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00011 #include <shogun/machine/OnlineLinearMachine.h>
00012 #include <shogun/base/Parameter.h>
00013
00014 using namespace shogun;
00015
00016 COnlineLinearMachine::COnlineLinearMachine()
00017 : CMachine(), w_dim(0), w(NULL), bias(0), features(NULL)
00018 {
00019 m_parameters->add_vector(&w, &w_dim, "w", "Parameter vector w.");
00020 SG_ADD(&bias, "bias", "Bias b.", MS_NOT_AVAILABLE);
00021 SG_ADD((CSGObject**) &features, "features",
00022 "Feature object.", MS_NOT_AVAILABLE);
00023 }
00024
00025 COnlineLinearMachine::~COnlineLinearMachine()
00026 {
00027
00028
00029 if (w != NULL)
00030 SG_FREE(w);
00031 SG_UNREF(features);
00032 }
00033
00034 CBinaryLabels* COnlineLinearMachine::apply_binary(CFeatures* data)
00035 {
00036 SGVector<float64_t> outputs = apply_get_outputs(data);
00037 return new CBinaryLabels(outputs);
00038 }
00039
00040 CRegressionLabels* COnlineLinearMachine::apply_regression(CFeatures* data)
00041 {
00042 SGVector<float64_t> outputs = apply_get_outputs(data);
00043 return new CRegressionLabels(outputs);
00044 }
00045
00046 SGVector<float64_t> COnlineLinearMachine::apply_get_outputs(CFeatures* data)
00047 {
00048 if (data)
00049 {
00050 if (!data->has_property(FP_STREAMING_DOT))
00051 SG_ERROR("Specified features are not of type CStreamingDotFeatures\n");
00052
00053 set_features((CStreamingDotFeatures*) data);
00054 }
00055
00056 ASSERT(features);
00057 ASSERT(features->has_property(FP_STREAMING_DOT));
00058
00059 DynArray<float64_t>* labels_dynarray=new DynArray<float64_t>();
00060 int32_t num_labels=0;
00061
00062 features->start_parser();
00063 while (features->get_next_example())
00064 {
00065 float64_t current_lab=features->dense_dot(w, w_dim) + bias;
00066
00067 labels_dynarray->append_element(current_lab);
00068 num_labels++;
00069
00070 features->release_example();
00071 }
00072 features->end_parser();
00073
00074 SGVector<float64_t> labels_array(num_labels);
00075 for (int32_t i=0; i<num_labels; i++)
00076 labels_array.vector[i]=(*labels_dynarray)[i];
00077
00078 return labels_array;
00079 }
00080
00081 float32_t COnlineLinearMachine::apply_one(float32_t* vec, int32_t len)
00082 {
00083 return SGVector<float32_t>::dot(vec, w, len)+bias;
00084 }
00085
00086 float32_t COnlineLinearMachine::apply_to_current_example()
00087 {
00088 return features->dense_dot(w, w_dim)+bias;
00089 }
00090
00091 bool COnlineLinearMachine::train_machine(CFeatures *data)
00092 {
00093 if (data)
00094 {
00095 if (!data->has_property(FP_STREAMING_DOT))
00096 SG_ERROR("Specified features are not of type CStreamingDotFeatures\n");
00097 set_features((CStreamingDotFeatures*) data);
00098 }
00099 start_train();
00100 features->start_parser();
00101 while (features->get_next_example())
00102 {
00103 train_example(features, features->get_label());
00104 features->release_example();
00105 }
00106
00107 features->end_parser();
00108 stop_train();
00109
00110 return true;
00111 }