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import mmap | |
import cffi | |
ffi = cffi.FFI() | |
ffi.cdef("struct test { int x; };") | |
ref = None | |
def alloc(): |
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/* | |
* Easing Functions - inspired from http://gizma.com/easing/ | |
* only considering the t value for the range [0, 1] => [0, 1] | |
*/ | |
EasingFunctions = { | |
// no easing, no acceleration | |
linear: function (t) { return t }, | |
// accelerating from zero velocity | |
easeInQuad: function (t) { return t*t }, | |
// decelerating to zero velocity |
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#include <stdio.h> | |
#include <assert.h> | |
#include <string.h> | |
// unfortunately there does not exist a magical computer with unbounded memory for an infinite tape | |
#define TAPE_SIZE 64 | |
typedef struct { | |
char symbol; // input symbol | |
char write; // output symbol |
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from github import Github | |
from collections import Counter | |
import sys | |
USER = None | |
PASSWORD = None | |
def get_repo(repo_name): | |
contribs = list(g.get_repo(repo_name).get_contributors()) |
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import random | |
# types are first class | |
List :: Type | |
List[Int] :: Type # just List indexed by another type ;) | |
xs = [1, 2, 3] # inferred type: xs :: List[Int] | |
for(xs, print) |
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import Data.Numbers.Primes (primes, primeFactors) | |
import Data.List (elemIndex) | |
import Control.Monad (forM_) | |
data N = P N | Z | Prod [N] deriving (Eq, Show) | |
primes' = 1 : primes | |
primeIndex n = let Just idx = elemIndex n primes' in idx | |
encode :: Int -> N |
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type Weight = Float | |
type Bias = Float | |
-- type Neuron = Bias -> [Weight] -> [Float] -> Float | |
-- Artificial neuron, given some activation function f. | |
-- A neuron is simply a function from a vector of weights, and a vector of values, to an activation value. | |
neuron :: (Float -> Float) -> Bias -> [Weight] -> [Float] -> Float | |
--neuron :: (Float -> Float) -> Neuron | |
neuron f b ws xs = f (sum (zipWith (*) ws xs) + b) |
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type Weight = Float | |
type Threshold = Float | |
perceptron :: [Weight] -> Threshold -> [Float] -> Bool | |
perceptron ws t xs = sum (zipWith (*) ws xs) >= t | |
-- OR gate | |
or_p = perceptron [1, 1] 1 | |
-- AND gate |
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