19 using namespace shogun;
23 std(NULL), num_idx(0), divide_by_std(divide), initialized(false)
42 int32_t num_features = simple_features->get_num_features();
55 for (i=0; i<num_features; i++)
64 for (i=0; i<num_examples; i++)
66 for (j=0; j<num_features; j++)
67 mean[j]+=feature_matrix.
matrix[i*num_features+j];
70 for (j=0; j<num_features; j++)
71 mean[j]/=num_examples;
74 for (i=0; i<num_examples; i++)
76 for (j=0; j<num_features; j++)
81 int32_t* idx_ok=
SG_MALLOC(
int, num_features);
83 for (j=0; j<num_features; j++)
94 SG_INFO(
"Reducing number of features from %i to %i\n", num_features, num_ok) ;
101 for (j=0; j<num_ok; j++)
104 new_mean[j]=
mean[idx_ok[j]];
105 std[j]=sqrt(var[idx_ok[j]]);
138 int32_t num_vectors=0;
139 int32_t num_features=0;
142 SG_INFO(
"get Feature matrix: %ix%i\n", num_vectors, num_features);
143 SG_INFO(
"Preprocessing feature matrix\n");
144 for (int32_t vec=0; vec<num_vectors; vec++)
151 for (int32_t feat=0; feat<
num_idx; feat++)
152 v_dst[feat]=(v_src[
idx[feat]]-
mean[feat])/
std[feat];
156 for (int32_t feat=0; feat<
num_idx; feat++)
157 v_dst[feat]=(v_src[
idx[feat]]-
mean[feat]);
163 SG_INFO(
"new Feature matrix: %ix%i\n", num_vectors, num_features);
180 for (int32_t i=0; i<
num_idx; i++)
185 for (int32_t i=0; i<
num_idx; i++)
192 for (int32_t i=0; i<vector.
vlen; i++)