ven-kyoshiro

#!/usr/bin/env python

import gym
import numpy as np
import tensorflow as tf


class PolicyGradientAgent(object):

    def __init__(self, hparams, sess):

ven-kyoshiro

#
#  mnist_cnn_bn.py   date. 5/21/2016
#                    date. 6/2/2017 check TF 1.1 compatibility
#

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

ven-kyoshiro

# -*- coding:utf-8 -*-
'''
ある人が抗体を持っている確率
p :[0-1)
想定する人数を配置する正方形の一辺
N
'''
import numpy as np
import random
import seaborn as sns

ven-kyoshiro

# -*- coding:utf-8 -*-
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import scipy.optimize as optimize
from multiprocessing import Pool
import os
from tqdm import tqdm
import multiprocessing as multi

ven-kyoshiro

import chainer
import chainer.functions as F
import chainer.links as L
import chainerrl
import gym
import numpy as np

# 描画用
import matplotlib as mpl
mpl.use('Agg')

ven-kyoshiro

import matplotlib
# matplotlib.use("TkAgg")
import matplotlib.pyplot as plt
import gym
import numpy as np

def main():
    env = gym.make('CartPole-v0')
    env.reset()
    _ = env.render(mode='rgb_array')

ven-kyoshiro

import requests
import os
import pandas as pd
import collections
import pickle


def notify(message = 'done'):
  pass

ven-kyoshiro

import matplotlib.pyplot as plt
import math
def xy2color(x,y):
    norm = min(1.0,(x*x + y*y)**(1/2))
    rad = math.atan2(x,y)+math.pi
    if rad < math.pi*2/3:
        ratio = rad/(math.pi*2/3)
        r = 1.-ratio
        g = ratio
        b = 0

ven-kyoshiro

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as pat
rot = lambda th:np.array([[np.cos(th),-np.sin(th)],[np.sin(th),np.cos(th)]])

mu = np.array([0.,0.]) 
S = np.array([[1,0],[0,6]]) @ rot(np.pi/3)
x = np.random.multivariate_normal(mu, S,10000)
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111)

ven-kyoshiro

import pprint
import numpy as np
class PolynomialPredictor(object):
    def __init__(self,u,v,n=1,debug=True): 
        self.n = n
        u = np.array(u)
        A = np.array([u**i for i in range(self.n,-1,-1)]).T
        b = np.array([v]).T
        self.alpha = np.matmul(np.matmul( np.linalg.inv( np.matmul(A.T,A) ), A.T ), b )
        self.sum_of_se = np.sum((np.matmul(A,np.fliplr(self.alpha))-b)**2)