Lines Matching full:alpha
233 double alpha, double beta )
245 results[j] = (Qfloat)(s*alpha + beta);
339 // min [0.5(\alpha^T Q \alpha) + b^T \alpha]
341 // y^T \alpha = \delta
348 // Q, b, y, Cp, Cn, and an initial feasible point \alpha
352 // solution will be put in \alpha, objective value will be put in obj
358 alpha = 0;
418 alpha = _alpha;
589 alpha_status[i] = (schar)(alpha[i] >= get_C(i) ? 1 : alpha[i] <= 0 ? -1 : 0)
600 // 1. initialize gradient and alpha status
614 double alpha_i = alpha[i];
635 if( fabs(alpha[i]) > 1e16 )
649 alpha_i = old_alpha_i = alpha[i];
650 alpha_j = old_alpha_j = alpha[j];
713 // update alpha
714 alpha[i] = alpha_i;
715 alpha[j] = alpha_j;
732 si.obj += alpha[i] * (G[i] + b[i]);
967 alpha[i] = 0;
975 alpha[i] *= y[i];
1000 alpha[i] = MIN(1.0, sum_pos);
1001 sum_pos -= alpha[i];
1005 alpha[i] = MIN(1.0, sum_neg);
1006 sum_neg -= alpha[i];
1017 alpha[i] *= y[i]*inv_r;
1047 alpha[i] = i < n ? 1 : 0;
1051 alpha[n] = nu * sample_count - n;
1053 alpha[n-1] = nu * sample_count - (n-1);
1072 alpha = (double*)cvMemStorageAlloc( storage, alpha_count*sizeof(alpha[0]) );
1076 alpha[i] = 0;
1080 alpha[i+sample_count] = 0;
1089 _alpha[i] = alpha[i] - alpha[i+sample_count];
1108 alpha = (double*)cvMemStorageAlloc( storage, alpha_count*sizeof(alpha[0]) );
1113 alpha[i] = alpha[i + sample_count] = MIN(sum, C);
1114 sum -= alpha[i];
1127 _alpha[i] = alpha[i] - alpha[i+sample_count];
1284 CvMemStorage* _storage, double* alpha, double& rho )
1298 Cp, Cn, _storage, kernel, alpha, si ) :
1300 _storage, kernel, alpha, si ) :
1302 _storage, kernel, alpha, si ) :
1304 _storage, kernel, alpha, si ) :
1306 _storage, kernel, alpha, si ) : false;
1317 const CvMat* responses, CvMemStorage* temp_storage, double* alpha )
1338 responses->data.i, 0, 0, temp_storage, alpha, df->rho ))
1342 sv_count += fabs(alpha[i]) > 0;
1345 CV_CALL( df->alpha = (double*)cvMemStorageAlloc( storage, sv_count*sizeof(df->alpha[0])) );
1350 if( fabs(alpha[i]) > 0 )
1354 df->alpha[k++] = alpha[i];
1447 Cp, Cn, temp_storage, alpha, df->rho ))
1451 sv_count += fabs(alpha[k]) > 0;
1455 CV_CALL( df->alpha = (double*)cvMemStorageAlloc( temp_storage,
1456 sv_count*sizeof(df->alpha[0])));
1462 if( fabs(alpha[k]) > 0 )
1466 df->alpha[k1++] = alpha[k];
1472 if( fabs(alpha[ci + k]) > 0 )
1476 df->alpha[k1++] = alpha[ci + k];
1539 double* alpha;
1566 CV_CALL( alpha = (double*)cvMemStorageAlloc(temp_storage, sample_count*sizeof(double)));
1571 if( !do_train( svm_type, sample_count, var_count, samples, responses, temp_storage, alpha ))
1607 double* alpha;
1718 CV_CALL(alpha = (double*)cvMemStorageAlloc(temp_storage, sample_count*sizeof(double)));
1806 (const float**)samples_local, responses_local, temp_storage, alpha ) )
1859 CV_CALL(ok = do_train( svm_type, sample_count, var_count, samples, responses, temp_storage, alpha ));
1921 sum += buffer[i]*df->alpha[i];
1942 sum += df->alpha[k]*buffer[df->sv_index[k]];
2095 cvStartWriteStruct( fs, "alpha", CV_NODE_SEQ+CV_NODE_FLOW );
2096 cvWriteRawData( fs, df[i].alpha, df[i].sv_count, "d" );
2278 CvFileNode* alpha_node = cvGetFileNodeByName( fs, df_elem, "alpha" );
2290 CV_ERROR( CV_StsParseError, "alpha is missing in the decision function" );
2292 CV_CALL( df[i].alpha = (double*)cvMemStorageAlloc( storage,
2293 sv_count*sizeof(df[i].alpha[0])));
2296 CV_CALL( cvReadRawData( fs, alpha_node, df[i].alpha, "d" ));
2382 CV_CALL( ddf->alpha = (double*)cvMemStorageAlloc( dst->storage,
2383 sv_count*sizeof(ddf->alpha[0])));
2384 memcpy( ddf->alpha, sdf->alpha, sv_count*sizeof(ddf->alpha[0]));
