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]; }
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_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); }
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); }
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); }
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]; }
@Override 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]; }
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); }
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 float[prob.l]; for(int i=0;i<prob.l;i++) QD[i]= (float)kernel_function(i,i); }