00001
00002
00003
00004
00005
00006
00007
00008
00009
00010
00011 #include "lib/common.h"
00012
00013 #ifdef HAVE_LAPACK
00014 #include "classifier/Classifier.h"
00015 #include "classifier/LinearClassifier.h"
00016 #include "classifier/LDA.h"
00017 #include "features/Labels.h"
00018 #include "lib/Mathematics.h"
00019 #include "lib/lapack.h"
00020
00021 CLDA::CLDA(float64_t gamma)
00022 : CLinearClassifier(), m_gamma(gamma)
00023 {
00024 }
00025
00026 CLDA::CLDA(float64_t gamma, CRealFeatures* traindat, CLabels* trainlab)
00027 : CLinearClassifier(), m_gamma(gamma)
00028 {
00029 set_features(traindat);
00030 set_labels(trainlab);
00031 }
00032
00033
00034 CLDA::~CLDA()
00035 {
00036 }
00037
00038 bool CLDA::train()
00039 {
00040 ASSERT(labels);
00041 ASSERT(features);
00042 int32_t num_train_labels=0;
00043 int32_t* train_labels=labels->get_int_labels(num_train_labels);
00044 ASSERT(train_labels);
00045
00046 int32_t num_feat=features->get_num_features();
00047 int32_t num_vec=features->get_num_vectors();
00048 ASSERT(num_vec==num_train_labels);
00049
00050 int32_t* classidx_neg=new int32_t[num_vec];
00051 int32_t* classidx_pos=new int32_t[num_vec];
00052
00053 int32_t i=0;
00054 int32_t j=0;
00055 int32_t num_neg=0;
00056 int32_t num_pos=0;
00057 for (i=0; i<num_train_labels; i++)
00058 {
00059 if (train_labels[i]==-1)
00060 classidx_neg[num_neg++]=i;
00061 else if (train_labels[i]==+1)
00062 classidx_pos[num_pos++]=i;
00063 else
00064 {
00065 SG_ERROR( "found label != +/- 1 bailing...");
00066 return false;
00067 }
00068 }
00069
00070 if (num_neg<=0 && num_pos<=0)
00071 {
00072 SG_ERROR( "whooooo ? only a single class found\n");
00073 return false;
00074 }
00075
00076 delete[] w;
00077 w=new float64_t[num_feat];
00078 w_dim=num_feat;
00079
00080 float64_t* mean_neg=new float64_t[num_feat];
00081 memset(mean_neg,0,num_feat*sizeof(float64_t));
00082
00083 float64_t* mean_pos=new float64_t[num_feat];
00084 memset(mean_pos,0,num_feat*sizeof(float64_t));
00085
00086
00087 double* scatter=new double[num_feat*num_feat];
00088 double* buffer=new double[num_feat*CMath::max(num_neg, num_pos)];
00089 int nf = (int) num_feat;
00090
00091
00092 for (i=0; i<num_neg; i++)
00093 {
00094 int32_t vlen;
00095 bool vfree;
00096 float64_t* vec=
00097 features->get_feature_vector(classidx_neg[i], vlen, vfree);
00098 ASSERT(vec);
00099
00100 for (j=0; j<vlen; j++)
00101 {
00102 mean_neg[j]+=vec[j];
00103 buffer[num_feat*i+j]=vec[j];
00104 }
00105
00106 features->free_feature_vector(vec, classidx_neg[i], vfree);
00107 }
00108
00109 for (j=0; j<num_feat; j++)
00110 mean_neg[j]/=num_neg;
00111
00112 for (i=0; i<num_neg; i++)
00113 {
00114 for (j=0; j<num_feat; j++)
00115 buffer[num_feat*i+j]-=mean_neg[j];
00116 }
00117 cblas_dgemm(CblasColMajor, CblasNoTrans, CblasTrans, nf, nf,
00118 (int) num_neg, 1.0, buffer, nf, buffer, nf, 0, scatter, nf);
00119
00120
00121 for (i=0; i<num_pos; i++)
00122 {
00123 int32_t vlen;
00124 bool vfree;
00125 float64_t* vec=
00126 features->get_feature_vector(classidx_pos[i], vlen, vfree);
00127 ASSERT(vec);
00128
00129 for (j=0; j<vlen; j++)
00130 {
00131 mean_pos[j]+=vec[j];
00132 buffer[num_feat*i+j]=vec[j];
00133 }
00134
00135 features->free_feature_vector(vec, classidx_pos[i], vfree);
00136 }
00137
00138 for (j=0; j<num_feat; j++)
00139 mean_pos[j]/=num_pos;
00140
00141 for (i=0; i<num_pos; i++)
00142 {
00143 for (j=0; j<num_feat; j++)
00144 buffer[num_feat*i+j]-=mean_pos[j];
00145 }
00146 cblas_dgemm(CblasColMajor, CblasNoTrans, CblasTrans, nf, nf, (int) num_pos,
00147 1.0/(num_train_labels-1), buffer, nf, buffer, nf,
00148 1.0/(num_train_labels-1), scatter, nf);
00149
00150 float64_t trace=CMath::trace((float64_t*) scatter, num_feat, num_feat);
00151
00152 double s=1.0-m_gamma;
00153 for (i=0; i<num_feat*num_feat; i++)
00154 scatter[i]*=s;
00155
00156 for (i=0; i<num_feat; i++)
00157 scatter[i*num_feat+i]+= trace*m_gamma/num_feat;
00158
00159 double* inv_scatter= (double*) CMath::pinv(
00160 scatter, num_feat, num_feat, NULL);
00161
00162 float64_t* w_pos=buffer;
00163 float64_t* w_neg=&buffer[num_feat];
00164
00165 cblas_dsymv(CblasColMajor, CblasUpper, nf, 1.0, inv_scatter, nf,
00166 (double*) mean_pos, 1, 0., (double*) w_pos, 1);
00167 cblas_dsymv(CblasColMajor, CblasUpper, nf, 1.0, inv_scatter, nf,
00168 (double*) mean_neg, 1, 0, (double*) w_neg, 1);
00169
00170 bias=0.5*(CMath::dot(w_neg, mean_neg, num_feat)-CMath::dot(w_pos, mean_pos, num_feat));
00171 for (i=0; i<num_feat; i++)
00172 w[i]=w_pos[i]-w_neg[i];
00173
00174 #ifdef DEBUG_LDA
00175 SG_PRINT("bias: %f\n", bias);
00176 CMath::display_vector(w, num_feat, "w");
00177 CMath::display_vector(w_pos, num_feat, "w_pos");
00178 CMath::display_vector(w_neg, num_feat, "w_neg");
00179 CMath::display_vector(mean_pos, num_feat, "mean_pos");
00180 CMath::display_vector(mean_neg, num_feat, "mean_neg");
00181 #endif
00182
00183 delete[] train_labels;
00184 delete[] mean_neg;
00185 delete[] mean_pos;
00186 delete[] scatter;
00187 delete[] inv_scatter;
00188 delete[] classidx_neg;
00189 delete[] classidx_pos;
00190 delete[] buffer;
00191 return true;
00192 }
00193 #endif