tomonari-masada

import sys
import math
import random

def invariant_distribution(x):
    return math.exp(- 0.5 * x ** 2 / (0.3 * 0.3))

def next_state(x, dx):
    return x + dx * (random.uniform(0.0, 1.0) - 0.5)

tomonari-masada

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <time.h>

#include <omp.h>

#include <xmmintrin.h>
#include <pmmintrin.h>

tomonari-masada

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

import os
import time
import random

mail_str = 'your@email.address'
password_str = 'your_password'

tomonari-masada

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

import sys
import math

DEG2RAD = math.pi / 180.0

# WGS84
dLA_A = 6378137.0 # 赤道半径

tomonari-masada

import numpy as np
import matplotlib
matplotlib.use('agg')
from pylab import *

def findE(x):
    return 250.25 * (x[0] * x[0] + x[1] * x[1]) \
        - 449.5 * x[0] * x[1]

def gradE(x):

tomonari-masada

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <stdint.h>
#include <time.h>
#include <math.h>
#include <omp.h>

#define real float

tomonari-masada

import sys
import numpy as np
import matplotlib.pyplot as plt
import theano
import theano.tensor as T
import scipy
rng = np.random
rng.seed(0)

class Layer(object):

tomonari-masada

import sys
import numpy as np
from scipy import special
from chainer import cuda, Function, FunctionSet, gradient_check, Variable, optimizers
import chainer.functions as F

def target(args, params):
    return np.exp(special.gammaln(np.sum(params))
                  - np.sum(special.gammaln(params))
                  + np.sum((params - 1.) * np.log(args)))

tomonari-masada

import tensorflow as tf

x = tf.placeholder(tf.float32, [None, 1])
y_ = tf.placeholder(tf.float32, [None, 1])

b = tf.Variable(tf.zeros([1]))
w = tf.Variable(tf.zeros([1, 1]))

y = w * x + b

tomonari-masada

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

mu,sigma=5.0,0.2
latent_dim=2

def mlp(input):
    w1=tf.get_variable("w1", [input.get_shape()[1], 8], initializer=tf.random_normal_initializer())
    b1=tf.get_variable("b1", [8], initializer=tf.constant_initializer(0.1))