IshitaTakeshi

Copyright (C) 2006, Jonathan E. Taylor
All rights reserved.

Copyright (c) 2006-2008 Scipy Developers.
All rights reserved.

Copyright (c) 2009-2018 statsmodels Developers.
All rights reserved.

IshitaTakeshi

Calling LAPACK/BLAS from Kotlin Native

The original C file is cblas_example1.c

$cinterop -def liblapack.def -o build/c_interop/liblapack
$kotlinc-native lapack.kt -library build/c_interop/liblapack
$./program.kexe 
11.0
14.0

IshitaTakeshi

import json
import timeit
from collections import defaultdict

n_executions = 10000


def run_numpy(n_samples, n_bins):
    setup = """
import numpy as xp

IshitaTakeshi

*.csv

IshitaTakeshi

import numpy as np
from matplotlib.animation import FuncAnimation
from matplotlib import pyplot as plt

from mcl import Environment, MCL, Agent


def particle_weight(particle_observation, agent_observation):
    return 1 if particle_observation == agent_observation else 0

IshitaTakeshi

import numpy as np
from matplotlib.animation import FuncAnimation
from matplotlib import pyplot as plt

from mcl import Environment, MCL, Agent


def particle_weight(particle_observation, agent_observation):
    return 1 if particle_observation == agent_observation else 0

IshitaTakeshi

using PyPlot
using PyCall

PyDict(pyimport("matplotlib")["rcParams"])["font.size"] = 18

include("ode_solvers.jl")


f(x, y) = -3sin(10*x) / exp(x+y^2)

IshitaTakeshi

from xml.etree import ElementTree as ET
from bs4 import BeautifulSoup
from gensim.models.doc2vec import Doc2Vec, TaggedDocument, DocvecsArray

# root = tree.getroot()
# for neighbor in root.iter("neighbor"):
#     print(neighbor)
MIN_LINE_LENGTH = 80

def generate_documents():

IshitaTakeshi

using Base.Test
using Iterators: filter
using Distributions


function h_xs_ys(f, a, b, n_parts)
    h = (b - a) / n_parts
    xs = linspace(a, b, n_parts + 1)
    ys = [f(x) for x in xs]
    h, xs, ys

IshitaTakeshi

using Base.Test


function integrate(f, a, b, n_samples=100)
    assert(b > a)
    h = (b - a) / n_samples
    xs = linspace(a, b, n_samples)
    ys = [f(x) for x in xs]
    h * ((ys[1] + ys[end]) / 2 + sum(ys[2:end-1]))
end