feedforward neural network

(define-library (neural-network matrix-algebra)
(import (scheme base) (srfi 1)) (export transpose hadamard sum dot-product)
(begin (define map map-in-order) (define (transpose x) (apply zip x))
  (define (hadamard x y) (map (lambda (x y) (map * x y)) x y))
  (define (sum xy) (map (lambda (x) (apply + x)) xy))
  (define (dot-product x xy) (sum (hadamard (make-list 3 x) (transpose xy))))))

(define-library (neural-network random)
(import (scheme base) (srfi 1) (srfi 27)) (export rand weights)
(begin (define source default-random-source) (random-source-randomize! source)
  (define rand (random-source-make-reals source))
  (define weights
    (list-tabulate 3 (lambda (i) (list-tabulate 3 (lambda (i) (list-tabulate 3 (lambda (i) (/ (rand) 2))))))))))

(define-library (neural-network feedforwarding)
(import (scheme base) (scheme inexact) (srfi 1) (neural-network matrix-algebra) (neural-network random))
(export sigmoid feedforward)
(begin (define (sigmoid x) (/ 1 (+ 1.0 (exp (- 0 x)))))
  (define (feedforward input ws)
    (let f ([in input] [ws ws] [ya (list)] [ys (list)])
      (if (null? ws) (list in (reverse ya) (reverse ys))
          (let* ([y (dot-product in (car ws))] [in (map sigmoid y)])
            (f in (cdr ws) (cons in ya) (cons y ys))))))))