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(:require [ :as io]
[ :as csv]))
(def training-data
(let [data (with-open [in-file (io/reader "data/winequality-data.csv")]
(drop 1 (doall
(csv/read-csv in-file))))
control-count (int (/ (count data) 10))
input (mapv #(->> %
(take 11)
(map (fn [val] (Double/parseDouble val)))
(into [])) (drop control-count data))
control (mapv #(->> %
(take 12)
(map (fn [val] (Double/parseDouble val)))
(into [])) (take control-count data))
target (mapv #(Double/parseDouble (nth % 11))
(drop control-count data))]
{:input input
:control control
:target target}))
(def test-data
(with-open [in-file (io/reader "data/winequality-solution-input.csv")]
(csv/read-csv in-file))))
(ns wine-fun.linear-regression
(:require [clojure.core.matrix :as m]
[clojure.core.matrix.operators :as op]
[clojure.core.matrix.linear :as linear]
[ :as data]))
(m/set-current-implementation :vectorz)
(defn hypot [thetas xs]
(m/mmul (m/transpose thetas) xs))
(defn sum [func m]
(for [rc (func m)]
(m/esum rc))))
(defn mult-rows [])
(defn gd-step [x y inv-rcnt thetas]
(m/add thetas
(sum m/columns
(let [tmp (m/mul (m/sub y
(sum m/rows
(m/mul x thetas)))
(fn [[i j] val] (* val (m/mget tmp i)))
(defn batch-gradient-decent [x y alpha]
(let [cnt (m/column-count x)
rcnt (m/row-count x)
inv-rcnt (* (/ 1 rcnt) alpha)]
(loop [loop-cnt 0
thetas (m/to-vector (repeat cnt 0))]
(let [v-thetas (gd-step x y inv-rcnt thetas)]
(if (or (> loop-cnt 10000)
(m/equals thetas v-thetas 0.000001))
(when (zero? (mod loop-cnt 1000))
(println "T:" v-thetas)
(println "E:" (m/esum (m/abs (m/add thetas (m/mmul v-thetas -1.0))))))
(recur (inc loop-cnt) v-thetas)))))))
(defn matrix-gradient-decent [x y]
(let [xt (m/transpose x)
a0 (m/mmul xt x)
a1 (m/inverse a0)
a2 (m/mmul a1 xt y)]
(def reg-funcs
{:batch-gd batch-gradient-decent
:matrix-gd matrix-gradient-decent
:least-sqr linear/least-squares})
(defn run
([func x y control]
(run func x y nil control))
([func x y step control]
(let [args (if step [x y step] [x y])
thetas (apply (reg-funcs func) args)
error (loop [total-error 0
[val & rst] control]
(let [quality (apply + (map #(* %1 %2)
(butlast val)))]
(if-not val
(recur (+ total-error
(Math/abs (- (last val)
(println "Method:" (name func))
(println "Thetas: " thetas)
(println "Error: " (/ error (count control)))))))
(let [control (:control data/training-data)
x (m/matrix (:input data/training-data))
y (m/to-vector (:target data/training-data))]
(println "Starting")
(run :batch-gd x y 0.00004 control)
(run :matrix-gd x y control)
(run :least-sqr x y control)))
Method: batch-gd
Thetas: [0.006431930492348725 -0.5528332715006099 0.06759091050641436 0.026698146203819523 -0.005776954243954035 0.006658957977980936 -0.00160701061370485 0.1429976619372882 0.477799819390825 0.18796491717313363 0.38301467221089935]
Error: 0.5630484543418387
"Elapsed time: 5369544.763433 msecs"
Method: matrix-gd
Thetas: #vectorz/vector [-0.03834677746391399,-1.9709278477762922,-0.04878694353371084,0.026068265886936417,-0.6763137716126468,0.004331197888668588,-7.234522797890264E-4,1.8169396095584391,0.16274153572638014,0.3303310066445396,0.3836755986839736]
Error: 0.5420093578819823
"Elapsed time: 4.606669 msecs"
Method: least-sqr
Thetas: #vectorz/vector [-0.038346777464427585,-1.9709278477787677,-0.048786943531438774,0.02606826588689189,-0.6763137716158678,0.004331197888660421,-7.234522797880334E-4,1.816939609577304,0.16274153572233313,0.33033100664458875,0.383675598683793]
Error: 0.5420093578819263
"Elapsed time: 218.852223 msecs"
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