
|
+ if(nonzero[start[i]+j])
|
|
|
+ {
|
|
|
+ ++nSV;
|
|
|
+ ++nnz;
|
|
|
+ }
|
|
|
+ model.nSV[i] = nSV;
|
|
|
+ nz_count[i] = nSV;
|
|
|
+ }
|
|
|
+
|
|
|
+ SupportVectorMachine.info("Total nSV = "+nnz+"\n");
|
|
|
+
|
|
|
+ model.l = nnz;
|
|
|
+ model.SV = new SupportVectorMachineNode[nnz][];
|
|
|
+ model.sv_indices = new int[nnz];
|
|
|
+ p = 0;
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ if(nonzero[i])
|
|
|
+ {
|
|
|
+ model.SV[p] = x[i];
|
|
|
+ model.sv_indices[p++] = perm[i] + 1;
|
|
|
+ }
|
|
|
+
|
|
|
+ int[] nz_start = new int[nr_class];
|
|
|
+ nz_start[0] = 0;
|
|
|
+ for(i=1;i<nr_class;i++)
|
|
|
+ nz_start[i] = nz_start[i-1]+nz_count[i-1];
|
|
|
+
|
|
|
+ model.sv_coef = new double[nr_class-1][];
|
|
|
+ for(i=0;i<nr_class-1;i++)
|
|
|
+ model.sv_coef[i] = new double[nnz];
|
|
|
+
|
|
|
+ p = 0;
|
|
|
+ for(i=0;i<nr_class;i++)
|
|
|
+ for(int j=i+1;j<nr_class;j++)
|
|
|
+ {
|
|
|
+ // classifier (i,j): coefficients with
|
|
|
+ // i are in sv_coef[j-1][nz_start[i]...],
|
|
|
+ // j are in sv_coef[i][nz_start[j]...]
|
|
|
+
|
|
|
+ int si = start[i];
|
|
|
+ int sj = start[j];
|
|
|
+ int ci = count[i];
|
|
|
+ int cj = count[j];
|
|
|
+
|
|
|
+ int q = nz_start[i];
|
|
|
+ int k;
|
|
|
+ for(k=0;k<ci;k++)
|
|
|
+ if(nonzero[si+k])
|
|
|
+ model.sv_coef[j-1][q++] = f[p].alpha[k];
|
|
|
+ q = nz_start[j];
|
|
|
+ for(k=0;k<cj;k++)
|
|
|
+ if(nonzero[sj+k])
|
|
|
+ model.sv_coef[i][q++] = f[p].alpha[ci+k];
|
|
|
+ ++p;
|
|
|
+ }
|
|
|
+ }
|
|
|
+ return model;
|
|
|
+ }
|
|
|
+
|
|
|
+ // Stratified cross validation
|
|
|
+ public static void svm_cross_validation(svm_problem prob, svm_parameter param, int nr_fold, double[] target)
|
|
|
+ {
|
|
|
+ int i;
|
|
|
+ int[] fold_start = new int[nr_fold+1];
|
|
|
+ int l = prob.l;
|
|
|
+ int[] perm = new int[l];
|
|
|
+
|
|
|
+ // stratified cv may not give leave-one-out rate
|
|
|
+ // Each class to l folds -> some folds may have zero elements
|
|
|
+ if((param.svm_type == svm_parameter.C_SVC ||
|
|
|
+ param.svm_type == svm_parameter.NU_SVC) && nr_fold < l)
|
|
|
+ {
|
|
|
+ int[] tmp_nr_class = new int[1];
|
|
|
+ int[][] tmp_label = new int[1][];
|
|
|
+ int[][] tmp_start = new int[1][];
|
|
|
+ int[][] tmp_count = new int[1][];
|
|
|
+
|
|
|
+ svm_group_classes(prob,tmp_nr_class,tmp_label,tmp_start,tmp_count,perm);
|
|
|
+
|
|
|
+ int nr_class = tmp_nr_class[0];
|
|
|
+ int[] start = tmp_start[0];
|
|
|
+ int[] count = tmp_count[0];
|
|
|
+
|
|
|
+ // naive shuffle and then data grouped by fold using the array perm
|
|
|
+ int[] fold_count = new int[nr_fold];
|
|
|
+ int c;
|
|
|
+ int[] index = new int[l];
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ index[i]=perm[i];
|
|
|
+ for (c=0; c<nr_class; c++)
|
|
|
+ for(i=0;i<count[c];i++)
|
|
|
+ {
|
|
|
+ int j = i+rand.nextInt(count[c]-i);
|
|
|
+ do {int tmp = index[start[c]+j]; index[start[c]+j]=index[start[c]+i]; index[start[c]+i] = tmp;} while(false);
|
|
|
+ }
|
|
|
+ for(i=0;i<nr_fold;i++)
|
|
|
+ {
|
|
|
+ fold_count[i] = 0;
|
|
|
+ for (c=0; c<nr_class;c++)
|
|
|
+ fold_count[i]+=(i+1)*count[c]/nr_fold-i*count[c]/nr_fold;
|
|
|
+ }
|
|
|
+ fold_start[0]=0;
|
|
|
+ for (i=1;i<=nr_fold;i++)
|
|
|
+ fold_start[i] = fold_start[i-1]+fold_count[i-1];
|
|
|
+ for (c=0; c<nr_class;c++)
|
|
|
+ for(i=0;i<nr_fold;i++)
|
|
|
+ {
|
|
|
+ int begin = start[c]+i*count[c]/nr_fold;
|
|
|
+ int end = start[c]+(i+1)*count[c]/nr_fold;
|
|
|
+ for(int j=begin;j<end;j++)
|
|
|
+ {
|
|
|
+ perm[fold_start[i]] = index[j];
|
|
|
+ fold_start[i]++;
|
|
|
+ }
|
|
|
+ }
|
|
|
+ fold_start[0]=0;
|
|
|
+ for (i=1;i<=nr_fold;i++)
|
|
|
+ fold_start[i] = fold_start[i-1]+fold_count[i-1];
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ for(i=0;i<l;i++) perm[i]=i;
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ {
|
|
|
+ int j = i+rand.nextInt(l-i);
|
|
|
+ do {int tmp = perm[i]; perm[i]=perm[j]; perm[j] = tmp;} while(false);
|
|
|
+ }
|
|
|
+ for(i=0;i<=nr_fold;i++)
|
|
|
+ fold_start[i]=i*l/nr_fold;
|
|
|
+ }
|
|
|
+
|
|
|
+ for(i=0;i<nr_fold;i++)
|
|
|
+ {
|
|
|
+ int begin = fold_start[i];
|
|
|
+ int end = fold_start[i+1];
|
|
|
+ int j,k;
|
|
|
+ svm_problem subprob = new svm_problem();
|
|
|
+
|
|
|
+ subprob.l = l-(end-begin);
|
|
|
+ subprob.x = new SupportVectorMachineNode[subprob.l][];
|
|
|
+ subprob.y = new double[subprob.l];
|
|
|
+
|
|
|
+ k=0;
|
|
|
+ for(j=0;j<begin;j++)
|
|
|
+ {
|
|
|
+ subprob.x[k] = prob.x[perm[j]];
|
|
|
+ subprob.y[k] = prob.y[perm[j]];
|
|
|
+ ++k;
|
|
|
+ }
|
|
|
+ for(j=end;j<l;j++)
|
|
|
+ {
|
|
|
+ subprob.x[k] = prob.x[perm[j]];
|
|
|
+ subprob.y[k] = prob.y[perm[j]];
|
|
|
+ ++k;
|
|
|
+ }
|
|
|
+ svm_model submodel = svm_train(subprob,param);
|
|
|
+ if(param.probability==1 &&
|
|
|
+ (param.svm_type == svm_parameter.C_SVC ||
|
|
|
+ param.svm_type == svm_parameter.NU_SVC))
|
|
|
+ {
|
|
|
+ double[] prob_estimates= new double[svm_get_nr_class(submodel)];
|
|
|
+ for(j=begin;j<end;j++)
|
|
|
+ target[perm[j]] = svm_predict_probability(submodel,prob.x[perm[j]],prob_estimates);
|
|
|
+ }
|
|
|
+ else
|
|
|
+ for(j=begin;j<end;j++)
|
|
|
+ target[perm[j]] = svm_predict(submodel,prob.x[perm[j]]);
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ public static int svm_get_svm_type(svm_model model)
|
|
|
+ {
|
|
|
+ return model.param.svm_type;
|
|
|
+ }
|
|
|
+
|
|
|
+ public static int svm_get_nr_class(svm_model model)
|
|
|
+ {
|
|
|
+ return model.nr_class;
|
|
|
+ }
|
|
|
+
|
|
|
+ public static void svm_get_labels(svm_model model, int[] label)
|
|
|
+ {
|
|
|
+ if (model.label != null)
|
|
|
+ for(int i=0;i<model.nr_class;i++)
|
|
|
+ label[i] = model.label[i];
|
|
|
+ }
|
|
|
+
|
|
|
+ public static void svm_get_sv_indices(svm_model model, int[] indices)
|
|
|
+ {
|
|
|
+ if (model.sv_indices != null)
|
|
|
+ for(int i=0;i<model.l;i++)
|
|
|
+ indices[i] = model.sv_indices[i];
|
|
|
+ }
|
|
|
+
|
|
|
+ public static int svm_get_nr_sv(svm_model model)
|
|
|
+ {
|
|
|
+ return model.l;
|
|
|
+ }
|
|
|
+
|
|
|
+ public static double svm_get_svr_probability(svm_model model)
|
|
|
+ {
|
|
|
+ if ((model.param.svm_type == svm_parameter.EPSILON_SVR || model.param.svm_type == svm_parameter.NU_SVR) &&
|
|
|
+ model.probA!=null)
|
|
|
+ return model.probA[0];
|
|
|
+ else
|
|
|
+ {
|
|
|
+ System.err.print("Model doesn't contain information for SVR probability inference\n");
|
|
|
+ return 0;
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ public static double svm_predict_values(svm_model model, SupportVectorMachineNode[] x, double[] dec_values)
|
|
|
+ {
|
|
|
+ int i;
|
|
|
+ if(model.param.svm_type == svm_parameter.ONE_CLASS ||
|
|
|
+ model.param.svm_type == svm_parameter.EPSILON_SVR ||
|
|
|
+ model.param.svm_type == svm_parameter.NU_SVR)
|
|
|
+ {
|
|
|
+ double[] sv_coef = model.sv_coef[0];
|
|
|
+ double sum = 0;
|
|
|
+ for(i=0;i<model.l;i++)
|
|
|
+ sum += sv_coef[i] * Kernel.k_function(x,model.SV[i],model.param);
|
|
|
+ sum -= model.rho[0];
|
|
|
+ dec_values[0] = sum;
|
|
|
+
|
|
|
+ ///QQQ dwp
|
|
|
+// if(model.param.svm_type == svm_parameter.ONE_CLASS)
|
|
|
+// return (sum>0)?1:-1;
|
|
|
+// else
|
|
|
+// return sum;
|
|
|
+ return sum;
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ int nr_class = model.nr_class;
|
|
|
+ int l = model.l;
|
|
|
+
|
|
|
+ double[] kvalue = new double[l];
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ kvalue[i] = Kernel.k_function(x,model.SV[i],model.param);
|
|
|
+
|
|
|
+ int[] start = new int[nr_class];
|
|
|
+ start[0] = 0;
|
|
|
+ for(i=1;i<nr_class;i++)
|
|
|
+ start[i] = start[i-1]+model.nSV[i-1];
|
|
|
+
|
|
|
+ int[] vote = new int[nr_class];
|
|
|
+ for(i=0;i<nr_class;i++)
|
|
|
+ vote[i] = 0;
|
|
|
+
|
|
|
+ int p=0;
|
|
|
+ for(i=0;i<nr_class;i++)
|
|
|
+ for(int j=i+1;j<nr_class;j++)
|
|
|
+ {
|
|
|
+ double sum = 0;
|
|
|
+ int si = start[i];
|
|
|
+ int sj = start[j];
|
|
|
+ int ci = model.nSV[i];
|
|
|
+ int cj = model.nSV[j];
|
|
|
+
|
|
|
+ int k;
|
|
|
+ double[] coef1 = model.sv_coef[j-1];
|
|
|
+ double[] coef2 = model.sv_coef[i];
|
|
|
+ for(k=0;k<ci;k++)
|
|
|
+ sum += coef1[si+k] * kvalue[si+k];
|
|
|
+ for(k=0;k<cj;k++)
|
|
|
+ sum += coef2[sj+k] * kvalue[sj+k];
|
|
|
+ sum -= model.rho[p];
|
|
|
+ dec_values[p] = sum;
|
|
|
+
|
|
|
+ if(dec_values[p] > 0)
|
|
|
+ ++vote[i];
|
|
|
+ else
|
|
|
+ ++vote[j];
|
|
|
+ p++;
|
|
|
+ }
|
|
|
+
|
|
|
+ int vote_max_idx = 0;
|
|
|
+ for(i=1;i<nr_class;i++)
|
|
|
+ if(vote[i] > vote[vote_max_idx])
|
|
|
+ vote_max_idx = i;
|
|
|
+
|
|
|
+ return model.label[vote_max_idx];
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ public static double svm_predict(svm_model model, SupportVectorMachineNode[] x)
|
|
|
+ {
|
|
|
+ int nr_class = model.nr_class;
|
|
|
+ double[] dec_values;
|
|
|
+ if(model.param.svm_type == svm_parameter.ONE_CLASS ||
|
|
|
+ model.param.svm_type == svm_parameter.EPSILON_SVR ||
|
|
|
+ model.param.svm_type == svm_parameter.NU_SVR)
|
|
|
+ dec_values = new double[1];
|
|
|
+ else
|
|
|
+ dec_values = new double[nr_class*(nr_class-1)/2];
|
|
|
+ double pred_result = svm_predict_values(model, x, dec_values);
|
|
|
+ return pred_result;
|
|
|
+ }
|
|
|
+
|
|
|
+ public static double svm_predict_probability(svm_model model, SupportVectorMachineNode[] x, double[] prob_estimates)
|
|
|
+ {
|
|
|
+ if ((model.param.svm_type == svm_parameter.C_SVC || model.param.svm_type == svm_parameter.NU_SVC) &&
|
|
|
+ model.probA!=null && model.probB!=null)
|
|
|
+ {
|
|
|
+ int i;
|
|
|
+ int nr_class = model.nr_class;
|
|
|
+ double[] dec_values = new double[nr_class*(nr_class-1)/2];
|
|
|
+ svm_predict_values(model, x, dec_values);
|
|
|
+
|
|
|
+ double min_prob=1e-7;
|
|
|
+ double[][] pairwise_prob=new double[nr_class][nr_class];
|
|
|
+
|
|
|
+ int k=0;
|
|
|
+ for(i=0;i<nr_class;i++)
|
|
|
+ for(int j=i+1;j<nr_class;j++)
|
|
|
+ {
|
|
|
+ pairwise_prob[i][j]=Math.min(Math.max(sigmoid_predict(dec_values[k],model.probA[k],model.probB[k]),min_prob),1-min_prob);
|
|
|
+ pairwise_prob[j][i]=1-pairwise_prob[i][j];
|
|
|
+ k++;
|
|
|
+ }
|
|
|
+ multiclass_probability(nr_class,pairwise_prob,prob_estimates);
|
|
|
+
|
|
|
+ int prob_max_idx = 0;
|
|
|
+ for(i=1;i<nr_class;i++)
|
|
|
+ if(prob_estimates[i] > prob_estimates[prob_max_idx])
|
|
|
+ prob_max_idx = i;
|
|
|
+ return model.label[prob_max_idx];
|
|
|
+ }
|
|
|
+ else
|
|
|
+ return svm_predict(model, x);
|
|
|
+ }
|
|
|
+
|
|
|
+ static final String svm_type_table[] =
|
|
|
+ {
|
|
|
+ "c_svc","nu_svc","one_class","epsilon_svr","nu_svr",
|
|
|
+ };
|
|
|
+
|
|
|
+ static final String kernel_type_table[]=
|
|
|
+ {
|
|
|
+ "linear","polynomial","rbf","sigmoid","precomputed"
|
|
|
+ };
|
|
|
+
|
|
|
+ public static void svm_save_model(String model_file_name, svm_model model) throws IOException
|
|
|
+ {
|
|
|
+ DataOutputStream fp = new DataOutputStream(new BufferedOutputStream(new FileOutputStream(model_file_name)));
|
|
|
+
|
|
|
+ svm_parameter param = model.param;
|
|
|
+
|
|
|
+ fp.writeBytes("svm_type "+svm_type_table[param.svm_type]+"\n");
|
|
|
+ fp.writeBytes("kernel_type "+kernel_type_table[param.kernel_type]+"\n");
|
|
|
+
|
|
|
+ if(param.kernel_type == svm_parameter.POLY)
|
|
|
+ fp.writeBytes("degree "+param.degree+"\n");
|
|
|
+
|
|
|
+ if(param.kernel_type == svm_parameter.POLY ||
|
|
|
+ param.kernel_type == svm_parameter.RBF ||
|
|
|
+ param.kernel_type == svm_parameter.SIGMOID)
|
|
|
+ fp.writeBytes("gamma "+param.gamma+"\n");
|
|
|
+
|
|
|
+ if(param.kernel_type == svm_parameter.POLY ||
|
|
|
+ param.kernel_type == svm_parameter.SIGMOID)
|
|
|
+ fp.writeBytes("coef0 "+param.coef0+"\n");
|
|
|
+
|
|
|
+ int nr_class = model.nr_class;
|
|
|
+ int l = model.l;
|
|
|
+ fp.writeBytes("nr_class "+nr_class+"\n");
|
|
|
+ fp.writeBytes("total_sv "+l+"\n");
|
|
|
+
|
|
|
+ {
|
|
|
+ fp.writeBytes("rho");
|
|
|
+ for(int i=0;i<nr_class*(nr_class-1)/2;i++)
|
|
|
+ fp.writeBytes(" "+model.rho[i]);
|
|
|
+ fp.writeBytes("\n");
|
|
|
+ }
|
|
|
+
|
|
|
+ if(model.label != null)
|
|
|
+ {
|
|
|
+ fp.writeBytes("label");
|
|
|
+ for(int i=0;i<nr_class;i++)
|
|
|
+ fp.writeBytes(" "+model.label[i]);
|
|
|
+ fp.writeBytes("\n");
|
|
|
+ }
|
|
|
+
|
|
|
+ if(model.probA != null) // regression has probA only
|
|
|
+ {
|
|
|
+ fp.writeBytes("probA");
|
|
|
+ for(int i=0;i<nr_class*(nr_class-1)/2;i++)
|
|
|
+ fp.writeBytes(" "+model.probA[i]);
|
|
|
+ fp.writeBytes("\n");
|
|
|
+ }
|
|
|
+ if(model.probB != null)
|
|
|
+ {
|
|
|
+ fp.writeBytes("probB");
|
|
|
+ for(int i=0;i<nr_class*(nr_class-1)/2;i++)
|
|
|
+ fp.writeBytes(" "+model.probB[i]);
|
|
|
+ fp.writeBytes("\n");
|
|
|
+ }
|
|
|
+
|
|
|
+ if(model.nSV != null)
|
|
|
+ {
|
|
|
+ fp.writeBytes("nr_sv");
|
|
|
+ for(int i=0;i<nr_class;i++)
|
|
|
+ fp.writeBytes(" "+model.nSV[i]);
|
|
|
+ fp.writeBytes("\n");
|
|
|
+ }
|
|
|
+
|
|
|
+ fp.writeBytes("SV\n");
|
|
|
+ double[][] sv_coef = model.sv_coef;
|
|
|
+ SupportVectorMachineNode[][] SV = model.SV;
|
|
|
+
|
|
|
+ for(int i=0;i<l;i++)
|
|
|
+ {
|
|
|
+ for(int j=0;j<nr_class-1;j++)
|
|
|
+ fp.writeBytes(sv_coef[j][i]+" ");
|
|
|
+
|
|
|
+ SupportVectorMachineNode[] p = SV[i];
|
|
|
+ if(param.kernel_type == svm_parameter.PRECOMPUTED)
|
|
|
+ fp.writeBytes("0:"+(int)(p[0].value));
|
|
|
+ else
|
|
|
+ for(int j=0;j<p.length;j++)
|
|
|
+ fp.writeBytes(p[j].index+":"+p[j].value+" ");
|
|
|
+ fp.writeBytes("\n");
|
|
|
+ }
|
|
|
+
|
|
|
+ fp.close();
|
|
|
+ }
|
|
|
+
|
|
|
+ private static double atof(String s)
|
|
|
+ {
|
|
|
+ return Double.valueOf(s).doubleValue();
|
|
|
+ }
|
|
|
+
|
|
|
+ private static int atoi(String s)
|
|
|
+ {
|
|
|
+ return Integer.parseInt(s);
|
|
|
+ }
|
|
|
+
|
|
|
+ private static boolean read_model_header(BufferedReader fp, svm_model model)
|
|
|
+ {
|
|
|
+ svm_parameter param = new svm_parameter();
|
|
|
+ model.param = param;
|
|
|
+ try
|
|
|
+ {
|
|
|
+ while(true)
|
|
|
+ {
|
|
|
+ String cmd = fp.readLine();
|
|
|
+ if(cmd == null) continue;
|
|
|
+
|
|
|
+ String arg = cmd.substring(cmd.indexOf(' ')+1);
|
|
|
+
|
|
|
+ if(cmd.startsWith("svm_type"))
|
|
|
+ {
|
|
|
+ int i;
|
|
|
+ for(i=0;i<svm_type_table.length;i++)
|
|
|
+ {
|
|
|
+ if(arg.indexOf(svm_type_table[i])!=-1)
|
|
|
+ {
|
|
|
+ param.svm_type=i;
|
|
|
+ break;
|
|
|
+ }
|
|
|
+ }
|
|
|
+ if(i == svm_type_table.length)
|
|
|
+ {
|
|
|
+ System.err.print("unknown svm type.\n");
|
|
|
+ return false;
|
|
|
+ }
|
|
|
+ }
|
|
|
+ else if(cmd.startsWith("kernel_type"))
|
|
|
+ {
|
|
|
+ int i;
|
|
|
+ for(i=0;i<kernel_type_table.length;i++)
|
|
|
+ {
|
|
|
+ if(arg.indexOf(kernel_type_table[i])!=-1)
|
|
|
+ {
|
|
|
+ param.kernel_type=i;
|
|
|
+ break;
|
|
|
+ }
|
|
|
+ }
|
|
|
+ if(i == kernel_type_table.length)
|
|
|
+ {
|
|
|
+ System.err.print("unknown kernel function.\n");
|
|
|
+ return false;
|
|
|
+ }
|
|
|
+ }
|
|
|
+ else if(cmd.startsWith("degree"))
|
|
|
+ param.degree = atoi(arg);
|
|
|
+ else if(cmd.startsWith("gamma"))
|
|
|
+ param.gamma = atof(arg);
|
|
|
+ else if(cmd.startsWith("coef0"))
|
|
|
+ param.coef0 = atof(arg);
|
|
|
+ else if(cmd.startsWith("nr_class"))
|
|
|
+ model.nr_class = atoi(arg);
|
|
|
+ else if(cmd.startsWith("total_sv"))
|
|
|
+ model.l = atoi(arg);
|
|
|
+ else if(cmd.startsWith("rho"))
|
|
|
+ {
|
|
|
+ int n = model.nr_class * (model.nr_class-1)/2;
|
|
|
+ model.rho = new double[n];
|
|
|
+ StringTokenizer st = new StringTokenizer(arg);
|
|
|
+ for(int i=0;i<n;i++)
|
|
|
+ model.rho[i] = atof(st.nextToken());
|
|
|
+ }
|
|
|
+ else if(cmd.startsWith("label"))
|
|
|
+ {
|
|
|
+ int n = model.nr_class;
|
|
|
+ model.label = new int[n];
|
|
|
+ StringTokenizer st = new StringTokenizer(arg);
|
|
|
+ for(int i=0;i<n;i++)
|
|
|
+ model.label[i] = atoi(st.nextToken());
|
|
|
+ }
|
|
|
+ else if(cmd.startsWith("probA"))
|
|
|
+ {
|
|
|
+ int n = model.nr_class*(model.nr_class-1)/2;
|
|
|
+ model.probA = new double[n];
|
|
|
+ StringTokenizer st = new StringTokenizer(arg);
|
|
|
+ for(int i=0;i<n;i++)
|
|
|
+ model.probA[i] = atof(st.nextToken());
|
|
|
+ }
|
|
|
+ else if(cmd.startsWith("probB"))
|
|
|
+ {
|
|
|
+ int n = model.nr_class*(model.nr_class-1)/2;
|
|
|
+ model.probB = new double[n];
|
|
|
+ StringTokenizer st = new StringTokenizer(arg);
|
|
|
+ for(int i=0;i<n;i++)
|
|
|
+ model.probB[i] = atof(st.nextToken());
|
|
|
+ }
|
|
|
+ else if(cmd.startsWith("nr_sv"))
|
|
|
+ {
|
|
|
+ int n = model.nr_class;
|
|
|
+ model.nSV = new int[n];
|
|
|
+ StringTokenizer st = new StringTokenizer(arg);
|
|
|
+ for(int i=0;i<n;i++)
|
|
|
+ model.nSV[i] = atoi(st.nextToken());
|
|
|
+ }
|
|
|
+ else if(cmd.startsWith("SV"))
|
|
|
+ {
|
|
|
+ break;
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ System.err.print("unknown text in model file: ["+cmd+"]\n");
|
|
|
+ return false;
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+ catch(RuntimeException e) {
|
|
|
+ throw e;
|
|
|
+ } catch(Exception e) {
|
|
|
+ return false;
|
|
|
+ }
|
|
|
+ return true;
|
|
|
+ }
|
|
|
+
|
|
|
+ public static svm_model svm_load_model(String model_file_name) throws IOException
|
|
|
+ {
|
|
|
+ return svm_load_model(new BufferedReader(new InputStreamReader(new FileInputStream(model_file_name), "UTF-8")));
|
|
|
+ }
|
|
|
+
|
|
|
+ public static svm_model svm_load_model(BufferedReader fp) throws IOException
|
|
|
+ {
|
|
|
+ // read parameters
|
|
|
+
|
|
|
+ svm_model model = new svm_model();
|
|
|
+ model.rho = null;
|
|
|
+ model.probA = null;
|
|
|
+ model.probB = null;
|
|
|
+ model.label = null;
|
|
|
+ model.nSV = null;
|
|
|
+
|
|
|
+ if (read_model_header(fp, model) == false)
|
|
|
+ {
|
|
|
+ System.err.print("ERROR: failed to read model\n");
|
|
|
+ return null;
|
|
|
+ }
|
|
|
+
|
|
|
+ // read sv_coef and SV
|
|
|
+
|
|
|
+ int m = model.nr_class - 1;
|
|
|
+ int l = model.l;
|
|
|
+ model.sv_coef = new double[m][l];
|
|
|
+ model.SV = new SupportVectorMachineNode[l][];
|
|
|
+
|
|
|
+ for(int i=0;i<l;i++)
|
|
|
+ {
|
|
|
+ String line = fp.readLine();
|
|
|
+
|
|
|
+ if(line != null) {
|
|
|
+ StringTokenizer st = new StringTokenizer(line, " \t\n\r\f:");
|
|
|
+
|
|
|
+ for (int k = 0; k < m; k++)
|
|
|
+ model.sv_coef[k][i] = atof(st.nextToken());
|
|
|
+ int n = st.countTokens() / 2;
|
|
|
+ model.SV[i] = new SupportVectorMachineNode[n];
|
|
|
+ for (int j = 0; j < n; j++) {
|
|
|
+ model.SV[i][j] = new SupportVectorMachineNode();
|
|
|
+ model.SV[i][j].index = atoi(st.nextToken());
|
|
|
+ model.SV[i][j].value = atof(st.nextToken());
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ fp.close();
|
|
|
+ return model;
|
|
|
+ }
|
|
|
+
|
|
|
+ public static String svm_check_parameter(svm_problem prob, svm_parameter param)
|
|
|
+ {
|
|
|
+ // svm_type
|
|
|
+
|
|
|
+ int svm_type = param.svm_type;
|
|
|
+ if(svm_type != svm_parameter.C_SVC &&
|
|
|
+ svm_type != svm_parameter.NU_SVC &&
|
|
|
+ svm_type != svm_parameter.ONE_CLASS &&
|
|
|
+ svm_type != svm_parameter.EPSILON_SVR &&
|
|
|
+ svm_type != svm_parameter.NU_SVR)
|
|
|
+ return "unknown svm type";
|
|
|
+
|
|
|
+ // kernel_type, degree
|
|
|
+
|
|
|
+ int kernel_type = param.kernel_type;
|
|
|
+ if(kernel_type != svm_parameter.LINEAR &&
|
|
|
+ kernel_type != svm_parameter.POLY &&
|
|
|
+ kernel_type != svm_parameter.RBF &&
|
|
|
+ kernel_type != svm_parameter.SIGMOID &&
|
|
|
+ kernel_type != svm_parameter.PRECOMPUTED)
|
|
|
+ return "unknown kernel type";
|
|
|
+
|
|
|
+ if(param.gamma < 0)
|
|
|
+ return "gamma < 0";
|
|
|
+
|
|
|
+ if(param.degree < 0)
|
|
|
+ return "degree of polynomial kernel < 0";
|
|
|
+
|
|
|
+ // cache_size,eps,C,nu,p,shrinking
|
|
|
+
|
|
|
+ if(param.cache_size <= 0)
|
|
|
+ return "cache_size <= 0";
|
|
|
+
|
|
|
+ if(param.eps <= 0)
|
|
|
+ return "eps <= 0";
|
|
|
+
|
|
|
+ if(svm_type == svm_parameter.C_SVC ||
|
|
|
+ svm_type == svm_parameter.EPSILON_SVR ||
|
|
|
+ svm_type == svm_parameter.NU_SVR)
|
|
|
+ if(param.C <= 0)
|
|
|
+ return "C <= 0";
|
|
|
+
|
|
|
+ if(svm_type == svm_parameter.NU_SVC ||
|
|
|
+ svm_type == svm_parameter.ONE_CLASS ||
|
|
|
+ svm_type == svm_parameter.NU_SVR)
|
|
|
+ if(param.nu <= 0 || param.nu > 1)
|
|
|
+ return "nu <= 0 or nu > 1";
|
|
|
+
|
|
|
+ if(svm_type == svm_parameter.EPSILON_SVR)
|
|
|
+ if(param.p < 0)
|
|
|
+ return "p < 0";
|
|
|
+
|
|
|
+ if(param.shrinking != 0 &&
|
|
|
+ param.shrinking != 1)
|
|
|
+ return "shrinking != 0 and shrinking != 1";
|
|
|
+
|
|
|
+ if(param.probability != 0 &&
|
|
|
+ param.probability != 1)
|
|
|
+ return "probability != 0 and probability != 1";
|
|
|
+
|
|
|
+ if(param.probability == 1 &&
|
|
|
+ svm_type == svm_parameter.ONE_CLASS)
|
|
|
+ return "one-class SVM probability output not supported yet";
|
|
|
+
|
|
|
+ // check whether nu-svc is feasible
|
|
|
+
|
|
|
+ if(svm_type == svm_parameter.NU_SVC)
|
|
|
+ {
|
|
|
+ int l = prob.l;
|
|
|
+ int max_nr_class = 16;
|
|
|
+ int nr_class = 0;
|
|
|
+ int[] label = new int[max_nr_class];
|
|
|
+ int[] count = new int[max_nr_class];
|
|
|
+
|
|
|
+ int i;
|
|
|
+ for(i=0;i<l;i++)
|
|
|
+ {
|
|
|
+ int this_label = (int)prob.y[i];
|
|
|
+ int j;
|
|
|
+ for(j=0;j<nr_class;j++)
|
|
|
+ if(this_label == label[j])
|
|
|
+ {
|
|
|
+ ++count[j];
|
|
|
+ break;
|
|
|
+ }
|
|
|
+
|
|
|
+ if(j == nr_class)
|
|
|
+ {
|
|
|
+ if(nr_class == max_nr_class)
|
|
|
+ {
|
|
|
+ max_nr_class *= 2;
|
|
|
+ int[] new_data = new int[max_nr_class];
|
|
|
+ System.arraycopy(label,0,new_data,0,label.length);
|
|
|
+ label = new_data;
|
|
|
+
|
|
|
+ new_data = new int[max_nr_class];
|
|
|
+ System.arraycopy(count,0,new_data,0,count.length);
|
|
|
+ count = new_data;
|
|
|
+ }
|
|
|
+ label[nr_class] = this_label;
|
|
|
+ count[nr_class] = 1;
|
|
|
+ ++nr_class;
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ for(i=0;i<nr_class;i++)
|
|
|
+ {
|
|
|
+ int n1 = count[i];
|
|
|
+ for(int j=i+1;j<nr_class;j++)
|
|
|
+ {
|
|
|
+ int n2 = count[j];
|
|
|
+ if(param.nu*(n1+n2)/2 > Math.min(n1,n2))
|
|
|
+ return "specified nu is infeasible";
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ return null;
|
|
|
+ }
|
|
|
+
|
|
|
+ public static int svm_check_probability_model(svm_model model)
|
|
|
+ {
|
|
|
+ if (((model.param.svm_type == svm_parameter.C_SVC || model.param.svm_type == svm_parameter.NU_SVC) &&
|
|
|
+ model.probA!=null && model.probB!=null) ||
|
|
|
+ ((model.param.svm_type == svm_parameter.EPSILON_SVR || model.param.svm_type == svm_parameter.NU_SVR) &&
|
|
|
+ model.probA!=null))
|
|
|
+ return 1;
|
|
|
+ else
|
|
|
+ return 0;
|
|
|
+ }
|
|
|
+
|
|
|
+ public static void svm_set_print_string_function(svm_print_interface print_func)
|
|
|
+ {
|
|
|
+ if (print_func == null)
|
|
|
+ svm_print_string = svm_print_stdout;
|
|
|
+ else
|
|
|
+ svm_print_string = print_func;
|
|
|
+ }
|
|
|
+}
|