svm.java

SVM是一种常用的模式分类机器学习算法

			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 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 void svm_predict_values(svm_model model, svm_node[] x, 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)
		{
			double[] sv_coef = model.sv_coef[0];
			double sum = 0;
			for(int 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;
		}
		else
		{
			int i;
			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 p=0;
			int pos=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[pos++] = sum;					
				}
		}
	}

	public static double svm_predict(svm_model model, svm_node[] x)
	{
		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[] res = new double[1];
			svm_predict_values(model, x, res);

			if(model.param.svm_type == svm_parameter.ONE_CLASS)
				return (res[0]>0)?1:-1;
			else
				return res[0];
		}
		else
		{
			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);

			int[] vote = new int[nr_class];
			for(i=0;i<nr_class;i++)
				vote[i] = 0;
			int pos=0;
			for(i=0;i<nr_class;i++)
				for(int j=i+1;j<nr_class;j++)
				{
					if(dec_values[pos++] > 0)
						++vote[i];
					else
						++vote[j];
				}

			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_probability(svm_model model, svm_node[] 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 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;
		svm_node[][] 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]+" ");

			svm_node[] 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);
	}

	public static svm_model svm_load_model(String model_file_name) throws IOException
	{
		BufferedReader fp = new BufferedReader(new FileReader(model_file_name));

		// read parameters

		svm_model model = new svm_model();
		svm_parameter param = new svm_parameter();
		model.param = param;
		model.rho = null;
		model.probA = null;
		model.probB = null;
		model.label = null;
		model.nSV = null;

		while(true)
		{
			String cmd = fp.readLine();
			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 null;
				}
			}
			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 null;
				}
			}
			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\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 svm_node[l][];

		for(int i=0;i<l;i++)
		{
			String line = fp.readLine();
			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 svm_node[n];
			for(int j=0;j<n;j++)
			{
				model.SV[i][j] = new svm_node();
				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.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;
	}
}