Java线性回归

package com.topsmob.amazon.utils;

import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;

/**
 * Java线性回归实现
 * x：是rank
 * y: 是销量
 */
public class LinearRegression {

    public static Map calculate(List> list){
        Map map = new LinkedHashMap<>();
        int MAXN = list.size();
        int n = 0;
        double[] x = new double[MAXN];
        double[] y = new double[MAXN];
        double sumx = 0.0, sumy = 0.0, sumx2 = 0.0;
        for (Map item:list){
            //这里是x轴的参数
            x[n] = Double.parseDouble(item.get("rank").toString());
            //这里是y轴的参数
            y[n] = Double.parseDouble(item.get("qty").toString());
            sumx  += x[n];
            sumx2 += x[n] * x[n];
            sumy  += y[n];
            n++;
        }
        double xbar = sumx / n;
        double ybar = sumy / n;

        // second pass: compute summary statistics
        double xxbar = 0.0, yybar = 0.0, xybar = 0.0;
        for (int i = 0; i < n; i++) {
            xxbar += (x[i] - xbar) * (x[i] - xbar);
            yybar += (y[i] - ybar) * (y[i] - ybar);
            xybar += (x[i] - xbar) * (y[i] - ybar);
        }
        double beta1 = xybar / xxbar;
        double beta0 = ybar - beta1 * xbar;

        // print results
        System.out.println("y   = " + beta1 + " * x + " + beta0);

        // analyze results
        int df = n - 2;
        double rss = 0.0;      // residual sum of squares
        double ssr = 0.0;      // regression sum of squares
        for (int i = 0; i < n; i++) {
            double fit = beta1*x[i] + beta0;
            rss += (fit - y[i]) * (fit - y[i]);
            ssr += (fit - ybar) * (fit - ybar);
        }
        double R2    = ssr / yybar;
        map.put("r",R2);
        map.put("a",beta1);
        map.put("b",beta0);
      /*  double svar  = rss / df;
        double svar1 = svar / xxbar;
        double svar0 = svar/n + xbar*xbar*svar1;

        System.out.println("R^2                 = " + R2);
        System.out.println("std error of beta_1 = " + Math.sqrt(svar1));
        System.out.println("std error of beta_0 = " + Math.sqrt(svar0));

        svar0 = svar * sumx2 / (n * xxbar);
        System.out.println("std error of beta_0 = " + Math.sqrt(svar0));
        System.out.println("SSTO = " + yybar);
        System.out.println("SSE  = " + rss);
        System.out.println("SSR  = " + ssr);*/
        return map;
    }
}

转载于:https://my.oschina.net/riaway/blog/1600693