lambda-ml.regression

Generalized linear model learning for two of the more popular techniques, linear regression and logistic regression.

Linear regression example usage:

(def data [[-2 -1] [1 1] [3 2]])
(def fit
  (let [alpha 0.01
        lambda 0
        iters 5000]
    (-> (make-linear-regression alpha lambda iters)
        (regression-fit data))))
(regression-predict fit (map butlast data))
;;=> (-0.9473684210526243 0.8684210526315812 2.0789473684210513)

Logistic regression example usage:

(def data [[4.0 1] [1.75 0] [4.25 1] [2.75 1] [5.0 1] [0.5 0] [1.0 0] [1.5 0]
           [5.5 1] [2.5 0] [2.0 0] [3.5 0] [1.75 1] [3.0 0] [4.75 1] [1.25 0]
           [4.5 1] [0.75 0] [3.25 1] [2.25 1]])
(def fit
  (let [alpha 0.1
        lambda 0
        iters 10000]
    (-> (make-logistic-regression alpha lambda iters)
        (regression-fit data))))
(regression-predict fit (take 3 (map butlast data)))
;;=> (0.8744474608195764 0.19083657134699333 0.9102776017566352)

gradient-descent

(gradient-descent h j x y alpha lambda)(gradient-descent h j x y alpha lambda theta)

Returns a lazy sequence of estimates of the model coefficients, along with the cost, at each iteration of gradient descent. Takes a hypothesis function h, which returns a predicted value given an example and parameters, and a cost function j, which computes the cost of applying the current model on all training examples.

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