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00010 #include <shogun/classifier/AveragedPerceptron.h>
00011 #include <shogun/features/Labels.h>
00012 #include <shogun/mathematics/Math.h>
00013
00014 using namespace shogun;
00015
00016 CAveragedPerceptron::CAveragedPerceptron()
00017 : CLinearMachine(), learn_rate(0.1), max_iter(1000)
00018 {
00019 }
00020
00021 CAveragedPerceptron::CAveragedPerceptron(CDotFeatures* traindat, CLabels* trainlab)
00022 : CLinearMachine(), learn_rate(.1), max_iter(1000)
00023 {
00024 set_features(traindat);
00025 set_labels(trainlab);
00026 }
00027
00028 CAveragedPerceptron::~CAveragedPerceptron()
00029 {
00030 }
00031
00032 bool CAveragedPerceptron::train(CFeatures* data)
00033 {
00034 ASSERT(labels);
00035 if (data)
00036 {
00037 if (!data->has_property(FP_DOT))
00038 SG_ERROR("Specified features are not of type CDotFeatures\n");
00039 set_features((CDotFeatures*) data);
00040 }
00041 ASSERT(features);
00042 bool converged=false;
00043 int32_t iter=0;
00044 SGVector<int32_t> train_labels=labels->get_int_labels();
00045 int32_t num_feat=features->get_dim_feature_space();
00046 int32_t num_vec=features->get_num_vectors();
00047
00048 ASSERT(num_vec==train_labels.vlen);
00049 SG_FREE(w);
00050 w_dim=num_feat;
00051 w=SG_MALLOC(float64_t, num_feat);
00052 float64_t* tmp_w=SG_MALLOC(float64_t, num_feat);
00053
00054 float64_t* output=SG_MALLOC(float64_t, num_vec);
00055
00056 bias=0;
00057 float64_t tmp_bias=0;
00058 for (int32_t i=0; i<num_feat; i++)
00059 w[i]=1.0/num_feat;
00060
00061
00062
00063 while (!converged && iter<max_iter)
00064 {
00065 converged=true;
00066 for (int32_t i=0; i<num_vec; i++)
00067 {
00068 output[i]=apply(i);
00069
00070 if (CMath::sign<float64_t>(output[i]) != train_labels.vector[i])
00071 {
00072 converged=false;
00073 bias+=learn_rate*train_labels.vector[i];
00074 features->add_to_dense_vec(learn_rate*train_labels.vector[i], i, w, w_dim);
00075 }
00076
00077
00078
00079 for (int32_t j=0; j<num_feat; j++)
00080 tmp_w[j]+=w[j];
00081 tmp_bias+=bias;
00082 }
00083 iter++;
00084 }
00085
00086 if (converged)
00087 SG_INFO("Averaged Perceptron algorithm converged after %d iterations.\n", iter);
00088 else
00089 SG_WARNING("Averaged Perceptron algorithm did not converge after %d iterations.\n", max_iter);
00090
00091
00092 for (int32_t i=0; i<num_feat; i++)
00093 w[i]=tmp_w[i]/(num_vec*iter);
00094 bias=tmp_bias/(num_vec*iter);
00095
00096 SG_FREE(output);
00097 train_labels.free_vector();
00098 SG_FREE(tmp_w);
00099
00100 return converged;
00101 }