Package "logistic_regression"

Title:	Logistic regression using Iteratively reweighted least squares (IRLS)
Rating:	Not rated. Create the first rating!
Latest version:	1.0
SHA1 sum:	9ff460f73c7bb9299eeddd31cc8ef0e3bdac7ab2
Author:	Fabrizio Riguzzi <fabrizio.riguzzi@unife.it>
Requires:	cplint
	matrix

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Details by download location

Version	SHA1	#Downloads	URL
1.0	9ff460f73c7bb9299eeddd31cc8ef0e3bdac7ab2	1	https://github.com/friguzzi/logistic_regression.git

logistic_regression

Logistic regression in SWI-Prolog using Iteratively Reweighted Least Squares (IRLS).

This program performs logistic regression using IRLS. See Logistic regression on Wikipedia and Murphy, Kevin P. (2012). Machine Learning – A Probabilistic Perspective. The MIT Press.

It also includes the predicate generate_data(N, Variance, Coeff, X, Y) that generates an N-row dataset with predictor variables in matrix X and predicted variable in list Y. The predicted variable is computed with the formula:

Y = (Coeff dotprod X + Noise > 0 -> 1 ; 0)

where Variance is the variance of Noise.

The predicate example_log_r(N, Coeff) is used to test the algorithm for logistic regression and dataset generation: it generates an N-row dataset with 5 as the noise variance and coefficients [1,2,3]. Then it performs 10 iterations of logistic regression. Coeff is the output of regression and should be a list of three numbers close to [1,2,3]. The higher N is, the closer to [1,2,3] Coeff should be.

Requirements:

• SWI-Prolog
• SWI-Prolog packs: matrix and cplint

Contents of pack "logistic_regression"

Pack contains 4 files holding a total of 9.0K bytes.