Naoya naoyashiga

## ir.py
import numpy
import matplotlib.pyplot as plt

import chainer
from chainer import cuda
import chainer.functions as F
import chainer.links as L
from chainer import optimizers
from chainer import cuda, Function, gradient_check, Variable, optimizers, serializers, utils
from chainer import Link, Chain, ChainList

## singlePerceptron.py
# -*- coding: utf-8 -*-

import numpy
import matplotlib.pyplot as plt

def predict(w, x):

	output = numpy.dot(w, x)

	if output >= 0:

## index.html
<html>
<head>
<style>
* {
	margin: 0;
	padding: 0;
}
</style>
	<script src="http://cdnjs.cloudflare.com/ajax/libs/p5.js/0.4.22/p5.js"></script>
	<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.4.23/addons/p5.sound.min.js"></script>

## GenerateText.py
# -*- coding: utf-8 -*-

u"""
マルコフ連鎖を用いて適当な文章を自動生成するファイル
"""

import os.path
import sqlite3
import random

## mnist_tensorboard.py
# -*- coding: utf-8 -*-

import input_data
import tensorflow as tf

# prameters
learning_rate = 0.01
training_epochs = 1000

def main():

## gulpfile.js

var gulp = require('gulp'),
    sass = require('gulp-ruby-sass')
    notify = require("gulp-notify")
    webserver = require('gulp-webserver');


gulp.task('server', function() {
  gulp.src('./')
    .pipe(webserver({

## BaseParticleWithVector.pde
class BaseParticleWithVector implements BaseParticleWithVectorInterface {
  PVector location;
  PVector velocity;

  float scalarVelocity;
  float r;

  BaseParticleWithVector() {
    location = new PVector(random(width), random(height));
    velocity = new PVector(0, 0);

## gulpfile.js
var gulp = require('gulp'),
    sass = require('gulp-ruby-sass')
    notify = require("gulp-notify")
    bower = require('gulp-bower'),
    jade = require('gulp-jade'),
    webserver = require('gulp-webserver');

var config = {
     sassPath: './resources/sass',
     jadePath: './resources/jade',

## BaseParticleWithVector.pde
class BaseParticleWithVector implements BaseParticleWithVectorInterface {
  PVector location;
  PVector velocity;

  float scalarVelocity;
  float r;

  BaseParticleWithVector() {
    location = new PVector(random(width), random(height));
    velocity = new PVector(0, 0);

## file0.swift
let array = ["カレー","ラーメン","ハンバーグ"]

array.enumerate().forEach { (index, element) in
    print("index:\(index) element:\(element)")
}

//省略記法
array.enumerate().forEach {
    print("index:\($0.0) element:\($0.1)")
}
	import numpy
	import matplotlib.pyplot as plt

	import chainer
	from chainer import cuda
	import chainer.functions as F
	import chainer.links as L
	from chainer import optimizers
	from chainer import cuda, Function, gradient_check, Variable, optimizers, serializers, utils
	from chainer import Link, Chain, ChainList
	# -- coding: utf-8 --

	import numpy
	import matplotlib.pyplot as plt

	def predict(w, x):

	output = numpy.dot(w, x)

	if output >= 0:
	<html>
	<head>
	<style>
	* {
	margin: 0;
	padding: 0;
	}
	</style>
	<script src="http://cdnjs.cloudflare.com/ajax/libs/p5.js/0.4.22/p5.js"></script>
	<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.4.23/addons/p5.sound.min.js"></script>
	# -- coding: utf-8 --

	u"""
	マルコフ連鎖を用いて適当な文章を自動生成するファイル
	"""

	import os.path
	import sqlite3
	import random
	# -- coding: utf-8 --

	import input_data
	import tensorflow as tf

	# prameters
	learning_rate = 0.01
	training_epochs = 1000

	def main():

	var gulp = require('gulp'),
	sass = require('gulp-ruby-sass')
	notify = require("gulp-notify")
	webserver = require('gulp-webserver');


	gulp.task('server', function() {
	gulp.src('./')
	.pipe(webserver({
	class BaseParticleWithVector implements BaseParticleWithVectorInterface {
	PVector location;
	PVector velocity;

	float scalarVelocity;
	float r;

	BaseParticleWithVector() {
	location = new PVector(random(width), random(height));
	velocity = new PVector(0, 0);
	let array = ["カレー","ラーメン","ハンバーグ"]

	array.enumerate().forEach { (index, element) in
	print("index:\(index) element:\(element)")
	}

	//省略記法
	array.enumerate().forEach {
	print("index:\($0.0) element:\($0.1)")
	}