masterdezign

for your convinience these instuction is available as:
gist
git repo

GHC_VERSION="7.10.2"

ARCHITECTURE="x86_64"

	<!DOCTYPE html>
	<html>
	<head>
	<title>My Presentation</title>
	<meta http-equiv="Content-Type" content="text/html; charset=UTF-8"/>
	<style type="text/css">
	.red { color: red }
	</style>
	</head>
	<body>

	-- "cabal install vector-fftw split"

	import qualified Numeric.FFT.Vector.Unnormalized as FFT
	import Data.Vector (fromList, toList)
	import Data.List.Split (splitOneOf)
	import Data.List (intersperse)
	import Control.Monad (forever)
	import Control.Arrow ((>>>))

	main = forever $

	{-# LANGUAGE ScopedTypeVariables #-}
	module CrazyIO (module CrazyIO, mmapFileByteString) where

	import qualified Data.Vector.Storable as V
	import qualified Data.ByteString as BS
	import qualified Data.ByteString.Internal as BS
	import Foreign
	import System.IO.MMap

	crazyLoad :: forall a. Storable a => FilePath -> Maybe (Int64, Int) -> IO (V.Vector a)

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)

	import numpy as np
	from keras.models import Sequential
	from keras.layers.core import Activation, Dense
	from keras.optimizers import SGD

	X = np.array([[0,0],[0,1],[1,0],[1,1]], "float32")
	y = np.array([[0],[1],[1],[0]], "float32")

	model = Sequential()
	model.add(Dense(2, input_dim=2, activation='sigmoid'))

	from random import random

	from numpy import tanh
	from scipy import array, vectorize, transpose

	class Network:
	""" Class builds neural network
	"""
	def __init__(self, inputs, outputs, hlayers=None, activation=tanh,
	learning_rate=1.):

	-- Worth reading http://www.geeksforgeeks.org/convex-hull-set-2-graham-scan/

	import Text.Printf
	import Data.List

	type Point = (Double, Double)

	-- Euclidean distance
	dist :: (Double, Double) -> (Double, Double) -> Double
	dist (x1, y1) (x2, y2) = sqrt (f x1 x2 + f y1 y2)

	{-
	Brainf**k interpreter

	Brainf**k is a Turing-complete programming language.

	Instructions:
	> Increment data pointer so that it points to next location in memory.
	< Decrement data pointer so that it points to previous location in memory.
	+ Increment the byte pointed by data pointer by 1. If it is already at its maximum value, 255, then new value will be 0.
	- Decrement the byte pointed by data pointer by 1. If it is at its minimum value, 0, then new value will be 255.

	-- Run a one dimensional gradient descent written in Clash [1],
	-- a high level language that compiles to Verilog and VHDL.
	-- Gradient descent can be described by a formula:
	--
	-- a_n+1 = a_n - gamma * Grad F(a_n),
	--
	-- where the constant `gamma` is what is referred to in deep learning as
	-- the learning rate.
	--
	-- [1] https://clash-lang.org/