import tensorflow as tf

import numpy as np

tf.set_random_seed(777)  # for reproducibility

def MinMaxScaler(data):

    numerator = data - np.min(data, 0)

    denominator = np.max(data, 0) - np.min(data, 0)

    # noise term prevents the zero division

    return numerator / (denominator + 1e-7)

xy = np.array([[828.659973, 833.450012, 908100, 828.349976, 831.659973],

               [823.02002, 828.070007, 1828100, 821.655029, 828.070007],

               [819.929993, 824.400024, 1438100, 818.97998, 824.159973],

               [816, 820.958984, 1008100, 815.48999, 819.23999],

               [819.359985, 823, 1188100, 818.469971, 818.97998],

               [819, 823, 1198100, 816, 820.450012],

               [811.700012, 815.25, 1098100, 809.780029, 813.669983],

               [809.51001, 816.659973, 1398100, 804.539978, 809.559998]])

# very important. It does not work without it.

xy = MinMaxScaler(xy)

print(xy)

x_data = xy[:, 0:-1]

y_data = xy[:, [-1]]

import tensorflow as tf

tf.set_random_seed(777)  # for reproducibility

x_data = [[1, 2, 1, 1],

          [2, 1, 3, 2],

          [3, 1, 3, 4],

          [4, 1, 5, 5],

          [1, 7, 5, 5],

          [1, 2, 5, 6],

          [1, 6, 6, 6],

          [1, 7, 7, 7]]

y_data = [[0, 0, 1],

          [0, 0, 1],

          [0, 0, 1],

          [0, 1, 0],

          [0, 1, 0],

          [0, 1, 0],

          [1, 0, 0],

          [1, 0, 0]]

X = tf.placeholder("float", [None, 4])

Y = tf.placeholder("float", [None, 3])

nb_classes = 3  #클래스의 갯수

# Shape

# 4 = X값, nb_classes 출력값

W = tf.Variable(tf.random_normal([4, nb_classes]), name='weight')

b = tf.Variable(tf.random_normal([nb_classes]), name='bias')

# tf.nn.softmax computes softmax activations

# softmax = exp(logits) / reduce_sum(exp(logits), dim)

hypothesis = tf.nn.softmax(tf.matmul(X, W) + b)

# Cross entropy cost/loss

cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1))

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)

# Launch graph

with tf.Session() as sess:

    sess.run(tf.global_variables_initializer())

    #Hypothesis에 대해 학습하여 최적의 W,b를 구함

    for step in range(2001):

        sess.run(optimizer, feed_dict={X: x_data, Y: y_data})

        if step % 200 == 0:

            print(step, sess.run(cost, feed_dict={X: x_data, Y: y_data}))

    print('--------------')

    # Testing & One-hot encoding: 예측을 하게 한다. argmax에서 최강자를 골라준다.

    a = sess.run(hypothesis, feed_dict={X: [[1, 11, 7, 9]]})

    print(a, sess.run(tf.argmax(a, 1)))

    print('--------------')

    b = sess.run(hypothesis, feed_dict={X: [[1, 3, 4, 3]]})

    print(b, sess.run(tf.argmax(b, 1)))

    print('--------------')

    c = sess.run(hypothesis, feed_dict={X: [[1, 1, 0, 1]]})

    print(c, sess.run(tf.argmax(c, 1)))

    print('--------------')

    all = sess.run(hypothesis, feed_dict={X: [[1, 11, 7, 9], [1, 3, 4, 3], [1, 1, 0, 1]]})

    print(all, sess.run(tf.argmax(all, 1)))

==> 결과 1 0 2

[[1.3890432e-03 9.9860197e-01 9.0612402e-06]

 [9.3119204e-01 6.2902056e-02 5.9058843e-03]

 [1.2732767e-08 3.3411323e-04 9.9966586e-01]] [1 0 2]

import tensorflow as tf

import numpy as np

tf.set_random_seed(777)  # for reproducibility

# Predicting animal type based on various features

xy = np.loadtxt('data-04-zoo.csv', delimiter=',', dtype=np.float32)

x_data = xy[:, 0:-1]

y_data = xy[:, [-1]]

print(x_data.shape, y_data.shape)

nb_classes = 7  # 0 ~ 6

X = tf.placeholder(tf.float32, [None, 16])

Y = tf.placeholder(tf.int32, [None, 1])  # 0 ~ 6

Y_one_hot = tf.one_hot(Y, nb_classes)  # one hot

print("one_hot", Y_one_hot)

Y_one_hot = tf.reshape(Y_one_hot, [-1, nb_classes])

print("reshape", Y_one_hot)

W = tf.Variable(tf.random_normal([16, nb_classes]), name='weight')

b = tf.Variable(tf.random_normal([nb_classes]), name='bias')

# tf.nn.softmax computes softmax activations

# softmax = exp(logits) / reduce_sum(exp(logits), dim)

logits = tf.matmul(X, W) + b

hypothesis = tf.nn.softmax(logits)

# Cross entropy cost/loss

cost_i = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y_one_hot)

cost = tf.reduce_mean(cost_i)

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)

# 정확도를 구한다. 

prediction = tf.argmax(hypothesis, 1)

correct_prediction = tf.equal(prediction, tf.argmax(Y_one_hot, 1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# Launch graph

with tf.Session() as sess:

    sess.run(tf.global_variables_initializer())

    # 학습을 수행한다

    for step in range(2000):

        sess.run(optimizer, feed_dict={X: x_data, Y: y_data})

        if step % 100 == 0:

            loss, acc = sess.run([cost, accuracy], feed_dict={X: x_data, Y: y_data})

            print("Step: {:5}\tLoss: {:.3f}\tAcc: {:.2%}".format(

                step, loss, acc))

    # Let's see if we can predict: 잘 예측되는지 확인한다. 

    pred = sess.run(prediction, feed_dict={X: x_data})

    # y_data: (N,1) = flatten => (N, ) matches pred.shape

    for p, y in zip(pred, y_data.flatten()):

        print("[{}] Prediction: {} True Y: {}".format(p == int(y), p, int(y)))

import tensorflow as tf

import numpy as np

tf.set_random_seed(777)  # for reproducibility

xy = np.loadtxt('data-03-diabetes.csv', delimiter=',', dtype=np.float32)

x_data = xy[:, 0:-1]

y_data = xy[:, [-1]]

print(x_data.shape, y_data.shape)

# placeholders for a tensor that will be always fed.

X = tf.placeholder(tf.float32, shape=[None, 8])

Y = tf.placeholder(tf.float32, shape=[None, 1])

W = tf.Variable(tf.random_normal([8, 1]), name='weight')

b = tf.Variable(tf.random_normal([1]), name='bias')

# Hypothesis using sigmoid: tf.div(1., 1. + tf.exp(-tf.matmul(X, W)))

hypothesis = tf.sigmoid(tf.matmul(X, W) + b)

# cost/loss function

cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) *

                       tf.log(1 - hypothesis))

train = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)

# Accuracy computation

# True if hypothesis>0.5 else False

predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)

accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))

# Launch graph

with tf.Session() as sess:

    # Initialize TensorFlow variables

    sess.run(tf.global_variables_initializer())

    for step in range(10001):

        cost_val, _ = sess.run([cost, train], feed_dict={X: x_data, Y: y_data})

        if step % 200 == 0:

            print(step, cost_val)

    # Accuracy report

    h, c, a = sess.run([hypothesis, predicted, accuracy],

                       feed_dict={X: x_data, Y: y_data})

    print("\nHypothesis: ", h, "\nCorrect (Y): ", c, "\nAccuracy: ", a)

import tensorflow as tf

tf.set_random_seed(777)  # for reproducibility

x_data = [[73., 80., 75.],

          [93., 88., 93.],

          [89., 91., 90.],

          [96., 98., 100.],

          [73., 66., 70.]]

y_data = [[152.],

          [185.],

          [180.],

          [196.],

          [142.]]

# placeholders for a tensor that will be always fed.

X = tf.placeholder(tf.float32, shape=[None, 3])

Y = tf.placeholder(tf.float32, shape=[None, 1])

W = tf.Variable(tf.random_normal([3, 1]), name='weight')

b = tf.Variable(tf.random_normal([1]), name='bias')

# Hypothesis

hypothesis = tf.matmul(X, W) + b

# Simplified cost/loss function

cost = tf.reduce_mean(tf.square(hypothesis - Y))

# Minimize

optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-5)

train = optimizer.minimize(cost)

# Launch the graph in a session.

sess = tf.Session()

# Initializes global variables in the graph.

sess.run(tf.global_variables_initializer())

for step in range(2001):

    cost_val, hy_val, _ = sess.run([cost, hypothesis, train], feed_dict={X: x_data, Y: y_data})

    if step % 10 == 0:

        print(step, "Cost: ", cost_val, "\nPrediction:\n", hy_val)

==> 결과

... 

2000 cost:  7.4047155

prediction:

 [[154.63629]

 [182.36778]

 [181.60802]

 [197.86899]

 [137.86888]]

import numpy as np

xy = np.loadtxt('data-01-test-score.csv', delimiter=',', dtype=np.float32)

x_data = xy[:, 0:-1] # slicing

y_data = xy[:, [-1]]

import tensorflow as tf

tf.set_random_seed(777)  # for reproducibility

filename_queue = tf.train.string_input_producer(['data-01-test-score.csv'], shuffle=False, name='filename_queue')

reader = tf.TextLineReader()

key, value = reader.read(filename_queue)

# Default values, in case of empty columns. Also specifies the type of the

# decoded result.

record_defaults = [[0.], [0.], [0.], [0.]]

xy = tf.decode_csv(value, record_defaults=record_defaults)

# collect batches of csv in

train_x_batch, train_y_batch = tf.train.batch([xy[0:-1], xy[-1:]], batch_size=10) #10 개씩 처리 

# placeholders for a tensor that will be always fed.

X = tf.placeholder(tf.float32, shape=[None, 3])

Y = tf.placeholder(tf.float32, shape=[None, 1])

W = tf.Variable(tf.random_normal([3, 1]), name='weight')

b = tf.Variable(tf.random_normal([1]), name='bias')

# Hypothesis

hypothesis = tf.matmul(X, W) + b

# Simplified cost/loss function

cost = tf.reduce_mean(tf.square(hypothesis - Y))

# Minimize

optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-5)

train = optimizer.minimize(cost)

# Launch the graph in a session.

sess = tf.Session()

# Initializes global variables in the graph.

sess.run(tf.global_variables_initializer())

# Start populating the filename queue.

coord = tf.train.Coordinator()

threads = tf.train.start_queue_runners(sess=sess, coord=coord)

for step in range(2001):

    x_batch, y_batch = sess.run([train_x_batch, train_y_batch])

    cost_val, hy_val, _ = sess.run(

        [cost, hypothesis, train], feed_dict={X: x_batch, Y: y_batch})

    if step % 10 == 0:

        print(step, "Cost: ", cost_val, "\nPrediction:\n", hy_val)

coord.request_stop()

coord.join(threads)

# Ask my score

print("Your score will be ",

      sess.run(hypothesis, feed_dict={X: [[100, 70, 101]]}))

print("Other scores will be ",

      sess.run(hypothesis, feed_dict={X: [[60, 70, 110], [90, 100, 80]]}))

$ python -mpip install -U matplotlib

backend: TkAgg

gvs = optimizer.compute_gradient(cost)

// gvs를 customizing한다. 

apply_gradients = optimizer.apply_gradients(gvs)

....

sess.run(apply_gradients)

$ python3

>>> import tensorflow as tf

>>> x_train = [1,2,3]

>>> y_train = [1,2,3]

>>> W = tf.Variable(tf.random_normal([1]), name='weight')

>>> b = tf.Variable(tf.random_normal([1]), name='bias')

>>> hypothesis = x_train * W + b

>>> cost = tf.reduce_mean(tf.square(hypothesis - y_train))

>>> optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)

>>> train = optimizer.minimize(cost)

>>> sess = tf.Session()

>>> sess.run(tf.global_variables_initializer())

>>> for step in range(2001):

...     sess.run(train)

...     if step % 20 == 0:

...             print(step, sess.run(cost), sess.run(W), sess.run(b))

...

0 8.145951 [-0.13096063] [-0.43867812]

20 0.0741704 [0.87371004] [0.00051325]

40 0.0009571453 [0.9702482] [0.04034551]

   ... 생략 ...

1960 2.7972684e-08 [0.9998057] [0.00044152]

1980 2.5414716e-08 [0.99981487] [0.00042076]

2000 2.3086448e-08 [0.9998234] [0.00040107]

>>> X = tf.placeholder(tf.float32)

>>> Y = tf.placeholder(tf.float32)

>>> for step in range(2001):

...     cost_val, W_val, b_val, _ = sess.run([cost, W, b, train], feed_dict={X: [1,2,3], Y:[1,2,3]})

...     if step % 20 == 0:

...             print(step, cost_val, W_val, b_val)

...

0 2.3086448e-08 [0.9998238] [0.00040011]

20 2.0964555e-08 [0.99983215] [0.00038142]

$ curl https://bootstrap.pypa.io/get-pip.py | python

$ virtualenv --system-site-packages -p python3 /Users/prototyping/machine-learning

$ cd machine-learning 

$ source ./bin/activate

(machine-learning)

~/prototyping/machine-learning

$

$ pip3 install --upgrade tensorflow (실패하면 아래 직접 URL을 지정한다)

$ pip3 install --upgrade https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py3-none-any.whl

// 아이콘 관련

$ npm install clarity-icons --save

$ npm install @webcomponents/custom-elements@1.0.0 --save

// UI CSS

$ npm install clarity-ui --save

// Angular 컴포넌트 

$ npm install clarity-angular --save

// 메인 모듈

import { ClarityModule } from 'clarity-angular';

@NgModule({

imports: [

BrowserModule,

ClarityModule.forRoot()

],

...

})

export class AppModule {} 

"module": {

{

"exclude": [

path.join(process.cwd(), "src/styles.css"),

path.join(process.cwd(), "node_modules/clarity-icons/clarity-icons.min.css"),

path.join(process.cwd(), "node_modules/clarity-ui/clarity-ui.min.css")

]

...

},

{

"include": [

path.join(process.cwd(), "src/styles.css"),

path.join(process.cwd(), "node_modules/clarity-icons/clarity-icons.min.css"),

path.join(process.cwd(), "node_modules/clarity-ui/clarity-ui.min.css")

]

...

}

...

},

"plugins": [

new ConcatPlugin({

"uglify": false,

"sourceMap": true,

"name": "scripts",

"fileName": "[name].bundle.js",

"fileToConcat": [

"node_modules/@webcomponents/custsom-elements/custom-elements.min.js",

"node_modules/clarity-icons/clarity-icons.min.js"

]

})

]

// webpack.config.js에서 clarity-icons.min.js 제거

"plugins": [

new ConcatPlugin({

"uglify": false,

"sourceMap": true,

"name": "scripts",

"fileName": "[name].bundle.js",

"fileToConcat": [

"node_modules/@webcomponents/custsom-elements/custom-elements.min.js"

]

})

]

$ npm install -g @angular/cli

$ ng new <projectName>

$ ng eject --aot --watch

====================================================

Ejection was successful.

To run your builds, you now need to do the following commands:

   - "npm run build" to build.

   - "npm test" to run unit tests.

   - "npm start" to serve the app using webpack-dev-server.

   - "npm run e2e" to run protractor.

Running the equivalent CLI commands will result in an error.

====================================================

Some packages were added. Please run "npm install".

$ npm install

$ npm run build

$ npm start

 10% building modules 3/3 modules 0 activeProject is running at http://localhost:4200/

webpack output is served from /

....

// webpack.config.js

const webpack = require('webpack');

...

resolve: {

  alias: {

    'api': path.resovle(__dirname, 'api/server'),

    ...

  }

}

externals: [ resolveExternals ],

plugins: [ ..., new webpack.ProvidePlugin({ __extends: 'typescript-extends' }) ],

node: { ..., __dirname: true }

// 맨 마지막에 넣음 

function resolveExternals(context, request, callback) {

  return resolveMeteor(request, callback) ||

    callback();

}

function resolveMeteor(request, callback) {

  var match = request.match(/^meteor\/(.+)$/);

  var pack = match && match[1];

  if (pack) {

    callback(null, 'Package["' + pack + '"]');

    return true;

  }

}

"compilerOptions: {

"baseUrl": ".",

"module": "commonjs",

...

"skipLibCheck": true,

"stripInternal": true,

"noImplicitAny": false,

"types": [ "@types/meteor" ]

},

"include": [ ..., "api/**/*.ts" ],

"exclude": [ ..., "api/node_modules", "api" ]

// src/tsconfig.app.json

"exclude": [

   ...,

   "../api/node_modules"

]

// src/tsconfig.spec.json

"exclude": [ "../api/node_modules" ]

$ npm install --save-dev typescript-extends

$ npm install --save-dev @types/meteor

$ npm install --save-dev tmp

$ curl https://install.meteor.com/ | sh

$ meteor create api

$ cd api

// 삭제

api$ rm -rf node_modules client package.json package-lock.json

// 심볼릭 링크

api$ ln -s ../package.json

api$ ln -s ../package-lock.json

api$ ln -s ../node_modules

api$ ln -s ../src/declarations.d.ts

api$ meteor add barbatus:typescript

api$ cd ..

$ npm install --save babel-runtime

$ npm install --save meteor-node-stubs

$ npm install --save meteor-rxjs

{

  "compilerOptions": {

    "allowSyntheticDefaultImports": true,

    "declaration": false,

    "emitDecoratorMetadata": true,

    "experimentalDecorators": true,

    "lib": [

      "dom",

      "es2017"

    ],

    "module": "commonjs",

    "moduleResolution": "node",

    "sourceMap": true,

    "target": "es6",

    "skipLibCheck": true,

    "stripInternal": true,

    "noImplicitAny": false,

    "types": [

      "@types/meteor"

    ]

  },

  "exclude": [

    "node_modules"

  ],

  "compileOnSave": false,

  "atom": {

    "rewriteTsconfig": false

  }

}

// 예) Meteor 클라이언트단 Collection 구성

import { MongoObservable } from 'meteor-rxjs';

export const Chats = new MongoObservable.Collection('chats');

$ sudo npm install -g meteor-client-bundler

$ meteor-client bundle --destination meteor.bundle.js --config bundler.config.json

// config file

{
  "release": "1.6",
  "runtime": {
    "DDP_DEFAULT_CONNECTION_URL": "http://1.0.0.127:8100"
  },
  "import": [
    "accounts-base",
    "mys:accounts-phone",
    "jalik:ufs@0.7.1_1",
    "jalik:ufs-gridfs@0.1.4"
  ]
}

"scripts": {

   ...,

   "meteor-client:bundle": "meteor-client bundle -s api"

}

//번들링 - 최초 한번만 수행한다.

$ npm run meteor-client:bundle

import "meteor-client";

// api/server/models.ts

export enum MessageType {

  TEXT = <any>'text'

}

export interface Chat {

  _id?: string;

  title?: string;

  picture?: string;

  lastMessage?: Message;

  memberIds?: string[];

}

export interface Message {

  _id?: string;

  chatId?: string;

  senderId?: string;

  content?: string;

  createdAt?: Date;

  type?: MessageType;

  ownership?: string;

}

// api/server/collections/chats.ts

import { MongoObservable } from 'meteor-rxjs';

import { Chat } from '../models';

export const Chats = new MongoObservable.Collection<Chat>('chats'); 

// api/server/collections/messages.ts

import { MongoObservable } from 'meteor-rxjs';

import { Message } from '../models';

export const Messages = new MongoObservable.Collection<Message>('messages');

$ npm install --save moment

// api/server/main.ts

import { Meteor } from 'meteor/meteor';

import { Chats } from './collections/chats';

import { Messages } from './collections/messages';

import * as moment from 'moment';

import { MessageType } from './models';

Meteor.startup(() => {

  // code to run on server at startup

  if (Chats.find({}).cursor.count() === 0) {

    let chatId;

    chatId = Chats.collection.insert({

      title: 'Ethan Gonzalez',

      picture: 'https://randomuser.me/api/portraits/thumb/men/1.jpg'

    });

    Messages.collection.insert({

      chatId: chatId,

      content: 'You on your way?',

      createdAt: moment().subtract(1, 'hours').toDate(),

      type: MessageType.TEXT

    });

    chatId = Chats.collection.insert({

      title: 'Bryan Wallace',

      picture: 'https://randomuser.me/api/portraits/thumb/lego/1.jpg'

    });

    Messages.collection.insert({

      chatId: chatId,

      content: 'Hey, it\'s me',

      createdAt: moment().subtract(2, 'hours').toDate(),

      type: MessageType.TEXT

    });

  }

});

// src/app/app.component.ts

import { Component, OnInit } from '@angular/core';

import { Chats } from '../../api/server/collections/chats';

import { Chat } from '../../api/server/models';

@Component({

  selector: 'app-root',

  templateUrl: './app.component.html',

  styleUrls: ['./app.component.css']

})

export class AppComponent implements OnInit {

  title = 'app';

  chats: Chat[];

  ngOnInit() {

    Chats.find({}).subscribe((chats: Chat[]) => this.chats = chats );

  }

}

// src/app/app.component.html 에 추가

<div> {{ chats | json }} </div>

$ cd api

api$ meteor

$ npm start

$ meteor mongo

MongoDB shell version: 3.2.15

connecting to: 127.0.0.1:3001/meteor

Welcome to the MongoDB shell.

For interactive help, type "help".

For more comprehensive documentation, see

http://docs.mongodb.org/

Questions? Try the support group

http://groups.google.com/group/mongodb-user

meteor:PRIMARY> show collections

chats

messages

$ npm install -g ionic cordova

$ ionic start whatsapp blank --cordova --skip-link

...

? Install the free Ionic Pro SDK and connect your app? (Y/n) n

$ cd whatsapp

$ ionic serve

$ npm install --save typescript

//  package.json

"config": {

    "ionic_webpack": "./webpack.config.js"

}

$ cp node_modules/@ionic/app-scripts/config/webpack.config.js .

// dev, prod양쪽에 넣는다.

resolve: {

...

alias: {

'api': path.resolve(__dirname, 'api/server')

}

},

externals: [ resolveExternals ],

plugins: [ ..., new webpack.ProvidePlugin({ __extends: 'typescript-extends' }) ],

node: { ..., __dirname: true }

// 맨 마지막에 넣음 

function resolveExternals(context, request, callback) {

  return resolveMeteor(request, callback) ||

    callback();

}

function resolveMeteor(request, callback) {

  var match = request.match(/^meteor\/(.+)$/);

  var pack = match && match[1];

  if (pack) {

    callback(null, 'Package["' + pack + '"]');

    return true;

  }

}

"compilerOptions": {

  "baseUrl": ".",

  "modules": "commonjs",

  "paths": { "api/*": ["./api/server/*"],

  ...,

  "skipLibCheck": true,

  "stripInternal": true,

  "noImplicitAny": false,

  "types": [

      "@types/meteor"

  ] 

},

"include": [ ..., "api/**/*.ts" ],

"exclude": [ ..., "api/node_modules", "api" ]

$ npm install --save-dev typescript-extends

$ npm install --save-dev @types/meteor

// Chrome devtools 메세지

Native: tried calling StatusBar.styleDefault, but Cordova is not available. Make sure to include cordova.js or run in a device/simulator

// src/app/app.component.ts 설정

// 미티어 설치

$ curl https://install.meteor.com/ | sh

// api 폴더 미티어 초기화

$ meteor create api

$ rm -rf api/node_modules

$ rm -rf api/client

$ rm api/package.json

$ rm api/package-lock.json

$ cd api

api$ ln -s ../package.json

api$ ln -s ../pack-lock.json

api$ ln -s ../node_modules

api$ ln -s ../src/declarations.d.ts

api$ meteor add barbatus:typescript

// 별도 필요 패키지 설치 

$ npm install --save babel-runtime

$ npm install --save meteor-node-stubs

$ npm install --save meteor-rxjs

{

  "compilerOptions": {

    "allowSyntheticDefaultImports": true,

    "declaration": false,

    "emitDecoratorMetadata": true,

    "experimentalDecorators": true,

    "lib": [

      "dom",

      "es2015"

    ],

    "module": "commonjs",

    "moduleResolution": "node",

    "sourceMap": true,

    "target": "es5",

    "skipLibCheck": true,

    "stripInternal": true,

    "noImplicitAny": false,

    "types": [

      "@types/meteor"

    ]

  },

  "exclude": [

    "node_modules"

  ],

  "compileOnSave": false,

  "atom": {

    "rewriteTsconfig": false

  }

}

// 예) Meteor 클라이언트단 Collection 구성

import { MongoObservable } from 'meteor-rxjs';

export const Chats = new MongoObservable.Collection('chats');

$ sudo npm install -g meteor-client-bundler

// 필요 패키지를 설치

$ npm install --save-dev tmp

// package.json

"scripts": {

...,

"meteor-client:bundle": "meteor-client bundle -s api"

}

// 번들링

$ npm run meteor-client:bundle

// src/app/main.ts

import "meteor-client";

$ ionic serve

$ cd api

api$ meteor

$> npm i -g @angular/cli

or 

$> yarn add global @angular/cli

$> ng new jamong

$> ng serve

$> npm run build && npm start

// package.json의 build 와 start 스크립트 설정내역

  "scripts": {

    "ng": "ng",

    "start": "webpack-dev-server --port=4200",

    "build": "webpack",

    "test": "karma start ./karma.conf.js",

    "lint": "ng lint",

    "e2e": "protractor ./protractor.conf.js",

    "pree2e": "webdriver-manager update --standalone false --gecko false --quiet"

  },

$> yarn add webpack --dev

또는

$> yarn add global webpack 

또는 

$> npm install -g webpack

$> webpack <entry file path> <output bundle file path>

예) webpack ./entry.js bunlde.js

module.exports = {

  context: __dirname + '/app',

  entry: {

    app: './app.js'

  },

  output: {

    path: __dirname + '/dist',

    filename: '[name].bundle.js'

  }

}

환경파일안에 module 밑으로 설정한다. 

module.exports = {

  entry: ...

  output: ...

  module: {

    rules: [

      {

        test: /\.css$/,

        use: [ 'style-loader', 'css-loader']

      }

    ]

    ...

  }

}

{

        "enforce": "pre",

        "test": /\.js$/,

        "loader": "source-map-loader",

        "exclude": [

          /(\\|\/)node_modules(\\|\/)/

        ]

}

// 전체 및 부분 리로딩 옵션 설정

$> webpack-dev-server --inline --hot

"plugins": [

    new NoEmitOnErrorsPlugin(),

    new GlobCopyWebpackPlugin({

      "patterns": [

        "assets",

        "favicon.ico"

      ],

      "globOptions": {

        "cwd": path.join(process.cwd(), "src"),

        "dot": true,

        "ignore": "**/.gitkeep"

      }

    }),

    new ProgressPlugin(),

    new CircularDependencyPlugin({

      "exclude": /(\\|\/)node_modules(\\|\/)/,

      "failOnError": false

    }),

    new NamedLazyChunksWebpackPlugin(),

    new HtmlWebpackPlugin({

      "template": "./src/index.html",

      "filename": "./index.html",

      "hash": false,

      "inject": true,

      "compile": true,

      "favicon": false,

      "minify": false,

      "cache": true,

      "showErrors": true,

      "chunks": "all",

      "excludeChunks": [],

      "title": "Webpack App",

      "xhtml": true,

      "chunksSortMode": function sort(left, right) {

        let leftIndex = entryPoints.indexOf(left.names[0]);

        let rightindex = entryPoints.indexOf(right.names[0]);

        if (leftIndex > rightindex) {

          return 1;

        } else if (leftIndex < rightindex) {

          return -1;

        } else {

          return 0;

        }

      }

    }),

    new BaseHrefWebpackPlugin({}),

    new CommonsChunkPlugin({

      "name": [

        "inline"

      ],

      "minChunks": null

    }),

    new CommonsChunkPlugin({

      "name": [

        "vendor"

      ],

      "minChunks": (module) => {

        return module.resource &&

          (module.resource.startsWith(nodeModules) ||

            module.resource.startsWith(genDirNodeModules) ||

            module.resource.startsWith(realNodeModules));

      },

      "chunks": [

        "main"

      ]

    }),

    new SourceMapDevToolPlugin({

      "filename": "[file].map[query]",

      "moduleFilenameTemplate": "[resource-path]",

      "fallbackModuleFilenameTemplate": "[resource-path]?[hash]",

      "sourceRoot": "webpack:///"

    }),

    new CommonsChunkPlugin({

      "name": [

        "main"

      ],

      "minChunks": 2,

      "async": "common"

    }),

    new NamedModulesPlugin({}),

    new AotPlugin({

      "mainPath": "main.ts",

      "replaceExport": false,

      "hostReplacementPaths": {

        "environments/environment.ts": "environments/environment.ts"

      },

      "exclude": [],

      "tsConfigPath": "src/tsconfig.app.json",

      "skipCodeGeneration": true

    })

]

plugins: [

        new BrowserSyncPlugin({

            host: 'localhost',

            port: 9000,

            proxy: {

                target: 'http://localhost:9060',

                ws: true

            }

        }, {

            reload: false

        }),

        new webpack.NoEmitOnErrorsPlugin(),

        new webpack.NamedModulesPlugin(),

        new writeFilePlugin(),

        new webpack.WatchIgnorePlugin([

            utils.root('src/test'),

        ]),

        new WebpackNotifierPlugin({

            title: 'JHipster',

            contentImage: path.join(__dirname, 'logo-jhipster.png')

        })

 ]

 "scripts": {

    "ng": "ng",

    "start": "webpack-dev-server --port=4200",

    "build": "webpack",

    "test": "karma start ./karma.conf.js",

    "lint": "ng lint",

    "e2e": "protractor ./protractor.conf.js",

    "pree2e": "webdriver-manager update --standalone false --gecko false --quiet"

}

"scripts": {

    "lint": "tslint --type-check --project './tsconfig.json' -e 'node_modules/**'",

    "lint:fix": "yarn run lint -- --fix",

    "ngc": "ngc -p tsconfig-aot.json",

    "cleanup": "rimraf target/{aot,www}",

    "clean-www": "rimraf target//www/app/{src,target/}",

    "start": "yarn run webpack:dev",

    "serve": "yarn run start",

    "build": "yarn run webpack:prod",

    "test": "karma start src/test/javascript/karma.conf.js",

    "test:watch": "yarn test -- --watch",

    "webpack:dev": "yarn run webpack-dev-server -- --config webpack/webpack.dev.js --progress --inline --hot --profile --port=9060",

    "webpack:build:main": "yarn run webpack -- --config webpack/webpack.dev.js --progress --profile",

    "webpack:build": "yarn run cleanup && yarn run webpack:build:main",

    "webpack:prod:main": "yarn run webpack -- --config webpack/webpack.prod.js --progress --profile",

    "webpack:prod": "yarn run cleanup && yarn run webpack:prod:main && yarn run clean-www",

    "webpack:test": "yarn run test",

    "webpack-dev-server": "node --max_old_space_size=4096 node_modules/webpack-dev-server/bin/webpack-dev-server.js",

    "webpack": "node --max_old_space_size=4096 node_modules/webpack/bin/webpack.js",

    "e2e": "protractor src/test/javascript/protractor.conf.js",

    "postinstall": "webdriver-manager update && node node_modules/phantomjs-prebuilt/install.js"

}

$ npm install -g @angular/cli 

$ ng new angular-docker

$ cd angular-docker

$ ng build

Hash: cfba09939df99de45615

Time: 7424ms

chunk    {0} polyfills.bundle.js, polyfills.bundle.js.map (polyfills) 165 kB {4} [initial] [rendered]

chunk    {1} main.bundle.js, main.bundle.js.map (main) 3.61 kB {3} [initial] [rendered]

chunk    {2} styles.bundle.js, styles.bundle.js.map (styles) 9.77 kB {4} [initial] [rendered]

chunk    {3} vendor.bundle.js, vendor.bundle.js.map (vendor) 2.09 MB [initial] [rendered]

chunk    {4} inline.bundle.js, inline.bundle.js.map (inline) 0 bytes [entry] [rendered]

// Delete all docker containers

$ docker rm $(docker ps -a -q)

// Delete all docker images

$ docker rmi $(docker images -q)

// Install docker image

$ docker pull nginx

Using default tag: latest

// 설치 내역 확인

$ docker images 

REPOSITORY        TAG                 IMAGE ID            CREATED             SIZE

nginx                  latest              5766334bdaa0   2 weeks ago         183 MB

$ cd dist

$ docker run -d -p 8080:80 -v $(pwd):/usr/share/nginx/html nginx:latest

48db2f0626c5a10429e95cbbbaf3cc58769cda45a9c1e7084b4f3a260e576838

// 이미지 실행 확인

$ docker ps -a

CONTAINER ID        IMAGE               COMMAND                  CREATED             STATUS              PORTS                           NAMES

48db2f0626c5        nginx:latest        "nginx -g 'daemon ..."   2 minutes ago       Up 2 minutes        443/tcp, 0.0.0.0:8080->80/tcp   loving_lumiere

// 멈춤 

$ docker stop 48

48

// 제거

$ docker rm 48

48

// 확인

$ docker ps -a

CONTAINER ID        IMAGE               COMMAND             CREATED             STATUS              PORTS               NAMES

version: '2'

services:

  angular-seed:

    build:

      context: .

      dockerfile: ./.docker/angular-seed.development.dockerfile

    command: npm start

    container_name: angular-seed-start

    image: angular-seed

    networks:

      - dev-network

    ports:

      - '5555:5555'

networks:

  dev-network:

    driver: bridge

$ docker-compose build

$ docker-comopse up

$ brew update && brew install rethinkdb

또는 

다운로드 (.dmg)

$ docker run -d -P --name rethink1 rethinkdb

$ which rethinkdb

/usr/local/bin/rethinkdb

$ rethinkdb --bind all

Running rethinkdb 2.3.5 (CLANG 7.3.0 (clang-703.0.31))...

Running on Darwin 16.5.0 x86_64

Loading data from directory /Users/dowonyun/opensource/rethinkdb_data

Listening for intracluster connections on port 29015

Listening for client driver connections on port 28015

Listening for administrative HTTP connections on port 8080

$ npm init -y

$ npm i rethinkdb --save

// connect.js

r = require('rethinkdb');

let connection = null;

r.connect({ host: 'localhost', port: 28015 }, function (err, conn) {

    if (err) throw err;

    connection = conn;

    r.db('test').tableCreate('authors').run(connection, function (err, result) {

        if (err) throw err;

        console.log(JSON.stringify(result, null, 2));

    })

})

$ node connect.js

{

  "config_changes": [

    {

      "new_val": {

        "db": "test",

        "durability": "hard",

        "id": "05f4b262-9e01-477e-a74c-3d4ccb14cf84",

        "indexes": [],

        "name": "authors",

        "primary_key": "id",

        "shards": [

          {

            "nonvoting_replicas": [],

            "primary_replica": "DowonYunui_MacBook_Pro_local_bcb",

            "replicas": [

              "DowonYunui_MacBook_Pro_local_bcb"

            ]

          }

        ],

        "write_acks": "majority"

      },

      "old_val": null

    }

  ],

  "tables_created": 1

}

r.table('users').pluck('last_name').distinct().count().run(conn)

$ npm install -g horizon

$ hz init realtime_app

Created new project directory realtime_app

Created realtime_app/src directory

Created realtime_app/dist directory

Created realtime_app/dist/index.html example

Created realtime_app/.hz directory

Created realtime_app/.gitignore

Created realtime_app/.hz/config.toml

Created realtime_app/.hz/schema.toml

Created realtime_app/.hz/secrets.toml

$ hz serve --dev

App available at http://127.0.0.1:8181

RethinkDB

   ├── Admin interface: http://localhost:55584

   └── Drivers can connect to port 55583

Starting Horizon...

🌄 Horizon ready for connections

$ npm i -g gulp-cli

$ npm i -g yarn

$ mkdir livereload && cd livereload

$ yarn init -y

yarn init v0.21.3

warning The yes flag has been set. This will automatically answer yes to all questions which may have security implications.

success Saved package.json

✨  Done in 0.06s.

$ touch index.html

$ yarn add --dev gulp

$ touch Gulpfile.ts

// Gulpfile.js

const gulp = require('gulp');

gulp.task('default', function () {

  console.log('Gulp and running!')

});

// test

$ gulp

[09:27:23] Using gulpfile ~/prototyping/livereload/gulpfile.ts

[09:27:23] Starting 'default'...

Gulp and running!

[09:27:23] Finished 'default' after 71 μs

$ yarn add --dev express

// Gulpfile.ts

const gulp = require('gulp');

function startExpress() {

  const express = require('express');

  const app = express();

  app.use(express.static(__dirname));

  app.listen(4000);

}

gulp.task('default', () => startExpress());

<!DOCTYPE html>

<html>

  <head>

    <meta charset="utf-8">

    <title></title>

  </head>

  <body>

    hi peter

  </body>

</html>

$ yarn add --dev tiny-lr

const gulp = require('gulp');

function startExpress() {

  const express = require('express');

  const app = express();

  app.use(express.static(__dirname));

  app.listen(4000);

}

function startLivereload() {

  const lr = require('tiny-lr')();

  lr.listen(35729);

}

gulp.task('default', () => {

  startExpress();

  startLivereload();

});

$ yarn add --dev connect-livereload

// Gulpfile.ts

function startExpress() {

  const express = require('express');

  const app = express();

  app.use(require('connect-livereload')());

  app.use(express.static(__dirname));

  app.listen(4000);

}

const gulp = require('gulp');

function startExpress() {

  const express = require('express');

  const app = express();

  app.use(require('connect-livereload')());

  app.use(express.static(__dirname));

  app.listen(4000);

}

let lr;

function startLivereload() {

  lr = require('tiny-lr')();

  lr.listen(35729);

}

function notifyLivereload(event) {

  // `gulp.watch()` events 는 절대경로를 주기 때문에 상대 경로(relative)를 만든다.

  var fileName = require('path').relative(__dirname, event.path);

  lr.changed({

    body: {

      files: [fileName]

    }

  });

}

gulp.task('default', () => {

  startExpress();

  startLivereload();

  gulp.watch('*.html', notifyLivereload);

});

"configuration": [

    {

            "type": "node",

            "request": "launch",

            "name": "Grunt Directly",

            "args": ["serve"],            

            "program": "/Users/peter/.nvm/versions/node/v6.10.1/bin/grunt", 

            "stopOnEntry": true,

            "cwd": "${workspaceRoot}"

      },

...

]

$ npm init -y

$ npm i -g typescript@latest

$ npm i -g ts-node

$ tsc --init

{

    "compilerOptions": {

        "module": "commonjs",

        "target": "es5",

        "noImplicitAny": false,

        "sourceMap": false

    },

    "typeRoots": ["node_modules/@types"],

    "exclude": [

        "node_modules"

    ]

}

$ touch index.ts

$ vi index.ts

const hi = 'hello peter';

console.log(hi);

$ npm i nodemon --save-dev

$ vi package.json

{

...

  "scripts": {

    "start": "npm run build:live",

    "build:live": "nodemon --exec ./node_modules/.bin/ts-node  ./index.ts"

  },

...

}

$ npm start

> typescript@1.0.0 start /Users/dowonyun/prototyping/typescript

> npm run build:live

> typescript@1.0.0 build:live /Users/dowonyun/prototyping/typescript

> nodemon --exec ./node_modules/.bin/ts-node ./index.ts

[nodemon] 1.11.0

[nodemon] to restart at any time, enter `rs`

[nodemon] watching: *.*

[nodemon] starting `./node_modules/.bin/ts-node ./index.ts`

hello peter

[nodemon] clean exit - waiting for changes before restart

$ npm install --save ts-node 

$ ts-node ./index.ts