ryanrhymes

s = window.getSelection();
oRange = s.getRangeAt(0); //get the text range
oRect = oRange.getBoundingClientRect();

ryanrhymes

let cpu_count () = 
  try match Sys.os_type with 
  | "Win32" -> int_of_string (Sys.getenv "NUMBER_OF_PROCESSORS") 
  | _ ->
    let i = Unix.open_process_in "getconf _NPROCESSORS_ONLN" in
     let close () = ignore (Unix.close_process_in i) in
     try Scanf.fscanf i "%d" (fun n -> close (); n) with e -> close (); raise e
  with
  | Not_found | Sys_error _ | Failure _ | Scanf.Scan_failure _ 
  | End_of_file | Unix.Unix_error (_, _, _) -> 1

ryanrhymes

shape = (10,1000,10000)

function f00()
  t0 = time()
  x = []
  t1 = time()
  @printf "empty:\t\t%.8f\n" (t1 -t0)
end

function f01()

ryanrhymes

#!/usr/bin/env python

import time
import math
import numpy as npy

m, n, o = 10, 1000, 10000
shape = [m,n,o]

def f00 () :

ryanrhymes

open Owl
open Bigarray

let m, n, o = 10, 1000, 10000
let shape = [|m;n;o|]

let test_op s c op =
  let ttime = ref 0. in
  for i = 1 to c do

ryanrhymes

CXXFLAGS0 = -shared -fPIC -ansi -pedantic -W -Wall
CXXFLAGS1 = -shared -fPIC -ansi -pedantic -Wno-extern-c-compat -Wno-c++11-long-long
CXXFLAGS2 = -c -ansi -Wno-extern-c-compat -Wno-c++11-long-long

all:
	g++ ${CXXFLAGS2} -I. eigen_dsmat.cpp -o eigen_dsmat.o
	g++ ${CXXFLAGS2} -I. eigen_spmat.cpp -o eigen_spmat.o
	#gcc -c ffi_eigen_generated_stub.c -I. -I`ocamlfind query ctypes` -I`ocamlc -where` -o eigen_stub.o
	ld -r eigen_*.o -o libeigen.o
	ocamlfind ocamlc -c -package ctypes ffi_eigen_generated.ml

ryanrhymes

(*
 * Eigen - an OCaml interface to C++ Eigen library
 * Copyright (c) 2016 Liang Wang <[email protected]>
 *)

module type MatSig = sig

  type elt
  type mat

ryanrhymes

(* Test neural network on MNIST *)

#require "bitstring";;
#require "bitstring.syntax";;

open Bitstring

type t = { magic : int; items : int }

let dataset = "/Users/liang/owl_dataset/t10k-images-idx3-ubyte"

ryanrhymes

open Owl_algodiff_ad

type layer = { mutable w : t; mutable b : t; a : t -> t }

type network = { layers : layer array }

let run_layer x l = Maths.((x $@ l.w) + l.b) |> l.a

let run_network x nn = Array.fold_left run_layer x nn.layers

ryanrhymes

open Owl
open Algodiff.AD

type layer = { mutable w : t; mutable b : t; a : t -> t }
type network = { layers : layer array }

let run_layer x l = Maths.((x $@ l.w) + l.b) |> l.a
let run_network x nn = Array.fold_left run_layer x nn.layers

let l0 = {