dpiponi

#include <stdio.h>

#define N 1000

__global__
void add(int *a, int *b) {
    int i = blockIdx.x;
    if (i<N) {
        b[i] = 2*a[i];
    }

dpiponi

#include <stdio.h>

//
// Nearly minimal CUDA example.
// Compile with:
//
// nvcc -o example example.cu
//

#define N 1000

dpiponi

//
// On MacOSX compile with:
// g++ -framework OpenGL -framework GLUT -o example example.cpp
//

#include <stdlib.h>

#include <GLUT/glut.h>

GLuint program;

dpiponi

> {-# LANGUAGE ExplicitForAll, RankNTypes #-}

> import Control.Monad.Cont

An Eilenberg-Moore algebra for a monad t (a t-algebra) is one of these:

> type Algebra t x = t x -> x

satisfying these laws (page 3):

dpiponi

function dm(Pa, Pb, y, β, n)
    for i in 1:n
        if i%100 == 0
            print("Iteration $i/$n\n")
        end
        fay = (1-1/β)*Pa(y)+(1/β)*y
        fby = (1+1/β)*Pb(y)-(1/β)*y
        y = y+β*(Pa(fby)-Pb(fay))
#        @show(y)
    end

dpiponi

{-# LANGUAGE FlexibleContexts #-} 

import Data.Array
import Data.Array.IO
import Data.Foldable
import Data.List hiding (sum)
import System.Random
import Control.Monad.State
import Prelude hiding (sum)
--import Debug.Trace

dpiponi

import Data.Ratio
import Data.List
import Control.Monad

-- I'm using two Haskell types for the two vertex types in
-- a bipartite graph.
-- Edges only go from type a to type b.
data BipartiteGraph a b = G [a] [b] [(a, b)]

instance (Show a, Show b) => Show (BipartiteGraph a b) where

dpiponi

# -*- coding: utf-8 -*-

import numpy as np
import matplotlib.pyplot as plt
import random
import scipy.integrate as integrate

# Dimension of image in pixels
N = 129
# Number of samples to use for integration

dpiponi

# -*- coding: utf-8 -*-

import numpy as np
import matplotlib.pyplot as plt
import scipy.integrate as integrate

# Dimension of image in pixels
N = 256
# Number of samples to use for integration
M = 257

dpiponi

# -*- coding: utf-8 -*-

import numpy as np
import matplotlib.pyplot as plt
import scipy.integrate as integrate

# Dimension of image in pixels
N = 256
# Number of samples to use for integration
M = 257