#!/usr/bin/env python
# coding: utf-8

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Base.banner()    # generic binnary


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Sys.cpu_summary()


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using Benchmarks: @benchmark    # Pkg.clone("https://github.com/johnmyleswhite/Benchmarks.jl.git")

Benchmarks.Environment()


# ### Memoized Julia

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using Memoize: @memoize         # Pkg.add("Memoize")

@memoize jl_fib(n) = n < 2 ? n : jl_fib(n-1) + jl_fib(n-2)

# Type inference for memoized functions currently not implemeted, which is why
# the type annotation is needed for it to be fast:
# * https://github.com/simonster/Memoize.jl#implementation-notes
@benchmark jl_fib(20)::Int


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@benchmark jl_fib(20.0)::Float64


# ### Memoized Python

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import sys

sys.version


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from functools import lru_cache as cache


@cache(maxsize = None)
def py_fib(n):
    if n < 2:
        return n
    return py_fib(n-1) + py_fib(n-2)

get_ipython().run_line_magic('timeit', 'py_fib(20)')


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get_ipython().run_line_magic('timeit', 'py_fib(20.0)')