Question

I was trying to use commons math to figure out the constants in a polynomial. It looks like the routine exists but I got this error. Does anyone see the issue?

I was trying to convert this question to commons-math:
https://math.stackexchange.com/questions/121212/how-to-find-curve-equation-from-data

From plotting you data (Wolfram|Alpha link), it does not look linear. So it better be fit by a polynomial. I assume you want to fit the data:

X Y
1 4
2 8
3 13
4 18
5 24
..
using a quadratic polynomial y=ax2+bx+c.

And wolfram alpha provided a great utility. I wish I could get the same answers like from wolfram.

http://www.wolframalpha.com/input/?i=fit+4%2C+8%2C+13%2C

E.g. By entering that data, I would get : 4.5 x-0.666667 (linear)

Here is the code and error:

import org.apache.commons.math3.stat.regression.OLSMultipleLinearRegression;
import org.apache.commons.math3.stat.regression.SimpleRegression;
final OLSMultipleLinearRegression regression2 = new OLSMultipleLinearRegression();
double[] y = {
        4.0, 
        8, 
        13,                 
};      
double[][] x2 = 
    {
        { 1.0, 1, 1  },
        { 1.0, 2, 4  },
        { 0.0, 3, 9  },             
    };
regression2.newSampleData(y, x2);
regression2.setNoIntercept(true);
regression2.newSampleData(y, x2);       
double[] beta = regression2.estimateRegressionParameters();
for (double d : beta) {
    System.out.println("D: " + d);
}

Exception in thread “main” org.apache.commons.math3.exception.MathIllegalArgumentException: not enough data (3 rows) for this many predictors (3 predictors)
at org.apache.commons.math3.stat.regression.AbstractMultipleLinearRegression.validateSampleData(AbstractMultipleLinearRegression.java:236)
at org.apache.commons.math3.stat.regression.OLSMultipleLinearRegression.newSampleData(OLSMultipleLinearRegression.java:70)
at org.berlin.bot.algo.BruteForceSort.main(BruteForceSort.java:108)

Answer 1

Rcook was right. I provided an additional row (test case) and that generated the same answer as from wolfram/alpha.

D: 0.24999999999999822
D: 3.4500000000000033
D: 0.24999999999999914

Or 0.25x^2 + 3.45x + 0.25

final OLSMultipleLinearRegression regression2 = new OLSMultipleLinearRegression();
        double[] y = {
                4, 
                8, 
                13,         
                18
        };              
        double[][] x2 = 
            {
                { 1, 1, 1  },
                { 1, 2, 4  },
                { 1, 3, 9  },                                                               
                { 1, 4, 16  },
            };

        regression2.newSampleData(y, x2);
        regression2.setNoIntercept(true);
        regression2.newSampleData(y, x2);       
        double[] beta = regression2.estimateRegressionParameters();
        for (double d : beta) {
            System.out.println("D: " + d);
        }