timing matrix multiplication

gistfile1.clj

(defn feature-vec [n]
  (map (partial cons 1)
       (for [x (range n)]
         (take 22 (repeatedly rand)))))

(defn dot-product [x y]
  (reduce + (map * x y)))

(defn transpose
  "returns the transposition of a `coll` of vectors"
  [coll]
  (apply map vector coll))

(defn matrix-mult
  [mat1 mat2]
  (let [row-mult (fn [mat row]
                   (map (partial dot-product row)
                        (transpose mat)))]
    (map (partial row-mult mat2)
         mat1)))

(defn test-my-mult
  [n afn]
  (let [xs  (feature-vec n)
        xst (transpose xs)]
    (time (dorun (afn xst xs)))))

;; Example (yields a 23x23 matrix):
;; (test-my-mult 1000 i/mmult) => "Elapsed time: 32.626 msecs"
;; (test-my-mult 10000 i/mmult) => "Elapsed time: 628.841 msecs"

;; (test-my-mult 1000 matrix-mult) => "Elapsed time: 14.748 msecs"
;; (test-my-mult 10000 matrix-mult) => "Elapsed time: 434.128 msecs"
;; (test-my-mult 1000000 matrix-mult) => "Elapsed time: 375751.999 msecs"


;; Test from wikipedia
;; (def A [[14 9 3] [2 11 15] [0 12 17] [5 2 3]])
;; (def B [[12 25] [9 10] [8 5]])

;; user> (matrix-mult A B)
;; ((273 455) (243 235) (244 205) (102 160))

(require '[incanter.core :as i])

;; user> (i/mmult A B)
;; [273.0000 455.0000
;; 243.0000 235.0000
;; 244.0000 205.0000
;; 102.0000 160.0000]

matrix.py

import numpy as np

def test_my_mult(n):
    A = np.random.rand(n*23).reshape(n,23)
    At = A.T

    t0 = time.time()
    res = np.dot(A.T, A)
    print time.time() - t0
    print np.shape(res)

    return res

# Example (returns a 23x23 matrix):
# >>> results = test_my_mult(1000000)
# 
# 0.906938076019
# (23, 23)