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class MathExc(Exception):
	def __init__(self,msg):
		print "\nError: "+str(msg)+" !"

class Converter (object):
	def __init__(self,base):
		self._BaseN_alpha=None 
		self._BaseN_num=None
		self._BaseN_base=None 
		base=int(base)

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import socket
import requests
from bs4 import BeautifulSoup
import time, datetime
import subprocess
import re
import os

# This script works on macOS and Linux only!
#

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Result of assembling on different platforms

Apparently, both GCC and Clang encode bl label in such a way that the resulting machine code ends up jumping to itself, not label. However, when assembled on an actual thumbv7 machine, the machine code suddenly becomes correct. What's more, in many cases of "incorrect" encoding that should jump to itself, objdump somehow recognizes that it jumps to the correct label.

armv7-alpine-linux-musleabihf

Put Dockerfile and mve_docker.s in the same directory and run:

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# brew tap nativeos/i386-elf-toolchain
# brew install i386-elf-binutils i386-elf-gcc i386-elf-grub

CC=i386-elf-gcc
CFLAGS=-ffreestanding -nostdlib -fno-pic -Wall -Wextra -I .

.PHONY: all test clean

all: kernel.elf

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I've recently encountered an article that showed some ways to speed up one's Python code. Some of these methods seemed rather peculiar, so I decided to do some extra complex data analysis to try to understand whether these methods actually work.

Python version: 3.7.1

Here's a quick summary of the methods proposed in that article that I found odd.

Decreasing the use of for loop

The article said that, since for loops are "dynamic" (not sure what this means), they're slower than while loops. I compared the following two loops and found that, on average, the for loop was about 2.5 times faster than the corresponding while loop:

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Results:

forcebru ~/t/julia_numpy_bench> ipython bench.ipy 
Python with Nx=50000, Np=500...
9.84 ms ± 73 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
forcebru ~/t/julia_numpy_bench> julia-1.6 bench.jl 
Julia with Nx=50000, Np=500...
Trial(32.285 ms)
forcebru ~/t/julia_numpy_bench> julia-1.6 -O3 bench.jl

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function (ˍ₋out, ˍ₋arg1, b)
    #= ~/.julia/packages/SymbolicUtils/zaGjS/src/code.jl:301 =#
    #= ~/.julia/packages/SymbolicUtils/zaGjS/src/code.jl:302 =#
    let var"p[1]" = #= ~/.julia/packages/SymbolicUtils/zaGjS/src/code.jl:188 =# @inbounds(ˍ₋arg1[1]), var"p[2]" = #= ~/.julia/packages/SymbolicUtils/zaGjS/src/code.jl:188 =# @inbounds(ˍ₋arg1[2]), var"p[3]" = #= ~/.julia/packages/SymbolicUtils/zaGjS/src/code.jl:188 =# @inbounds(ˍ₋arg1[3]), var"μ[1]" = #= ~/.julia/packages/SymbolicUtils/zaGjS/src/code.jl:188 =# @inbounds(ˍ₋arg1[4]), var"μ[2]" = #= ~/.julia/packages/SymbolicUtils/zaGjS/src/code.jl:188 =# @inbounds(ˍ₋arg1[5]), var"μ[3]" = #= ~/.julia/packages/SymbolicUtils/zaGjS/src/code.jl:188 =# @inbounds(ˍ₋arg1[6]), var"σ2[1]" = #= ~/.julia/packages/SymbolicUtils/zaGjS/src/code.jl:188 =# @inbounds(ˍ₋arg1[7]), var"σ2[2]" = #= ~/.julia/packages/SymbolicUtils/zaGjS/src/code.jl:188 =# @inbounds(ˍ₋arg1[8]), var"σ2[3]" = #= ~/.julia/packages/SymbolicUtils/zaGjS/src/code.jl:188 =# @inbounds(ˍ₋arg1[9])
        #= ~

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Benchmark of several Julia autodiff packages (including Symbolics.jl)

The goal is to differentiate a log-likelihood function - the workhorse of probability theory, mathematical statistics and machine learning.

Here, it's the log-likelihood of a Gaussian mixture model:

normal_pdf(x::Real, mean::Real, var::Real) =
    exp(-(x - mean)^2 / (2var)) / sqrt(2π * var)

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import Pkg
Pkg.activate(temp=true)
@info "Installing Ipopt, Optimization, ADNLPModels and JuMP. Output is suppressed."
Pkg.add([
    Pkg.PackageSpec(name="Ipopt", version="1.1.0"),
    Pkg.PackageSpec(name="Optimization", version="3.10.0"),
    Pkg.PackageSpec(name="OptimizationMOI", version="0.1.5"),
    Pkg.PackageSpec(name="ADNLPModels", version="0.4.0"),
    Pkg.PackageSpec(name="NLPModelsIpopt", version="0.10.0"),
    Pkg.PackageSpec(name="JuMP", version="1.6.0"),