EnsekiTT EnsekiTT

## build.sbt
name := "scalatest_study"

version := "1.0"

scalaVersion := "2.13.2"

libraryDependencies += "org.scalatest" %% "scalatest" % "3.1.1" % "test"
libraryDependencies += "org.mockito" % "mockito-core" % "3.3.3" % "test"

## str.ipynb

      
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                EnsekiTT
                / str.ipynb
            
            
              Created
              March 18, 2018 21:15
            
              
                pandas.Series.strを全部試す。
              
          
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## fiml-py-4me.py
# References
# https://github.com/kamadak/fiml-py
# http://koumurayama.com/koujapanese/missing_data.pdf
# http://fixxman.hatenablog.com/entry/2015/10/10/224955

import numpy as np
import scipy as sp
import scipy.optimize

def fiml(data):

## DecisionTweeJumanpp.py

# coding: utf-8

# In[ ]:


# JUMAN++版

# ツイートを集める
# https://github.com/sixohsix/twitter

## DecisionTwee.py
# ツイートを集める
# https://github.com/sixohsix/twitter
# 環境変数にトークンとか入れておく
import os
from twitter import *
import time

# Janomeの形態素解析
# https://github.com/mocobeta/janome
from janome.tokenizer import Tokenizer

## app.py
import os
import sys
import argparse

from flask import Flask, render_template, request, redirect, url_for, jsonify
from werkzeug.utils import secure_filename

import numpy as np
import tensorflow as tf

## calib.yaml
%YAML:1.0

#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------

# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 1467.836136
Camera.fy: 1471.712197
Camera.cx: 941.204100

## simple_sample.lua
require 'torch'

torch.manualSeed(114514)

-- choose a dimension
N = 5

-- create a random NxN matrix
A = torch.rand(N, N)

## extensions_test.py
import numpy as np
import chainer
from chainer import Function, gradient_check, report, training, utils, Variable
from chainer import datasets, iterators, optimizers, serializers
from chainer import Link, Chain, ChainList
import chainer.functions as F
import chainer.links as L
from chainer.training import extensions

class MyModel(Chain):

## ant.py
# coding utf-8
import copy
import random
from math import *
import matplotlib.pyplot as plt


class Agent():
    def __init__(self, towns, roads, start, pheromone):
        # value
	name := "scalatest_study"

	version := "1.0"

	scalaVersion := "2.13.2"

	libraryDependencies += "org.scalatest" %% "scalatest" % "3.1.1" % "test"
	libraryDependencies += "org.mockito" % "mockito-core" % "3.3.3" % "test"
	# References
	# https://github.com/kamadak/fiml-py
	# http://koumurayama.com/koujapanese/missing_data.pdf
	# http://fixxman.hatenablog.com/entry/2015/10/10/224955

	import numpy as np
	import scipy as sp
	import scipy.optimize

	def fiml(data):

	# coding: utf-8

	# In[ ]:


	# JUMAN++版

	# ツイートを集める
	# https://github.com/sixohsix/twitter
	# ツイートを集める
	# https://github.com/sixohsix/twitter
	# 環境変数にトークンとか入れておく
	import os
	from twitter import *
	import time

	# Janomeの形態素解析
	# https://github.com/mocobeta/janome
	from janome.tokenizer import Tokenizer
	import os
	import sys
	import argparse

	from flask import Flask, render_template, request, redirect, url_for, jsonify
	from werkzeug.utils import secure_filename

	import numpy as np
	import tensorflow as tf
	%YAML:1.0

	#--------------------------------------------------------------------------------------------
	# Camera Parameters. Adjust them!
	#--------------------------------------------------------------------------------------------

	# Camera calibration and distortion parameters (OpenCV)
	Camera.fx: 1467.836136
	Camera.fy: 1471.712197
	Camera.cx: 941.204100
	require 'torch'

	torch.manualSeed(114514)

	-- choose a dimension
	N = 5

	-- create a random NxN matrix
	A = torch.rand(N, N)
	import numpy as np
	import chainer
	from chainer import Function, gradient_check, report, training, utils, Variable
	from chainer import datasets, iterators, optimizers, serializers
	from chainer import Link, Chain, ChainList
	import chainer.functions as F
	import chainer.links as L
	from chainer.training import extensions

	class MyModel(Chain):
	# coding utf-8
	import copy
	import random
	from math import *
	import matplotlib.pyplot as plt


	class Agent():
	def __init__(self, towns, roads, start, pheromone):
	# value