matsuken92

# (reference) https://ja.wikipedia.org/wiki/ガボールフィルタ

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as st
import math

plt.style.use('fivethirtyeight')

matsuken92

# 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

matsuken92

# http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.eig.html
#numpy.linalg.eig(a)[source]
#Compute the eigenvalues and right eigenvectors of a square array.
#
#Parameters:	
#a : (..., M, M) array
#Matrices for which the eigenvalues and right eigenvectors will be computed
#Returns:	
#w : (..., M) array
#The eigenvalues, each repeated according to its multiplicity. The eigenvalues are not necessarily ordered. The resulting array will be #always be of complex type. When a is real the resulting eigenvalues will be real (0 imaginary part) or occur in conjugate pairs

matsuken92

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

from matplotlib import animation as ani


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

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

matsuken92

%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

matsuken92

%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)

matsuken92

# 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/')

matsuken92

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

matsuken92

%matplotlib inline
import numpy as np
import sklearn.decomposition as decomp

import matplotlib.pyplot as plt
import matplotlib.cm as cm

# function definitions
class DigitData:
    def __init__(self, data):