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