JavaScript之数值计算

//两等长数组对应元素之间做减法运算【可拓展：基本运算(+/-*//)】
function array_dif(length,arrayA,arrayB){
var array = new Array();
for(var i = 0;i<length;i++){
array.push(arrayA[i]-arrayB[i]);
//console.log(arrayA[i]-arrayB[i]);
}
return array;
} 
real = [181,135,61,63,53,60,51,5];
interests = [175,145,71,66,57,59,62,9];
array_dif(8,interests,real);

function array_euclideanMetric(size,pointsA,pointsB){//求欧式距离
        var array = new Array();
        for(var i = 0;i<size;i++){
                array.push(Math.sqrt(Math.pow(pointsA[i][0] - pointsB[i][0],2)+Math.pow(pointsA[i][1] - pointsB[i][1],2)));
                console.log("第"+i+"：" + Math.sqrt(Math.pow(pointsA[i][0] - pointsB[i][0],2)+Math.pow(pointsA[i][1] - pointsB[i][1],2)));//
        }
        return array;
}

real_points = [[1,181],[2,135],[3,61],[4,63],[5,53],[6,60],[7,51],[8,5]];
interests_points = [[1,175],[2,145],[3,71],[4,66],[5,57],[6,59],[7,62],[8,9]];
array_euclideanMetric(8,interests_points,real_points);

function simularity_byEuclideanMetric(size,rate,baseArray,euclideanMetricArray){//通过计算点之间的欧式距离，计算坐标点数组的两相似度[rate:判断为相似点的阈值;注意：x轴值统一不变]

　　var count = 0; //计数器：相似的点

　　var array = [];

　　for(var i = 0;i<size;i++){

　　　　if(rate <= Math.abs(baseArray[i]-euclideanMetricArray[i])/baseArray[i]){

　　　　　　array.push(1);

　　　　　　count++;

　　　　}

　　　　else

　　　　　　array.push(0);

　　}

　　return count/size;

}

function array_euclideanMetric(size,pointsA,pointsB){//求欧式距离
        var array = new Array();
        for(var i = 0;i<size;i++){
                array.push(Math.sqrt(Math.pow(pointsA[i][0] - pointsB[i][0],2)+Math.pow(pointsA[i][1] - pointsB[i][1],2)));
                //console.log("第"+i+"：" + Math.sqrt(Math.pow(pointsA[i][0] - pointsB[i][0],2)+Math.pow(pointsA[i][1] - pointsB[i][1],2)));//
        }
        return array;
}

//两等长数组对应元素之间做减法运算【可拓展：基本运算(+/-*//)】
function array_dif(length,arrayA,arrayB){
    var array = new Array();
    for(var i = 0;i<length;i++){
    array.push(arrayA[i]-arrayB[i]);
    //console.log(arrayA[i]-arrayB[i]);
    }
    return array;
}

function simularity_byEuclideanMetric(size,rate,baseArray,euclideanMetricArray){//通过计算点之间的欧式距离，计算坐标点数组的两相似度[rate:判断为相似点的阈值;注意：x轴值统一不变]
        var count = 0; //计数器：相似的点
        var array = [];
        for(var i = 0;i<size;i++){
                if(rate <= Math.abs(baseArray[i]-euclideanMetricArray[i])/baseArray[i]){
                        array.push(1);
                        count++;
                }
                else
                        array.push(0);
        }
        return count/size;
}

real = [181,135,61,63,53,60,51,5];
interests = [175,145,71,66,57,59,62,9];
real_points = [[1,181],[2,135],[3,61],[4,63],[5,53],[6,60],[7,51],[8,5]];
interests_points = [[1,175],[2,145],[3,71],[4,66],[5,57],[6,59],[7,62],[8,9]];

euclideanMetrics = array_euclideanMetric(8,interests_points,real_points);
rate = 0.85;
simularity_byEuclideanMetric(real.length,rate,interests,euclideanMetrics);