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# See "Self-Normalizing Neural Networks" https://arxiv.org/abs/1706.02515
# "SNNs cannot be derived with...tanh units..."
# So I'm probably missing the point somewhere...

import math
import numpy

# Magic number
lambda0 = 1.59254

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import math
import numpy

lambda0 = 1.59254

n = 1000
nlayers = 100

# Incoming activiations have mean 0, variance 1
x = numpy.random.normal(0, 1, n)

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{-# LANGUAGE FlexibleContexts #-}

-- Version 2.0

import Data.Ratio

(*!) _ 0 = 0
(*!) a b = a*b

(^+) a b = zipWith (+) a b

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{-# LANGUAGE DataKinds #-}
{-# LANGUAGE FlexibleContexts #-}

-- Uses exact-real package

import Data.Ratio
import Data.CReal

(*!) _ 0 = 0
(*!) a b = a*b

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import Data.Ratio

(^+) a b = zipWith (+) a b
(^-) a b = zipWith (-) a b

(a : as) `convolve` (b : bs) = (a * b) :
    ((map (a *) bs) ^+ (as `convolve` (b : bs)))

compose (f : fs) (0 : gs) = f : (gs `convolve` (compose fs (0 : gs)))

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 = 33

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

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# -*- 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

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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