(ns example (:require [lambda-ml.neural-network :refer :all]))

(def data [[0 0 0] [0 1 1] [1 0 1] [1 1 0]])
(def hidden-layers [3])
(def alpha 0.5)
(def lambda 0.001)
(def model (make-neural-network hidden-layers alpha lambda))

(def fit
  (-> #(neural-network-fit % (map butlast data) (map (comp vector last) data))
      (iterate model)
      (nth 5000)))

(neural-network-predict fit (map butlast data))
;;=> ((0.018947665113895507) (0.9768421723034613) (0.9770467568718264) (0.03108874342427228))

(ns example (:require [lambda-ml.clustering.dbscan :refer :all]
                      [lambda-ml.distance :refer :all]))

(def epsilon 4.0)
(def min-pts 2)
(def data [[2 10] [2 5] [8 4] [5 8] [7 5] [6 4] [1 2] [4 9]])

(dbscan euclidean epsilon min-pts data)
;;=> {[4 9] 2, [5 8] 2, [7 5] 1, [6 4] 1, [8 4] 1}

(ns example (:require [lambda-ml.decision-tree :refer :all]
                      [lambda-ml.metrics :refer :all]))

(def min-split 2)
(def min-leaf 1)
(def data [[0 0 0] [0 1 1] [1 0 1] [1 1 0]])
(def model (make-classification-tree gini-impurity min-split min-leaf))
(def fit (decision-tree-fit model data))

(decision-tree-predict fit (map butlast data))
;;=> (0 1 1 0)

(ns example (:require [lambda-ml.clustering.k-means :refer :all]
                      [lambda-ml.distance :refer :all]))

(def k 2)
(def iters 100)
(def data [[1 1] [1.5 2] [3 4] [5 7] [3.5 5] [4.5 5] [3.5 4.5]])

(nth (k-means k euclidean data) iters)
;;=> {0 ([3.5 4.5] [4.5 5] [3.5 5] [5 7] [3 4]), 1 ([1.5 2] [1 1])}

(ns example (:require [lambda-ml.neighborhood :refer :all]
                      [lambda-ml.distance :refer :all]))

(def data [[2 3] [5 4] [9 6] [4 7] [8 1] [7 2]])
(def knn (make-knn euclidean data))

(knn 3 [8 1])
;;=> [[0 [8 1]] [2 [7 2]] [18 [5 4]]]

(ns example (:require [lambda-ml.regression :refer :all]))

(def data [[-2 -1] [1 1] [3 2]])
(def model (make-linear-regression 0.01 0 5000))
(def fit (regression-fit model data))

(regression-predict fit (map #(take 1 %) data))
;;=> (-0.9473684210526243 0.8684210526315812 2.0789473684210513)

(ns example (:require [lambda-ml.regression :refer :all]))

(def data [[0.50 0] [0.75 0] [1.00 0] [1.25 0] [1.50 0] [1.75 0] [1.75 1] [2.00 0] [2.25 1] [2.50 0] [2.75 1] [3.00 0] [3.25 1] [3.50 0] [4.00 1] [4.25 1] [4.50 1] [4.75 1] [5.00 1] [5.50 1]])
(def model (make-logistic-regression 0.1 0 10000))
(def fit (regression-fit model data))

(fit :parameters)
;;=> (-4.077712444728931 1.5046450944762417)

(ns example (:require [lambda-ml.naive-bayes :refer :all]))

(def x [[6.0 180 12] [5.92 190 11] [5.58 170 12] [5.92 165 10] [5.0 100 6] [5.5 150 8] [5.42 130 7] [5.75 150 9]])
(def y [:male :male :male :male :female :female :female :female])
(def model (naive-bayes-fit x y))

(naive-bayes-predict model [[6.0 130 8]])
;;=> (:female)