distance

Posted 2020-08-09 BelFuture

 1 /*
 2 * Copyright 2016 E-Tool, Inc.
 3 */
 4 
 5 #ifndef distance_h
 6 #define distance_h
 7 #include <math.h>
 8 #include <vector>
 9 using std::vector;
10 
11 #define max(a,b) (a>b?a:b)
12 #define min(a,b) (a<b?a:b)
13 
14 class Distance {
15 public:
16     static double euclidean_distance(double x1,double y1,double x2,double y2) {
17         return sqrt((x1-x2)*(x1-x2)+(y1-y2)*(y1-y2));
18     }
19     static double euclidean_distance(double x1, double y1, double z1, double x2, double y2,  double z2) {
20         return sqrt((x1 - x2)*(x1 - x2) + (y1 - y2)*(y1 - y2)+(z1-z2)*(z1-z2));
21     }
22     static double euclidean_distance(vector<double> x, vector<double> y) {
23         double result = 0;
24         for (int i = 0;i < x.size();i++) {
25             result += (x[i]-y[i])*(x[i]-y[i]);
26         }
27         return sqrt(result);
28     }
29     static double manhattan_distance(double x1, double y1, double x2, double y2) {
30         return abs(x1 - x2) + abs(y1 - y2);
31     }
32     static double manhattan_distance(vector<double> x, vector<double> y) {
33         double result = 0;
34         for (int i = 0;i < x.size();i++) {
35             result += abs(x[i] - y[i]);
36         }
37         return result;
38     }
39     static double chebyshev_distance(double x1, double y1, double x2, double y2) {
40         return max(abs(x1 - x2) ,abs(y1 - y2));
41     }
42     static double chebyshev_distance(vector<double> x, vector<double> y) {
43         double result=-1;
44         for (int i = 0;i < x.size();i++) {
45             result = max(result, abs(x[i] - y[i]));
46         }
47         return result;
48     }
49     static double minkowski_distance(vector<double> x, vector<double> y,int p) {
50         double result = 0;
51         for (int i = 0;i < x.size();i++) {
52             result += pow(abs(x[i]-y[i]),p);
53         }
54         return pow(result,1/p);
55     }
56     /*
57     *  Standardized Euclidean distance
58     */
59     static double standardized_euclidean_distance(vector<double> x, vector<double> y) {
60         double result = 0;
61         for (int i = 0;i < x.size();i++) {
62             double r=(x[i]+y[i])/2;
63             double s = (x[i]-r)*(x[i]-r)+(y[i]-r)*(y[i]-r);
64             result += (x[i]-y[i])*(x[i]-y[i]) / s;
65         }
66         return sqrt(result);
67     }
68     static double information_entropy(vector<double> p) {
69         double result=0;
70         for (int i = 0;i < p.size();i++) {
71             result -= p[i] * (log(p[i])/log(2));
72         }
73         return result;
74     }
75 };
76 
77 #endif