-
Notifications
You must be signed in to change notification settings - Fork 0
/
kNN.h
131 lines (118 loc) · 2.87 KB
/
kNN.h
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
#ifndef _KNN_H_
#define _KNN_H_
#include <vector>
#include <cmath>
#include <algorithm>
#include <map>
#include <iterator>
#include <iostream>
using namespace std;
struct valueAndIndices{
double value;
int index;
};
int cmp(const valueAndIndices& a, const valueAndIndices& b){
if (a.value < b.value)
return 1;
else
return 0;
}
class data{
public:
vector<double> _data;
char _label;
data();
data(const data&);
};
data::data(){
//copy(istream_iterator<double>(cin), istream_iterator<double>(), back_inserter(_data));
double tmp;
cin >> _label;
cout << "请输入数据维数: ";
int num;
cin >> num;
while (num--){
cin >> tmp;
_data.push_back(tmp);
}
}
data::data(const data& d){
_label = d._label;
_data = d._data;
}
class trainingDataSet{
public:
vector<data> _dataSet;
vector<char> _labelSet;
trainingDataSet();
};
trainingDataSet::trainingDataSet(){
cout << "有多少组数据: ";
int num;
cin >> num;
while (num--){
data tempData;
_dataSet.push_back(tempData);
_labelSet.push_back(tempData._label);
}
}
class kNN{
data _testData;
trainingDataSet _trainingDataSet;
public:
kNN();
kNN(data aTestData, trainingDataSet aTrainingDataSet) : _testData(aTestData), _trainingDataSet(aTrainingDataSet){}
vector<double> distances();
vector<int> sortDistancesIndices(vector<double>);
map<char, int> classCount(vector<int>, int);
char classify(const map<char, int>&);
};
vector<double> kNN::distances(){
vector<double> retVec;
double sum = 0, aDistance;
for (vector<data>::const_iterator it1 = _trainingDataSet._dataSet.begin(); it1 != _trainingDataSet._dataSet.end(); ++it1){
for (vector<double>::const_iterator it2 = _testData._data.begin(), it3 = it1->_data.begin(); it2 != _testData._data.end(); ++it2){
sum += (*it2 - *it3) * (*it2 - *it3);
++it3;
}
aDistance = sqrt(sum);
retVec.push_back(aDistance);
sum = 0;
}
return retVec;
}
vector<int> kNN::sortDistancesIndices(vector<double> vec){
vector<int> retVec;
vector<valueAndIndices> vecTemp;
for (vector<double>::size_type st = 0; st != vec.size(); ++st){
valueAndIndices v;
v.value = vec[st];
v.index = st;
vecTemp.push_back(v);
}
sort(vecTemp.begin(), vecTemp.end(), cmp);
for (vector<valueAndIndices>::const_iterator it = vecTemp.begin(); it != vecTemp.end(); ++it)
retVec.push_back(it->index);
return retVec;
}
map<char, int> kNN::classCount(vector<int> vec, int k){
map<char, int> retMap;
for (vector<int>::size_type st = 0; st != k; ++st)
++retMap[_trainingDataSet._labelSet[vec[st]]];
return retMap;
}
char kNN::classify(const map<char, int>& m){
char labelResult;
int num;
map<char, int>::const_iterator baseIt = m.begin();
num = baseIt->second;
labelResult = baseIt->first;
for (map<char, int>::const_iterator it = m.begin(); it != m.end(); ++it){
if (it->second > num){
num = it->second;
labelResult = it->first;
}
}
return labelResult;
}
#endif // !_KNN_H_