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import numpy as np
import matplotlib.pyplot as plt
from moviepy.editor import *
from matplotlib import animation as ani

sigma = 1
mu     = 3

def norm_dist_neg(x):
    return -1./(np.sqrt(2 * np.pi) * sigma)* np.exp(-0.5*((x-mu)**2)/((sigma**2)))

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%matplotlib inline
import matplotlib.pyplot as plt
from matplotlib import animation as ani
import numpy as np
import sys
plt.style.use('ggplot')

matsuken92

%matplotlib inline 
import sys
import matplotlib.pyplot as plt
from matplotlib import animation as ani
import numpy as np
import pandas as pd
import scipy.stats as st
from scipy.special import ndtri

# Data Import

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%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np

from matplotlib import animation as ani


plt.figure(figsize=(8,8))
n=20

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# these code on this page are based on the following chainer's example. Thanks!
# https://github.com/pfnet/chainer/tree/master/examples/mnist/train_mnist.py

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import fetch_mldata
from chainer import cuda, Variable, FunctionSet, optimizers
import chainer.functions  as F
import sys, time, math

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%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import chi2
from matplotlib import animation as ani

def plot_dist(data, bins, title =""):
    plt.figure(figsize=(7,5))
    plt.title(title)
    plt.hist(data, bins, color="lightgreen", normed=True)

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from requests_oauthlib import OAuth1Session
from requests.exceptions import ConnectionError, ReadTimeout, SSLError
import json, datetime, time, pytz, re, sys, traceback, unicodedata, pymongo
#from pymongo import Connection     # Connection classは廃止されたのでMongoClientに変更 
from pymongo import MongoClient
import numpy as np
from collections import defaultdict
from bson.objectid import ObjectId
import MeCab as mc

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from sklearn.model_selection._search import ParameterGrid

param_grid = {
    "metric_list" : [None, "None", "binary_logloss", "auc", ["binary_logloss"], ["auc"], 
                    ['binary_logloss','auc', ], ['auc', 'binary_logloss',] ],
    "first_metric_only": [True, False],
    "eval_train_metric": [True, False],
}
pg = ParameterGrid(param_grid)

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# coding:utf-8
%matplotlib inline
import matplotlib.pyplot as plt
from wordcloud import WordCloud
from bs4 import BeautifulSoup
import requests
import MeCab as mc

def mecab_analysis(text):
    t = mc.Tagger('-Ochasen -d /usr/local/Cellar/mecab/0.996/lib/mecab/dic/mecab-ipadic-neologd/')

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%matplotlib inline 
import sys
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as st
from matplotlib import animation as ani
import sklearn.metrics as mt