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📄 partitiontreenode.cpp

📁 dysii is a C++ library for distributed probabilistic inference and learning in large-scale dynamical
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#include "PartitionTreeNode.hpp"using namespace indii::ml::aux;PartitionTreeNode::PartitionTreeNode() : size(0), left(NULL), right(NULL) {  //}PartitionTreeNode::PartitionTreeNode(const unsigned int i,    const unsigned int depth) : flagLeaf(true), flagPrune(false),    flagInternal(false), depth(depth), size(1), i(i), is(0), left(NULL),    right(NULL) {  //}PartitionTreeNode::PartitionTreeNode(const std::vector<unsigned int>& is,    const unsigned int depth) : flagLeaf(false), flagPrune(true),    flagInternal(false), depth(depth), size(is.size()), i(0), is(is),    left(NULL), right(NULL) {  /* note that prune nodes with zero components are permitted, and indeed   * required for DiracMixturePdf::redistributeBySpace() */ }     PartitionTreeNode::PartitionTreeNode(PartitionTreeNode* left,    PartitionTreeNode* right, const unsigned int depth) : flagLeaf(false),    flagPrune(false), flagInternal(true), depth(depth),    size(left->getSize() + right->getSize()), i(0), is(0), left(left),    right(right) {  //}PartitionTreeNode::PartitionTreeNode(const PartitionTreeNode& o) {  flagLeaf = o.flagLeaf;  flagPrune = o.flagPrune;  flagInternal = o.flagInternal;  depth = o.depth;  size = o.size;  i = o.i;  is = o.is;    if (o.left == NULL) {    left = NULL;  } else {    left = o.left->clone();  }    if (o.right == NULL) {    right = NULL;  } else {    right = o.right->clone();  }}PartitionTreeNode::~PartitionTreeNode() {  delete left;  delete right;}PartitionTreeNode& PartitionTreeNode::operator=(const PartitionTreeNode& o) {  flagLeaf = o.flagLeaf;  flagPrune = o.flagPrune;  flagInternal = o.flagInternal;  depth = o.depth;  size = o.size;  i = o.i;  is = o.is;    delete left;  delete right;    if (o.left == NULL) {    left = NULL;  } else {    left = o.left->clone();  }    if (o.right == NULL) {    right = NULL;  } else {    right = o.right->clone();  }    return *this;}

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