generic-library/opencv
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a big patch; use special proxy types (Input/OutputArray, Input/OutputArrayOfArrays) for passing in vectors, matrices etc.

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This commit is contained in:
Vadim Pisarevsky
2011-04-17 13:14:45 +00:00
parent 335370a7c0
commit abeeb40d46
94 changed files with 10831 additions and 9631 deletions
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7
modules/ml/src/ann_mlp.cpp
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@@ -1228,7 +1228,7 @@ int CvANN_MLP::train_rprop( CvVectors x0, CvVectors u, const double* sw )
__BEGIN__;
int i, ivcount, ovcount, l_count, total = 0, max_iter, buf_sz, dcount0, dcount=0;
int i, ivcount, ovcount, l_count, total = 0, max_iter, buf_sz, dcount0;
double *buf_ptr;
double prev_E = DBL_MAX*0.5, epsilon;
double dw_plus, dw_minus, dw_min, dw_max;
@@ -1294,10 +1294,7 @@ int CvANN_MLP::train_rprop( CvVectors x0, CvVectors u, const double* sw )
*/
for( iter = 0; iter < max_iter; iter++ )
{
int n1, n2, si, j, k;
double* w;
CvMat _w, _dEdw, hdr1, hdr2, ghdr1, ghdr2, _df;
CvMat *x1, *x2, *grad1, *grad2, *temp;
int n1, n2, j, k;
double E = 0;
// first, iterate through all the samples and compute dEdw

12
modules/ml/src/knearest.cpp
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@@ -355,8 +355,6 @@ float CvKNearest::find_nearest( const CvMat* _samples, int k, CvMat* _results,
float result = 0.f;
const int max_blk_count = 128, max_buf_sz = 1 << 12;
int i, count, count_scale, blk_count0, blk_count = 0, buf_sz, k1;
if( !samples )
CV_Error( CV_StsError, "The search tree must be constructed first using train method" );
@@ -395,15 +393,15 @@ float CvKNearest::find_nearest( const CvMat* _samples, int k, CvMat* _results,
"The distances from the neighbors (if present) must be floating-point matrix of <num_samples> x <k> size" );
}
count = _samples->rows;
count_scale = k*2;
blk_count0 = MIN( count, max_blk_count );
buf_sz = MIN( blk_count0 * count_scale, max_buf_sz );
int count = _samples->rows;
int count_scale = k*2;
int blk_count0 = MIN( count, max_blk_count );
int buf_sz = MIN( blk_count0 * count_scale, max_buf_sz );
blk_count0 = MAX( buf_sz/count_scale, 1 );
blk_count0 += blk_count0 % 2;
blk_count0 = MIN( blk_count0, count );
buf_sz = blk_count0 * count_scale + k;
k1 = get_sample_count();
int k1 = get_sample_count();
k1 = MIN( k1, k );
cv::parallel_for(cv::BlockedRange(0, count), P1(this, buf_sz, k, _samples, _neighbors, k1,

6
modules/ml/test/test_emknearestkmeans.cpp
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@@ -203,7 +203,7 @@ void CV_KMeansTest::run( int /*start_from*/ )
int code = cvtest::TS::OK;
Mat bestLabels;
// 1. flag==KMEANS_PP_CENTERS
kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_PP_CENTERS, 0 );
kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_PP_CENTERS, OutputArray() );
if( calcErr( bestLabels, labels, sizes, false ) > 0.01f )
{
ts->printf( cvtest::TS::LOG, "bad accuracy if flag==KMEANS_PP_CENTERS" );
@@ -211,7 +211,7 @@ void CV_KMeansTest::run( int /*start_from*/ )
}
// 2. flag==KMEANS_RANDOM_CENTERS
kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_RANDOM_CENTERS, 0 );
kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_RANDOM_CENTERS, OutputArray() );
if( calcErr( bestLabels, labels, sizes, false ) > 0.01f )
{
ts->printf( cvtest::TS::LOG, "bad accuracy if flag==KMEANS_PP_CENTERS" );
@@ -223,7 +223,7 @@ void CV_KMeansTest::run( int /*start_from*/ )
RNG rng;
for( int i = 0; i < 0.5f * pointsCount; i++ )
bestLabels.at<int>( rng.next() % pointsCount, 0 ) = rng.next() % 3;
kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_USE_INITIAL_LABELS, 0 );
kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_USE_INITIAL_LABELS, OutputArray() );
if( calcErr( bestLabels, labels, sizes, false ) > 0.01f )
{
ts->printf( cvtest::TS::LOG, "bad accuracy if flag==KMEANS_PP_CENTERS" );