rossant

"""
MIT License

Copyright (c) 2017 Cyrille Rossant

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is

danijar

# Working example for my blog post at:
# https://danijar.github.io/structuring-your-tensorflow-models
import functools
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


def doublewrap(function):
    """
    A decorator decorator, allowing to use the decorator to be used without

higham

% Complex step example.  Requires Symbolic Math Toolbox.

format long
syms x; f = atan(x)/(1+exp(-x^2))

% Derivative at $a = 2$:
a = 2; fd = double(subs( diff(f), a))

% Convert symbolic function to MATLAB function.
f = matlabFunction(f);  

breuderink

#include "fx.h"
#include <stddef.h>
#include <stdint.h>
#include <math.h>
#include <assert.h>

void static inline wht_butterfly(float * const s, float * const d) {
	float temp = *s;
	*s += *d;
	*d = temp - *d;

higham

function fd = complex_step(f,x,h)
%COMPLEX_STEP  Complex step approximation to derivative.
%  fd = COMPLEX_STEP(f,x,h) computes the complex step approximation 
%  fd to the derivative of f at x, using step h (default 1e-100).
if nargin < 3, h = 1e-100; end
fd = imag( f(x + sqrt(-1)*h) )/h;

kingjr

from googletrans import Translator
from itertools import product
from pandas import DataFrame, read_csv
import numpy as np
import string
import time
import os

# Get google translator object
translator = Translator()