int GetMaxDistance(TreeNode* root,int& maxLeft,int& maxRight)
{
if(root == NULL)
{
maxLeft = 0;
maxRight = 0;
return 0;
}
int maxLL,maxLR,maxRL,maxRR;
int maxDistLeft,maxDistRight;
if(root->left != NULL)
{
maxDistLeft = GetMaxDistance(root->left,maxLL,maxLR);
maxLeft = std::max(maxLL,maxLR) + 1;
}
else
{
maxDistLeft = 0;
maxLeft = 0;
}
if(root->right != NULL)
{
maxDistRight = GetMaxDistance(root->right,maxRL,maxRR);
maxRight= std::max(maxRL,maxRR) + 1;
}
else
{
maxDistRight = 0;
maxRight = 0;
}
return std::max(std::max(maxDistLeft,maxDistRight),maxLeft+maxRight);
}
void Test4(){
//情况1:最长路径就在根节点所在的树中
int arr1[] = {1,2,3,4,0,0,0,5,0,0,6,7,0,8,0,0,9};
//情况2:最长路径在根节点所在的子树中
int arr2[] = {1,2,3,4,0,0,5,0,6};
Node_p root1 = CreateBinaryTree(arr1,sizeof(arr1)/sizeof(*arr1),0);
Node_p root2 = CreateBinaryTree(arr2,sizeof(arr2)/sizeof(*arr2),0);
cout<<GetMaxDistance(root1)<<endl; //6
cout<<GetMaxDistance(root2)<<endl; //4
PrevOrder(root1);
InOrder(root1);
PostOrder(root1);
GetTreeMirror(root1);
cout<<endl;
PrevOrder(root1);
InOrder(root1);
PostOrder(root1);
cout<<endl;
PrevOrder(root2);
InOrder(root2);
PostOrder(root2);
GetTreeMirror(root2);
cout<<endl;
PrevOrder(root2);
InOrder(root2);
PostOrder(root2);
}
float Source::GetMaxDistance() const
{
#ifdef HAS_AUDIO_SOURCE
return impl->GetMaxDistance();
#else
throw System::PunkException(L"Audio source is not available");
#endif
}
/**
*@ classify unclassification sample
*/
string CTest_Knn::Classify(struct dataVector Sample,int k)
{
double dist=0;
int maxid=0,i,tmpfreq=1; //freq[k] store the number of each class apperaed in k-nearest nighbour
vector<int> freq;
distanceStruct gND;
string curClassLable = " ";
//memset(&freq,1,sizeof(freq)); //initialized freq[k] to 1
for(i=0; i<k; ++i)
{
freq.push_back(1);
}
//step.1---initalize the distance to MAX_VALUE
for(i=0;i<k;i++)
{
gND.distance = MAX_VALUE;
gNearestDistance.push_back(gND);
}
//step.2---calculate K-nearest neighbour distance
for(i=0;i<curTrainingSetSize;i++)
{
//step.2.1---calculate the distance between unclassification sample and each training sample
dist=euclideanDistance(gTrainingSet[i],Sample);
//step.2.2---get the max distance in gNearestDistance
maxid=GetMaxDistance(k);
//step.2.3---if the dist less than the maxdistance in gNearestDistance£¬it will be one of k-nearest neighbour
if(dist<gNearestDistance[maxid].distance)
{
gNearestDistance[maxid].ID=gTrainingSet[i].ID;
gNearestDistance[maxid].distance=dist;
gNearestDistance[maxid].classLabel=gTrainingSet[i].classLabel;
}
}
//step.3---statistics the number of each class appeared
for(i=0;i<k;i++)
{
for(int j=0;j<k;j++)
{
if((i!=j)&&(gNearestDistance[i].classLabel==gNearestDistance[j].classLabel))
{
freq[i]+=1;
}
}
}
//step.4---chose the class label with maximum frequency
for(i=0;i<k;i++)
{
if(freq[i]>tmpfreq)
{
tmpfreq=freq[i];
curClassLable=gNearestDistance[i].classLabel;
}
}
return curClassLable;
}
