//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);