made everything compile and even run somehow
This commit is contained in:
Vadim Pisarevsky
@@ -179,10 +179,7 @@ build_rtrees_classifier( const string& data_filename,
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// create classifier by using <data> and <responses>
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cout << "Training the classifier ...\n";
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Ptr<TrainData> tdata = prepare_train_data(data, responses, ntrain_samples);
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// 3. train classifier
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model = RTrees::create(RTrees::Params(10,10,0,false,15,Mat(),true,4,TC(100,0.01f)));
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model->train( tdata );
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model = StatModel::train<RTrees>(tdata, RTrees::Params(10,10,0,false,15,Mat(),true,4,TC(100,0.01f)));
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cout << endl;
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}
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@@ -267,10 +264,12 @@ build_boost_classifier( const string& data_filename,
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Ptr<TrainData> tdata = TrainData::create(new_data, ROW_SAMPLE, new_responses,
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noArray(), noArray(), noArray(), var_type);
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model = Boost::create(Boost::Params(Boost::REAL, 100, 0.95, 5, false, Mat() ));
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vector<double> priors(2);
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priors[0] = 1;
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priors[1] = 26;
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cout << "Training the classifier (may take a few minutes)...\n";
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model->train(tdata);
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model = StatModel::train<Boost>(tdata, Boost::Params(Boost::GENTLE, 100, 0.95, 5, false, Mat(priors) ));
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cout << endl;
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}
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@@ -333,7 +332,6 @@ build_mlp_classifier( const string& data_filename,
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if( !ok )
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return ok;
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int i, j;
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Ptr<ANN_MLP> model;
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int nsamples_all = data.rows;
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@@ -360,14 +358,14 @@ build_mlp_classifier( const string& data_filename,
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// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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Mat train_data = data.rowRange(0, ntrain_samples);
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Mat new_responses = Mat::zeros( ntrain_samples, class_count, CV_32F );
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Mat train_responses = Mat::zeros( ntrain_samples, class_count, CV_32F );
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// 1. unroll the responses
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cout << "Unrolling the responses...\n";
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for( i = 0; i < ntrain_samples; i++ )
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for( int i = 0; i < ntrain_samples; i++ )
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{
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int cls_label = responses.at<int>(i) - 'A'
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new_responses.at<float>(i, cls_label) = 1.f;
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int cls_label = responses.at<int>(i) - 'A';
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train_responses.at<float>(i, cls_label) = 1.f;
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}
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// 2. train classifier
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@@ -385,180 +383,63 @@ build_mlp_classifier( const string& data_filename,
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int max_iter = 1000;
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#endif
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mlp.train( &train_data, new_responses, 0, 0,
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ANN_MLP::Params(TC(max_iter,0), method, method_param));
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Ptr<TrainData> tdata = TrainData::create(train_data, ROW_SAMPLE, train_responses);
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model = ANN_MLP::create() mlp.create( &layer_sizes );
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printf( "Training the classifier (may take a few minutes)...\n");
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cvReleaseMat( &new_responses );
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printf("\n");
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cout << "Training the classifier (may take a few minutes)...\n";
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model = StatModel::train<ANN_MLP>(tdata, ANN_MLP::Params(layer_sizes, ANN_MLP::SIGMOID_SYM, 0, 0, TC(max_iter,0), method, method_param));
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cout << endl;
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}
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Mat mlp_response;
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// compute prediction error on train and test data
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for( i = 0; i < nsamples_all; i++ )
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{
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int best_class;
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CvMat sample;
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cvGetRow( data, &sample, i );
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CvPoint max_loc;
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mlp.predict( &sample, mlp_response );
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cvMinMaxLoc( mlp_response, 0, 0, 0, &max_loc, 0 );
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best_class = max_loc.x + 'A';
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int r = fabs((double)best_class - responses->data.fl[i]) < FLT_EPSILON ? 1 : 0;
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if( i < ntrain_samples )
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train_hr += r;
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else
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test_hr += r;
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}
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test_hr /= (double)(nsamples_all-ntrain_samples);
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train_hr /= (double)ntrain_samples;
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printf( "Recognition rate: train = %.1f%%, test = %.1f%%\n",
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train_hr*100., test_hr*100. );
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if( !filename_to_save.empty() )
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model->save( filename_to_save );
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test_and_save_classifier(model, data, responses, ntrain_samples, 'A', filename_to_save);
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return true;
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}
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static bool
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build_knearest_classifier( const string& data_filename, int K )
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{
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const int var_count = 16;
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Mat data;
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CvMat train_data;
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Mat responses;
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bool ok = read_num_class_data( data_filename, 16, &data, &responses );
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if( !ok )
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return ok;
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int nsamples_all = 0, ntrain_samples = 0;
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Ptr<KNearest> model;
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nsamples_all = data->rows;
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ntrain_samples = (int)(nsamples_all*0.8);
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int nsamples_all = data.rows;
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int ntrain_samples = (int)(nsamples_all*0.8);
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// 1. unroll the responses
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printf( "Unrolling the responses...\n");
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cvGetRows( data, &train_data, 0, ntrain_samples );
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// create classifier by using <data> and <responses>
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cout << "Training the classifier ...\n";
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Ptr<TrainData> tdata = prepare_train_data(data, responses, ntrain_samples);
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model = StatModel::train<KNearest>(tdata, KNearest::Params(K, true));
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cout << endl;
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// 2. train classifier
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Mat train_resp = cvCreateMat( ntrain_samples, 1, CV_32FC1);
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for (int i = 0; i < ntrain_samples; i++)
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train_resp->data.fl[i] = responses->data.fl[i];
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Ptr<KNearest> model = KNearest::create(true);
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model->train(train_data, train_resp);
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Mat nearests = cvCreateMat( (nsamples_all - ntrain_samples), K, CV_32FC1);
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float* _sample = new float[var_count * (nsamples_all - ntrain_samples)];
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CvMat sample = cvMat( nsamples_all - ntrain_samples, 16, CV_32FC1, _sample );
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float* true_results = new float[nsamples_all - ntrain_samples];
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for (int j = ntrain_samples; j < nsamples_all; j++)
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{
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float *s = data->data.fl + j * var_count;
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for (int i = 0; i < var_count; i++)
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{
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sample.data.fl[(j - ntrain_samples) * var_count + i] = s[i];
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}
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true_results[j - ntrain_samples] = responses->data.fl[j];
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}
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CvMat *result = cvCreateMat(1, nsamples_all - ntrain_samples, CV_32FC1);
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knearest.find_nearest(&sample, K, result, 0, nearests, 0);
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int true_resp = 0;
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int accuracy = 0;
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for (int i = 0; i < nsamples_all - ntrain_samples; i++)
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{
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if (result->data.fl[i] == true_results[i])
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true_resp++;
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for(int k = 0; k < K; k++ )
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{
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if( nearests->data.fl[i * K + k] == true_results[i])
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accuracy++;
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}
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}
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printf("true_resp = %f%%\tavg accuracy = %f%%\n", (float)true_resp / (nsamples_all - ntrain_samples) * 100,
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(float)accuracy / (nsamples_all - ntrain_samples) / K * 100);
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delete[] true_results;
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delete[] _sample;
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cvReleaseMat( &train_resp );
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cvReleaseMat( &nearests );
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cvReleaseMat( &result );
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cvReleaseMat( &data );
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cvReleaseMat( &responses );
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return 0;
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test_and_save_classifier(model, data, responses, ntrain_samples, 0, string());
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return true;
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}
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static bool
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build_nbayes_classifier( const string& data_filename )
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{
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const int var_count = 16;
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Mat data;
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CvMat train_data;
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Mat responses;
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bool ok = read_num_class_data( data_filename, 16, &data, &responses );
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if( !ok )
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return ok;
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int nsamples_all = 0, ntrain_samples = 0;
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Ptr<NormalBayesClassifier> model;
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nsamples_all = data->rows;
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ntrain_samples = (int)(nsamples_all*0.5);
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int nsamples_all = data.rows;
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int ntrain_samples = (int)(nsamples_all*0.8);
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// 1. unroll the responses
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printf( "Unrolling the responses...\n");
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cvGetRows( data, &train_data, 0, ntrain_samples );
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// create classifier by using <data> and <responses>
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cout << "Training the classifier ...\n";
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Ptr<TrainData> tdata = prepare_train_data(data, responses, ntrain_samples);
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model = StatModel::train<NormalBayesClassifier>(tdata, NormalBayesClassifier::Params());
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cout << endl;
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// 2. train classifier
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Mat train_resp = cvCreateMat( ntrain_samples, 1, CV_32FC1);
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for (int i = 0; i < ntrain_samples; i++)
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train_resp->data.fl[i] = responses->data.fl[i];
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CvNormalBayesClassifier nbayes(&train_data, train_resp);
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float* _sample = new float[var_count * (nsamples_all - ntrain_samples)];
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CvMat sample = cvMat( nsamples_all - ntrain_samples, 16, CV_32FC1, _sample );
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float* true_results = new float[nsamples_all - ntrain_samples];
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for (int j = ntrain_samples; j < nsamples_all; j++)
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{
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float *s = data->data.fl + j * var_count;
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for (int i = 0; i < var_count; i++)
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{
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sample.data.fl[(j - ntrain_samples) * var_count + i] = s[i];
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}
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true_results[j - ntrain_samples] = responses->data.fl[j];
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}
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CvMat *result = cvCreateMat(1, nsamples_all - ntrain_samples, CV_32FC1);
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nbayes.predict(&sample, result);
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int true_resp = 0;
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//int accuracy = 0;
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for (int i = 0; i < nsamples_all - ntrain_samples; i++)
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{
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if (result->data.fl[i] == true_results[i])
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true_resp++;
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}
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printf("true_resp = %f%%\n", (float)true_resp / (nsamples_all - ntrain_samples) * 100);
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delete[] true_results;
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delete[] _sample;
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cvReleaseMat( &train_resp );
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cvReleaseMat( &result );
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cvReleaseMat( &data );
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cvReleaseMat( &responses );
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return 0;
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test_and_save_classifier(model, data, responses, ntrain_samples, 0, string());
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return true;
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}
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static bool
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@@ -568,95 +449,47 @@ build_svm_classifier( const string& data_filename,
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{
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Mat data;
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Mat responses;
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Mat train_resp;
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CvMat train_data;
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int nsamples_all = 0, ntrain_samples = 0;
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int var_count;
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Ptr<SVM> model;
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bool ok = read_num_class_data( data_filename, 16, &data, &responses );
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if( !ok )
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return ok;
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////////// SVM parameters ///////////////////////////////
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CvSVMParams param;
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param.kernel_type=CvSVM::LINEAR;
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param.svm_type=CvSVM::C_SVC;
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param.C=1;
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///////////////////////////////////////////////////////////
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Ptr<SVM> model;
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printf( "The database %s is loaded.\n", data_filename );
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nsamples_all = data->rows;
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ntrain_samples = (int)(nsamples_all*0.1);
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var_count = data->cols;
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int nsamples_all = data.rows;
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int ntrain_samples = (int)(nsamples_all*0.8);
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// Create or load Random Trees classifier
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if( filename_to_load )
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if( !filename_to_load.empty() )
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{
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// load classifier from the specified file
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svm.load( filename_to_load );
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model = load_classifier<SVM>(filename_to_load);
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if( model.empty() )
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return false;
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ntrain_samples = 0;
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if( svm.get_var_count() == 0 )
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{
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printf( "Could not read the classifier %s\n", filename_to_load );
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return -1;
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}
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printf( "The classifier %s is loaded.\n", filename_to_load );
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}
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else
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{
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// train classifier
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printf( "Training the classifier (may take a few minutes)...\n");
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cvGetRows( data, &train_data, 0, ntrain_samples );
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train_resp = cvCreateMat( ntrain_samples, 1, CV_32FC1);
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for (int i = 0; i < ntrain_samples; i++)
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train_resp->data.fl[i] = responses->data.fl[i];
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svm.train(&train_data, train_resp, 0, 0, param);
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// create classifier by using <data> and <responses>
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cout << "Training the classifier ...\n";
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Ptr<TrainData> tdata = prepare_train_data(data, responses, ntrain_samples);
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SVM::Params params;
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params.svmType = SVM::C_SVC;
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params.kernelType = SVM::LINEAR;
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params.C = 1;
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model = StatModel::train<SVM>(tdata, params);
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cout << endl;
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}
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// classification
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std::vector<float> _sample(var_count * (nsamples_all - ntrain_samples));
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CvMat sample = cvMat( nsamples_all - ntrain_samples, 16, CV_32FC1, &_sample[0] );
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std::vector<float> true_results(nsamples_all - ntrain_samples);
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for (int j = ntrain_samples; j < nsamples_all; j++)
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{
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float *s = data->data.fl + j * var_count;
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for (int i = 0; i < var_count; i++)
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{
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sample.data.fl[(j - ntrain_samples) * var_count + i] = s[i];
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}
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true_results[j - ntrain_samples] = responses->data.fl[j];
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}
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CvMat *result = cvCreateMat(1, nsamples_all - ntrain_samples, CV_32FC1);
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printf("Classification (may take a few minutes)...\n");
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double t = (double)cvGetTickCount();
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svm.predict(&sample, result);
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t = (double)cvGetTickCount() - t;
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printf("Prediction type: %gms\n", t/(cvGetTickFrequency()*1000.));
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int true_resp = 0;
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for (int i = 0; i < nsamples_all - ntrain_samples; i++)
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{
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if (result->data.fl[i] == true_results[i])
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true_resp++;
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}
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printf("true_resp = %f%%\n", (float)true_resp / (nsamples_all - ntrain_samples) * 100);
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if( !filename_to_save.empty() )
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model->save( filename_to_save );
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test_and_save_classifier(model, data, responses, ntrain_samples, 0, filename_to_save);
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return true;
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}
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int main( int argc, char *argv[] )
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{
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char* filename_to_save = 0;
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char* filename_to_load = 0;
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char default_data_filename[] = "./letter-recognition.data";
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char* data_filename = default_data_filename;
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string filename_to_save = "";
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string filename_to_load = "";
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string data_filename = "./letter-recognition.data";
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int method = 0;
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int i;
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@@ -685,15 +518,15 @@ int main( int argc, char *argv[] )
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{
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method = 2;
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}
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else if ( strcmp(argv[i], "-knearest") == 0)
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else if( strcmp(argv[i], "-knearest") == 0 || strcmp(argv[i], "-knn") == 0 )
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{
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method = 3;
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}
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else if ( strcmp(argv[i], "-nbayes") == 0)
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else if( strcmp(argv[i], "-nbayes") == 0)
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{
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method = 4;
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}
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else if ( strcmp(argv[i], "-svm") == 0)
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else if( strcmp(argv[i], "-svm") == 0)
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{
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method = 5;
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}
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