IshitaTakeshi

"""
A*アルゴリズムの実装です．
手順はWikipedia(https://ja.wikipedia.org/wiki/A*)の解説と同じです
"""

def generate_path(parents, start_node, goal_node):
    """経路上のノードの親子関係を記録した辞書(parents)から経路を復元する"""
    c = goal_node
    path = [goal_node]
    while c != start_node:

IshitaTakeshi

function plot_bisection_method(f, a, b;
                               n_max_iter=20, n_samples=100, interval=1000)
    xrange, yrange, xs, ys = init_xs_ys(f, a, b, n_samples)
    fig, ax = init_figure_plot()

    function draw(i)
        cla()

        ax[:set_xlabel]("x")
        ax[:set_ylabel]("f(x)")

IshitaTakeshi

from itertools import permutations, combinations

import numpy as np
from numpy.linalg import det

from matplotlib.patches import FancyArrowPatch
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import proj3d
from mpl_toolkits.mplot3d import Axes3D

IshitaTakeshi

using PyPlot
using PyCall


function generate_Lᵢ(xs, ys, i)
    function Lᵢ(x)
        n, d = 1, 1
        for (j, xⱼ) in enumerate(xs)
            if i == j
                continue

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

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

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 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

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