LinearRegression.java

static class LinearRegression
{
	double[] independent;
	double[] independentDiff;
	double independentDiffSquaredSum;
	double independentMean;
	
	double[] calculate(double[] values)
	{
		if (values.length != independent.length)
			return new double[] { Double.NaN, Double.NaN };
		
		double mean = mean(values);
		double sum = 0;
		for (int i = 0; i < values.length; i++)
		{
			final double square = (values[i] - mean) * independentDiff[i];
			sum += square;
		}
		final double m = sum / independentDiffSquaredSum;
		final double q = mean - (m * independentMean);
		
		return new double[] { m, q };
	}
	
	double[][] calculate(double[][] values)
	{
		double[][] results = new double[values.length][];
		
		for (int i = 0; i < values.length; i++)
			results[i] = calculate(values[i]);
		
		return results;
	}
	
	double mean(double[] values)
	{
		double sum = 0;
		for (int i = 0; i < values.length; i++)
			sum += values[i];
		return sum / values.length;
	}
	
	void setIndependent(double[] values)
	{
		independent = values.clone();
		independentMean = mean(independent);
		independentDiff = new double[independent.length];
		for (int i = 0; i < independent.length; i++)
			independentDiff[i] = independent[i] - independentMean;
		
		independentDiffSquaredSum = 0;
		for (int i = 0; i < independentDiff.length; i++)
			independentDiffSquaredSum += independentDiff[i] * independentDiff[i];
	}
}