private function gailv($weici_arr,$cur_weici,$flag=1){
	
		$a = file_get_contents('norm.txt');
		$a = $this->shape($a);
		$dim =[33,11];

		$xk = $weici_arr;//[5263,4960,5961,4977];	// 高校录取位次
		$position_student = $cur_weici;//4936;	// 学生当年位次

		/**************************************************************/
		$xk_average = array_sum($xk)/count($xk);
		# (1) 当学生位次与高校位次差异较大时，选择不放大“方差因子”以使得求解的概率更大或更小
		if($position_student <$xk_average){
			$distance_factor=$position_student/$xk_average;
		}else{
			$distance_factor=$xk_average/$position_student;
		}
		//echo 'distance_factor='.$distance_factor.'   ';
		
		$enlarge_factor_sigma=$distance_factor*2+1; #方差放大因子在[1,3]之间变化

		# (2)数据的倾斜程度越大，越需要降低直线斜率
		$scale_step=$xk_average/2; #选取合适的x轴尺度
		/*************************************************************/

		// 线性规划预测当年数据
		$xi_vector = [];
		for($i=0;$i<=count($xk)-1;$i++){
			$xi_vector[] = $i*$scale_step+$scale_step;
		}
		
		$yi_vector = $xk;
		$xi_vector_average = array_sum($xi_vector)/count($xi_vector);
		$yi_vector_average = array_sum($yi_vector)/count($yi_vector);

		$frac_up = 0;$frac_down=0;$a_linear=0;$b_linear=0;
		foreach ($xi_vector as $i => $val) {
			$frac_up = $frac_up+($xi_vector[$i]-$xi_vector_average)*($yi_vector[$i]-$yi_vector_average);
			$frac_down = $frac_down+($xi_vector[$i]-$xi_vector_average)*($xi_vector[$i]-$xi_vector_average);
			$a_linear = $frac_up/$frac_down;
			$b_linear = $yi_vector_average - $a_linear*$xi_vector_average;
		}
		$xk_predicted = $a_linear * (count($xk)+1)+$b_linear;
		
		$theta_line=abs(atan($a_linear)*180/pi()); #计算直线斜率
		
		# (2)数据的倾斜程度越大，越需要降低直线斜率
		$beta_a_linear=1 - $theta_line/90;
		//echo 'beta_a_linear='.$beta_a_linear.'  ';

		$xk_predicted = $beta_a_linear * $a_linear*($xi_vector[count($xi_vector)-1]+$scale_step)+$b_linear;

		///////////////////////////////////////////////
		$mu = array_sum($xk)/count($xk)*0.99999999;	//样本均值

		// 样本方差
		$s = 0;
		foreach ($xk as $val) {
			$s = $s + pow(($val-$mu),2);
		}
		$sigma = sqrt($s/(count($xk)-1))*$enlarge_factor_sigma;
		//echo 'mu='.$mu.'   sigma='.$sigma;

		$Z_norm = ($position_student - $mu)/$sigma;	// 转换成标准正态分布

		$sign_index = 1;
		if($Z_norm<0){
			$sign_index = -1;
			$Z_norm = abs($Z_norm);
		}

		//使数据不超过表格行的范围
		$Z_norm = $Z_norm>3.19?3.19:$Z_norm;

		// 查表确定Z的值
		for($i=0;$i<$dim[0];$i++){

			if($Z_norm<=$a[$i][0]){
				$m_row = $i-1;
				break;
			}else{
				$m_row = $dim[0]-1;
			}
		}
		$Z_decimal = $Z_norm - $a[$m_row][0];// 获取Z的百分位小数部分

		for($j=0;$j<11;$j++){
			if($Z_decimal<=$a[0][$j]){
				$n_col = $j-1;
				break;
			}else{
				$n_col = $dim[1] - 1;
			}
		}

		//echo 'm_row='.$m_row.'   n_col='.$n_col;

		// 均值到Z的概率
		if($n_col<10){
			$x0 = $Z_decimal;
			$xk = $a[0][$n_col];
			$xkk = $a[0][$n_col+1];
			$phi_xk = $a[$m_row][$n_col];
			$phi_xkk = $a[$m_row][$n_col+1];
		}else{
			$x0 = $Z_decimal;
			$xk = $a[0][$n_col];
			$xkk = 0.1;
			$phi_xk = $a[$m_row][$n_col];
			$phi_xkk = $a[$m_row+1][1];
		}
		$P_Z_norm = ($x0-$xk)/($xkk-$xk)*$phi_xkk+($xkk-$x0)/($xkk-$xk)*$phi_xk;// 插值
		//echo 'P_Z_norm='.$P_Z_norm."\r\n";

		// 录取概率
		$P_student = 0.5 -$sign_index*$P_Z_norm;
		//echo '学生的录取概率：'.$P_student;
		

		return $P_student;
	}

	private function shape($a){
		$arr = explode("\n",$a);   

		foreach($arr as $key => $val){
			$_temp = explode(' ', $val);
			$item = [];
			foreach($_temp as $t){
				if(trim($t)==''){
					continue;
				}
				$item[] = trim($t);
			}
			$arr[$key] = $item;
		}
		return $arr;
	}