pointcloud/Sample.cpp at master · awei1999/pointcloud · GitHub

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#include "Sample.h"
#include <ctime>
#include <iostream>

Sample::Sample(const Eigen::MatrixXf &dataset, const Eigen::VectorXi &labels,
			   Eigen::MatrixXi &indexMat, Eigen::MatrixXf &distMat, int numClass, int numFeature):
	_dataset(dataset),
	_labels(labels),
	_indexMat(indexMat),
	_distMat(distMat),
	_numClass(numClass),
	_numFeature(numFeature)
{ }

Sample::Sample(Sample* sample):
	_dataset(sample->_dataset),
	_labels(sample->_labels),
	_indexMat(sample->_indexMat),
	_distMat(sample->_distMat),
	_numClass(sample->_numClass),
	_numFeature(sample->_numFeature),
	_numSelectedSamples(sample->getNumSelectedSamples()),
	_selectedSamplesId(sample->getSelectedSamplesId())
{ }

Sample::Sample(const Sample* sample, Eigen::VectorXi &samplesId) :
	_dataset(sample->_dataset),
	_labels(sample->_labels),
	_indexMat(sample->_indexMat),
	_distMat(sample->_distMat),
	_numClass(sample->_numClass),
	_numFeature(sample->_numFeature),
	_selectedSamplesId(samplesId),
	_numSelectedSamples(samplesId.rows())
{ }

void Sample::randomSampleDataset(float percentage)
{
	// TODO: first to tell if percentage is equal to 1
	int numTotalSamples = _dataset.rows();
	_numSelectedSamples = (int)(numTotalSamples * percentage);
	_selectedSamplesId.resize(_numSelectedSamples);

	// to generate a uniform distribution and sample with replacement
	std::default_random_engine generator;
	std::uniform_int_distribution<int> distribution(0, numTotalSamples-1);

	for (int i = 0; i < _numSelectedSamples; ++i) {
		_selectedSamplesId[i] = distribution(generator);
	}

	// std::cout << _selectedSamples << std::endl;
	// return _selectedSamples;
}

void Sample::randomSampleFeatures()
{
	Features feature;
	// total population
	int numPoints = _indexMat.cols();
	// randomly select two points from neighboorhood
	for (int i = 0; i < _numFeature; ++i)
	{
		Random rd(numPoints, 2);
		std::vector<int> selectedPointId = rd.sampleWithoutReplacement();

		// randomly select one of the features from feature factory
		int selectedFeatType = rand() % 6; // 6 features in the factory now
		feature._point1 = selectedPointId[0];
		feature._point2 = selectedPointId[1];
		feature._featType = selectedFeatType;
		_features.push_back(feature);
	}
}

Eigen::MatrixXf Sample::buildNeighborhood(int pointId) const
{
	// number of points in the neighborhood
	int k = _indexMat.cols();
	// datapoint dimension
	int d = _dataset.cols();
	/*std::cout << "pointID\n";
	std::cout << pointId << std::endl;*/
	Eigen::MatrixXf neighborhood(k, d);
	Eigen::VectorXi candidatePointIndices;
	candidatePointIndices = _indexMat.row(pointId);
	// std::cout << "candidates\n";
	// std::cout << candidatePointIndices << std::endl;
	//neighborhood.row(0) = _dataset.row(pointId);
	for (int i = 0; i < k; ++i) {
		neighborhood.row(i) = _dataset.row(candidatePointIndices[i]);
	}
	return neighborhood;
}