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00011 #include <shogun/classifier/svm/SVMLin.h>
00012 #include <shogun/labels/Labels.h>
00013 #include <shogun/mathematics/Math.h>
00014 #include <shogun/lib/external/ssl.h>
00015 #include <shogun/machine/LinearMachine.h>
00016 #include <shogun/features/DotFeatures.h>
00017 #include <shogun/labels/Labels.h>
00018 #include <shogun/labels/BinaryLabels.h>
00019
00020 using namespace shogun;
00021
00022 CSVMLin::CSVMLin()
00023 : CLinearMachine(), C1(1), C2(1), epsilon(1e-5), use_bias(true)
00024 {
00025 }
00026
00027 CSVMLin::CSVMLin(
00028 float64_t C, CDotFeatures* traindat, CLabels* trainlab)
00029 : CLinearMachine(), C1(C), C2(C), epsilon(1e-5), use_bias(true)
00030 {
00031 set_features(traindat);
00032 set_labels(trainlab);
00033 }
00034
00035
00036 CSVMLin::~CSVMLin()
00037 {
00038 }
00039
00040 bool CSVMLin::train_machine(CFeatures* data)
00041 {
00042 ASSERT(m_labels);
00043
00044 if (data)
00045 {
00046 if (!data->has_property(FP_DOT))
00047 SG_ERROR("Specified features are not of type CDotFeatures\n");
00048 set_features((CDotFeatures*) data);
00049 }
00050
00051 ASSERT(features);
00052
00053 SGVector<float64_t> train_labels=((CBinaryLabels*) m_labels)->get_labels();
00054 int32_t num_feat=features->get_dim_feature_space();
00055 int32_t num_vec=features->get_num_vectors();
00056
00057 ASSERT(num_vec==train_labels.vlen);
00058
00059 struct options Options;
00060 struct data Data;
00061 struct vector_double Weights;
00062 struct vector_double Outputs;
00063
00064 Data.l=num_vec;
00065 Data.m=num_vec;
00066 Data.u=0;
00067 Data.n=num_feat+1;
00068 Data.nz=num_feat+1;
00069 Data.Y=train_labels.vector;
00070 Data.features=features;
00071 Data.C = SG_MALLOC(float64_t, Data.l);
00072
00073 Options.algo = SVM;
00074 Options.lambda=1/(2*get_C1());
00075 Options.lambda_u=1/(2*get_C1());
00076 Options.S=10000;
00077 Options.R=0.5;
00078 Options.epsilon = get_epsilon();
00079 Options.cgitermax=10000;
00080 Options.mfnitermax=50;
00081 Options.Cp = get_C2()/get_C1();
00082 Options.Cn = 1;
00083
00084 if (use_bias)
00085 Options.bias=1.0;
00086 else
00087 Options.bias=0.0;
00088
00089 for (int32_t i=0;i<num_vec;i++)
00090 {
00091 if(train_labels.vector[i]>0)
00092 Data.C[i]=Options.Cp;
00093 else
00094 Data.C[i]=Options.Cn;
00095 }
00096 ssl_train(&Data, &Options, &Weights, &Outputs);
00097 ASSERT(Weights.vec && Weights.d==num_feat+1);
00098
00099 float64_t sgn=train_labels.vector[0];
00100 for (int32_t i=0; i<num_feat+1; i++)
00101 Weights.vec[i]*=sgn;
00102
00103 set_w(SGVector<float64_t>(Weights.vec, num_feat));
00104 set_bias(Weights.vec[num_feat]);
00105
00106 SG_FREE(Data.C);
00107 SG_FREE(Outputs.vec);
00108 return true;
00109 }