int main (int argc, char* argv[]){
ClassificationDataset dataset;
dataset.setName("PN");
dataset.addClass("positive");
dataset.addClass("negative");
RNG random;
for(int i=0;i<100;i++){
Mat tempMat(1,1,CV_64FC1,1.0);
random.next();
vector<realv> meanValue = vector<realv>(0.0,1);
vector<realv> stdValue = vector<realv>(1.0,1);
random.fill(tempMat,RNG::NORMAL,meanValue,stdValue);
// random.fill(tempMat,RNG::NORMAL,0.0,1.0);
FeatureVector tempVec(tempMat);
if(tempVec[0]>0){
dataset.addSequence(tempVec, "positive");
}
else{
dataset.addSequence(tempVec, "negative");
}
}
// cout << dataset;
cout << dataset.getMean();
cout << dataset.getStandardDeviation();
dataset.save("../xml/pn.xml");
return EXIT_SUCCESS;
}
void spm_bp::RandomAssignRepresentativePixel(const vector<vector<int> >& spPixelsList, int numOfLabels, vector<int>& rePixel)
{
rePixel.resize(numOfLabels);
RNG rng;
int iy;
for (iy = 0; iy < numOfLabels; ++iy) {
rePixel[iy] = spPixelsList[iy][rng.next() % spPixelsList[iy].size()];
}
}
void CV_KMeansTest::run( int /*start_from*/ )
{
const int iters = 100;
int sizesArr[] = { 5000, 7000, 8000 };
int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2];
Mat data( pointsCount, 2, CV_32FC1 ), labels;
vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
vector<Mat> means, covs;
defaultDistribs( means, covs );
generateData( data, labels, sizes, means, covs, CV_32SC1 );
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, noArray() );
if( calcErr( bestLabels, labels, sizes, false ) > 0.01f )
{
ts->printf( cvtest::TS::LOG, "bad accuracy if flag==KMEANS_PP_CENTERS" );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
// 2. flag==KMEANS_RANDOM_CENTERS
kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_RANDOM_CENTERS, noArray() );
if( calcErr( bestLabels, labels, sizes, false ) > 0.01f )
{
ts->printf( cvtest::TS::LOG, "bad accuracy if flag==KMEANS_PP_CENTERS" );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
// 3. flag==KMEANS_USE_INITIAL_LABELS
labels.copyTo( bestLabels );
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, noArray() );
if( calcErr( bestLabels, labels, sizes, false ) > 0.01f )
{
ts->printf( cvtest::TS::LOG, "bad accuracy if flag==KMEANS_PP_CENTERS" );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
ts->set_failed_test_info( code );
}
//==================================
// Update CodeTree
//==================================
void SpineDecoder::expand_wavefront(vector<int> sym_spine){
/* 变量的定义和初始化是按照变量的用途分类的 */
int edge_metric = 0;
int distance = 0;
RNG rng;
SymbolMap mapper;
uint8_t node_symbol = 0;
vector<CodeTreeNode> wavefront = this->wavefront;
vector<CodeTreeNode> new_wavefront;
/* 数组 wavefront 中的每一个元素对应 spine_t 的一个节点 */
vector<CodeTreeNode>::iterator iwave = wavefront.begin();
/* 创建树的过程是一层一层的创建，使用 vector 添加或删除节点时，实现起来比较方便 */
while(iwave != wavefront.end()){
for(int edge=0; edge!=(1<<(this->k)); ++edge){//edge 也就是 mt，这个循环用来遍历可能出现的 k-bit m.
CodeTreeNode new_node;
/* 1. update spine_value */
new_node.spine_value = hash_func(iwave->spine_value, edge);
rng = RNG(new_node.spine_value);
for(int i=0; i!=this->L; ++i){
node_symbol = mapper.map_func(rng.next());
distance = sym_spine[i] - node_symbol;
edge_metric = distance * distance;
}//end for
/* 2.update path_metric */
new_node.path_metric = iwave->path_metric + edge_metric;
/* 3.update path */
new_node.path = iwave->path;
new_node.path.push_back(edge);
/* Finaly, update wavefront */
new_wavefront.push_back(new_node);
}//end for
++iwave;
}//end while
this->wavefront = new_wavefront;
if(!wavefront.empty())
wavefront.clear();
if(!new_wavefront.empty())
new_wavefront.clear();
}
int NearestNeighborTest::checkFind( const Mat& data )
{
int code = CvTS::OK;
int pointsCount = 1000;
float noise = 0.2f;
RNG rng;
Mat points( pointsCount, dims, CV_32FC1 );
Mat results( pointsCount, K, CV_32SC1 );
std::vector<int> fmap( pointsCount );
for( int pi = 0; pi < pointsCount; pi++ )
{
int fi = rng.next() % featuresCount;
fmap[pi] = fi;
for( int d = 0; d < dims; d++ )
points.at<float>(pi, d) = data.at<float>(fi, d) + rng.uniform(0.0f, 1.0f) * noise;
}
code = findNeighbors( points, results );
if( code == CvTS::OK )
{
int correctMatches = 0;
for( int pi = 0; pi < pointsCount; pi++ )
{
if( fmap[pi] == results.at<int>(pi, 0) )
correctMatches++;
}
double correctPerc = correctMatches / (double)pointsCount;
if (correctPerc < .75)
{
ts->printf( CvTS::LOG, "correct_perc = %d\n", correctPerc );
code = CvTS::FAIL_BAD_ACCURACY;
}
}
return code;
}
