Month: May 2017

CRUD

Create, Read, Update, Delete

In order to open and see the list of all the pets in the shelter taking read actions on the table.
Tapping on the entry in the CatalogActivity in order to see information about specific pet require Read.
From the EditorActivity, changing the weight of a specific pet requires taking Update actions on the table.
Using the option from the overflow menu that removes incorrectly entered pets requires Delete.
Selecting the “Insert Pet” option from the overflow menu to insert a list of default information requires taking Create actions on the table.

Types in SQLite

storage class, data types
NULL, INTEGER, REAL, TEXT, BLOB

No booleans, use INTEGER instead
Ex:
false = 0
true = 1

attribute
Name TEXT
Price INTEGER
Style INTEGER
In Stock INTEGER
Description TEXT

CREATE TABLE headphones (_id INTEGER, name TEXT,
	price INTEGER,
	style INTEGER,
	in_stock INTEGER,
	description TEXT);


CREATE TABLE pets (_id INTEGER, 
	name TEXT,
	bread TEXT,
	gender INTEGER,
	weight INTEGER);

Computer Memory

Temporary Storage:Short term
-can be used for calculations or displaying data on screen
-quick to access
-short-lived
e.g.
-computer RAM

Permanent Storage:Long term
-can be used for storing user data
-slower to save and access data
-stick around forever(until deleted)
e.g.
-Hard disk, Flash drives

Different Data Storage Options
Files: good for saving large media files
SharedPreferences: Good for in-app user preferences, key and value, Unique String and primitive types and strings
SQLight Databases: Good for organizing a lot of related and structured data for easy access: Row, Column
commonly used for text data, easily grows in size and easily searchable

PS C:\Users\xxx> sqlite3 -version
3.15.0 2016-10-14 10:20:30 707875582fcba352b4906a595ad89198d84711d8

PS C:\Users\xxx> sqlite3
SQLite version 3.15.0 2016-10-14 10:20:30
Enter ".help" for usage hints.
Connected to a transient in-memory database.
Use ".open FILENAME" to reopen on a persistent database.
sqlite> .open pets.db
sqlite>

プッシュ通知

スマホのプッシュ通知
iOS:Apple Push Notification Service(APNs)
Android:Google Cloud Messaging(GCM)

①Apple / google よりデバイストークンを取得
②アプリ用サーバにデバイストークンを登録
(ユーザーIDや端末IDと紐づけて送信)
③デバイストークンとメッセージを送信

$ curl \
--header "Authorization:key=【APIキー】"\
--header "Content-Type:\"application/json\""\
https://android.googleapis.com/gcm/send\
-d"{\"registration_ids\":[\"【RegistrationID】\"],\"data\":
{\"message\":/"Hello monotty!\"}}"

iOSのプッシュ通知
Command, Frame data(Item, Item)
device_id, os, device_token

AndroidはテキストベースのシンプルなHTTP通信で送信できる
iOSは、ApnsPHPを使う

function connectAPSN($sslclient,$pem_path,$passphrase){
	$ctx = stream_content_create();
	stream_context_set_option($ctx, 'ssl', 'local_cert', $pem_path);
	stream_context_set_option($ctx, 'ssl', 'passphrase', $passphrase);
	$fp = stream_socket_client($sslclient, $err,
		$errstr, 60, STREAM_CLIENT_CONNECT|STREAM_CLIENT_PERSISTENT, $ctx);
	if (!$fp){
		echo "接続エラーになったので1秒後再接続を試みます。メメタア!". PHP_EOL;
	sleep(1);
	$fp = connectAPSN($sslclient,$pem_path,$passphrase);
	return $fp;
	} else {
		echo "APNS接続OK" . PHP_EOL;
		return $fp;
	}

	// 送信処理
	$passphrase = 'password';
	$pem_path = '/xxx/xxx.pem';
	$sslclient = 'ssl://gateway.sandbox.push.apple.com:2195';
	$fp = connectAPSN($sslclient,$pem_path,$passphrase);

Calculating a confidence interval

P^ = x/N
P^ = 100/1000 = 0.1

m = z*se
m = z * √p^(1-p^)/n
m = 0.019
z distribution μ=0, σ=1 -1.96, 1.96

N = 2000, x = 300
p^ = 300 / 2000
center of confident 0.15

Hypothesis Testing
P(results due to chance)
Pcont, Pexp
Pcont = Pexp
Pexp-Pcont = 0

Size vs. Power Trade-Off
How many page views
α = P(reject null | null true)

A/B testing

AB test is using data like user behavior to make a decision.
Which part of the future is better.
AB testing rather than useful for new experiment.

Overview
Example
Choose a metric
Review statistics
Design
Analyze

Examples of when to use A/B testing
・Movie recommendation site: new ranking algorithm
・Change backend-page load time, results users see etc.
・Test layout of initial page

probability
Repeated measurement of click-through-probability
visitors = 1000
unique clicks = 10
click-through-probability ≒10%

Binominal Distribution
@ p = 3/4
mean = P
std dev = √p(1-p)/N
P^ = 16/20 = 4/5

types of outcomes
independent events
identical distribution

Antilock Braking System

Two types of friction
static: moves when F > μs・N
kinetic: F = μk * N

wheel slip
rolling, rocked

speed = 120 km/h
μ=1.0: 3.4s
μ=0.7: 4.9s

F = m*a
u*gravitational force(mg)
a = μ*g

t/ 120km/h = 1 / μg
t = 120km/h / μ*g

Computing the coefficient of Friction
s = 1 – w/v
F(s) = μ(s)*mqcG
V= -F(s)/mqc
F = ma
w = F(s)/mew – B

import math
from **** import *

h = 0.01
mass_quarter_car = 250.
mass_effective_wheel = 20.
g = 9.81

end_time = 5.
num_steps = int(end_time / h)

w = numpy.zeros(num_steps + 1)
v = numpy.zeros(num_steps + 1)
x = numpy.zeros(num_steps + 1)
times = h * numpy.array(range(num_steps + 1))

@show_plot(7, 7)
def plot_me():
	axes_x = matplotlib.pyplot.subplot(411)
	axes_v = matplotlib.pyplot.subplot(412)
	axes_w = matplotlib.pyplot.subplot(413)
	awes_s = matplotlib.pyplot.subplot(414)

	def friction_coeff(slip):
		return 1.1 * (1. - math.exp(-20. * slip)) - 0.4 * slip

	def wheel_slip():
		b_values = numpy.arange(70., 190.1, 30.)
		for b in b_values:
			x[0] = 0.

			for step in range(num_steps):
				if v[step] < 0.01:
					break
				s = max(0., 1. - w[step] / v[step])

				w[step + 1] = max(0., w[step + 1])

			axes_x.plot(times[:step], x[:step])
			axes_v.plot(times[:step], v[:step])
			axes_w.plot(times[:step], w[:step])
			axes_s.plot(times[:step], 1. - w[:step] / v[:step])
			p = int((0.35 + 0.4 * (b - b_values[0])/ (b_values[-1] - b_values[0])) * num_steps)
			axes_x.annotate(b, (times[p], x[p]),
					xytext = (-30, -30), textcoords = 'offset point',
					arrowprops = dict(arrowstyle = '-', connectionstyle = 'arc3, '))

			p = int((0.35 + 0.4 * (v - b_values[0]) / (b_values[-1] - b_values[0])) * num_steps)
			axes_x.annotate(b, (times[p], x[p]),
				xytext = (-30, -30), textcoords - 'offset points',
				arrowprops = dict(arrowstyle = '-', connectionstyle = 'arc3, rad = 0.2', shrinkB = 0.))

		return x, v, w

    axes_x.set_ylabel('Position\nin m', multialignment = 'center')
    axes_v.set_ylabel('Car velocity\nin m/s', multialignment = 'center')
    axes_w.set_ylabel('Wheel velocity\nin m/s', multialignment = 'center')
    axes_s.set_ylabel('Wheel\nslip', multialignment = 'center')
    axes_s.set_xlabel('Time in s')
    axes_s.set_ylim(0., 1.)

    return wheel_slip()

x, v, w = plot_me()

Tangent

y=2^x, y=3^x
the slope of the tangent line at x < 1, x > 1

the exponential function

y = e^x -> slope of the tange line is exactly 1.

e = lim n->∞(1 + 1/N)^N = 2.7
e^π = (1+ 3.1416/10,000)^10,000

(1 + x/4)^4 = 1 + x + 3/8x^2 + 1/16x^3 + 1/256x^4

e^x = 1 + x + x^2/2 + x^3/6 + x^4/24 + …
e^x+h = e^x * e^h

dy(x)/dx = -3y(x)
y(0) = 5