C++的三角定位法

#include <iostream>

#include <fstream>

#include <sstream>

#include <math.h>

#include <vector>

using namespace std;

struct point

{

    float x,y;

};

float norm (point p) // get the norm of a vector

{

    return pow(pow(p.x,2)+pow(p.y,2),.5);

}

point trilateration(point point1, point point2, point point3, double r1, double r2, double r3) {

    point resultPose;

    //unit vector in a direction from point1 to point 2

    double p2p1Distance = pow(pow(point2.x-point1.x,2) + pow(point2.y-   point1.y,2),0.5);

    point ex = {(point2.x-point1.x)/p2p1Distance, (point2.y-point1.y)/p2p1Distance};

    point aux = {point3.x-point1.x,point3.y-point1.y};

    //signed magnitude of the x component

    double i = ex.x * aux.x + ex.y * aux.y;

    //the unit vector in the y direction.

    point aux2 = { point3.x-point1.x-i*ex.x, point3.y-point1.y-i*ex.y};

    point ey = { aux2.x / norm (aux2), aux2.y / norm (aux2) };

    //the signed magnitude of the y component

    double j = ey.x * aux.x + ey.y * aux.y;

    //coordinates

    double x = (pow(r1,2) - pow(r2,2) + pow(p2p1Distance,2))/ (2 * p2p1Distance);

    double y = (pow(r1,2) - pow(r3,2) + pow(i,2) + pow(j,2))/(2*j) - i*x/j;

    //result coordinates

    double finalX = point1.x+ x*ex.x + y*ey.x;

    double finalY = point1.y+ x*ex.y + y*ey.y;

    resultPose.x = finalX;

    resultPose.y = finalY;

    return resultPose;

}

int main(int argc, char* argv[]){

    point finalPose;

    point p1 = {4.0,4.0};

    point p2 = {9.0,7.0};

    point p3 = {9.0,1.0};

    double r1,r2,r3;

    r1 = 4;

    r2 = 3;

    r3 = 3.25;

    finalPose = trilateration(p1,p2,p3,r1,r2,r3);

    cout<<"X:::  "<<finalPose.x<<endl;

    cout<<"Y:::  "<<finalPose.y<<endl;

}

Machine Learning分類法

Machine Learning分類法有兩篇寫的淺顯昜懂，就不重覆造輪子了，直接看

https://ithelp.ithome.com.tw/articles/10187191

1、Decision Tree

from sklearn.datasets import load_iris

from sklearn import tree

from sklearn.cross_validation import train_test_split

from  sklearn import metrics

# 讀入鳶尾花資料

iris = load_iris()

iris_X = iris.data

iris_y = iris.target

# 切分訓練與測試資料

train_X, test_X, train_y, test_y = train_test_split(iris_X, iris_y, test_size = 0.3)

# 建立分類器

clf = tree.DecisionTreeClassifier()

iris_clf = clf.fit(train_X, train_y)

# 預測

test_y_predicted = iris_clf.predict(test_X)

print(test_y_predicted)

# 標準答案

print(test_y)

# 績效

accuracy = metrics.accuracy_score(test_y, test_y_predicted)

print(accuracy)

2、knn

from sklearn.datasets import load_iris

from sklearn import neighbors

from sklearn.cross_validation import train_test_split

from sklearn import metrics

# 讀入鳶尾花資料

iris = load_iris()

iris_X = iris.data

iris_y = iris.target

# 切分訓練與測試資料

train_X, test_X, train_y, test_y = train_test_split(iris_X, iris_y, test_size = 0.3)

# 建立分類器

clf = neighbors.KNeighborsClassifier()

iris_clf = clf.fit(train_X, train_y)

# 預測

test_y_predicted = iris_clf.predict(test_X)

print(test_y_predicted)

# 標準答案

print(test_y)

2、knn與svm的比較

https://www.jianshu.com/p/d8664bf9adf9

這樣就很清楚分類的用法啦!