Plotting stock price data

import pandas as pd
import matplotlib.pyplot as plt

def test_run():
	df = pd.read_csv("data/APPL.csv")
	print df['Adj Close']
	df['Adj Close'].plot()
	plot.show()

if __name__ == "__main__":
	test_run()

Here we go

import pandas as pd
import matplotlib.pyplot as plt

def test_run():
	df = pd.read_csv("data/IBM.csv")
	df['High'].plot()
	plot.show()

if __name__ == "__main__":
	test_run()

plot two column, you can observe two lines

import pandas as pd
import matplotlib.pyplot as plt

def test_run():
	df = pd.read_csv("data/APPL.csv")
	df[['Close','Adj Close']].plot()
	plot.show()

if __name__ == "__main__":
	test_run()

unsupported operand type(s) for +: ‘int’ and ‘str’

Compute mean volume

    df = pd.read_csv("data/{}.csv".format(symbol))  # read in data
    s = sum(df)
    l = len(df)
    print(s/l)

unsupported operand type(s) for +: 'int' and 'str'

TypeError showed.

We must calculate dataframe mean.

import pandas as pd

def get_mean_volume(sympol):
	df = pd.read_csv("data/{}.csv".format(symbol))
    print(df.mean())

def test_run():
	for symbol in ['AAPL', 'IBM']:
		print "Mean Volume"
		print symbol, get_mean_volume(symbol)

if __name__ == "__main__": # if run standalone
	test_run()

Mean Volume
AAPL Open         1.363176e+02
High         1.380075e+02
Low          1.344201e+02
Close        1.362885e+02
Volume       2.149143e+07
Adj Close    1.282174e+02
dtype: float64
None
Mean Volume
IBM Open         1.109328e+02
High         1.121182e+02
Low          1.098853e+02
Close        1.110325e+02
Volume       7.103571e+06
Adj Close    1.022113e+02
dtype: float64
None

Here is a solution

import pandas as pd

def get_mean_volume(sympol):
	df = pd.read_csv("data/{}.csv".format(symbol))
    return df['Volume'].mean()

def test_run():
	for symbol in ['AAPL', 'IBM']:
		print "Mean Volume"
		print symbol, get_mean_volume(symbol)

if __name__ == "__main__": # if run standalone
	test_run()

Mean Volume
AAPL 21491431.3386
Mean Volume
IBM 7103570.80315

python for stock data

features:
1. strong scientific libraries
2. strongly maintained
3. fast

install pandas into Centos

$ sudo easy_install pandas

>>> import numpy
>>> numpy.version.version
'1.13.3'

Print last 5 rows of the data frame

import pandas as pd


def test_run():
    df = pd.read_csv("data/AAPL.csv")
    print(df[-5:])

if __name__ == "__main__":
    test_run()

compute max closing price of Apple and IBM

import pandas as pd

def get_max_close(sympol)
	
	df = pd.read_csv("data/{}.csv".format(symbol))
	return df['Close'].max()

def test_run():
	for symbol in ['AAPL', 'IBM']:
		print "Max close"
		print symbol, get_max_close(symbol)

if __name__ == "__main__": # if run standalone
	test_run()

Creating Paper Transformations

ViewAnimationUtils.createCircularReveal(
View view,
int centerX,
int centerY,
float startRadius,
float endRadius
)

@Override
public void onClick(View view){
	boolean isVeggie = ((ColorDrawable)viewe.getBackground()) != null && ((ColorDrawable)view)

	int finalRadius = (int)Math.hypot(view.getWidth()/2, view.getHeight()/2);

	if (isVeggie){
		text1.setText(baconTitle);
		text2.setText(baconText);
		view.setBackgroundColor(white);
	} else {
		Animator anim = ViewAnimationUnits.createCircularReveal(
			view, (int) view.getWidth()/2, (int) view.getHeight()/2, 0, finalRadius);
		text1.setText(veggieTitle);
		text2.setText(veggieText);
		view.setBackgroundColor(green);
		anim.start();
	}
}

res/layout/activity.xml

<android.support.design.widget.CoordinatorLayout
	xmlns:android="http://schemas.android.com/apk/res.android"
	xmlns:app="http://schemas.android.com/apk/res-auto"
	...>

CoordinatorLayout
->AppBarLayout
android:layout_height = “168dp”
android:background=”@color/indigo_500″
-> CollapsingToolbarLayout
app:layout_scrollFlags=”scroll|exitUnitCollapsed”>
-> Toolbar
android:layout_height=”56dp”
app:layout_collapseMode=”pin” />
RecyclerView
app:layout_behavior=”@string/appbar_scrolling_view_behavior” />

Color palette

<?xml version="1.0" encoding="utf-8"?>
<resources>
	<color name="indigo_300">#7986CB</color>
	<color name="indigo_500">#3F51B5</color>
	<color name="indigo_700">#303F9F</color>
	<color name="pink_a200">#FF4081</color>
</resources>

https://developer.android.com/training/material/theme.html

Fonts within a font family and weight, style

onClick

TransitionManager.go(
	Scene.getSceneForLayout(
	(ViewGroup) findViewById(R.id.root),
	R.layout.activity_main_scene_info,
	MainActivity.this));

res/transition/grid_exit.xml
<explode xmlns... />

res/values/styles.xml
<style name="AppTheme.Home">
	<item name="android:windowExitTransition">
		@transition/grid_exit</item>
</style>

Implementing Surfaces

<LinearLayout
	android:layout_width="match_parent"
	android:layout_height="wrap_content"
	android:orientation="vertical">

	<FrameLayout
		android:layout_width="match_parent"
		android:layout_height="200dp"
		android:layout_margin="16dp"
		android:background="#fff"
		android:elevation="4dp" />

	<FrameLayout
		android:layout_width="match_parent"
		android:layout_height="200dp"
		android:layout_margin="16dp"
		android:background="#fff"
		android:elevation="8dp" />

	<FrameLayout
		android:layout_width="match_parent"
		android:layout_height="200dp"
		android:layout_margin="16dp"
		android:background="#fff"
		android:elevation="16dp" />

</LinearLayout>

f-a-b => FAB

dependencies {
	compile fileTree(dir: 'libs', include: ['*.jar'])
	compile 'com.android.support:appcompat-v7:22.2.0'
	compile 'com.android.support:design:22.2.0'
}

activity_main.xml

<android.support.design.widget.FloatingActionButton
	app:fabSize="normal"
	app:elevation="6dp"
	app:layout_gravity="end"
	app:pressedTranslationZ="12dp"

	android:id="@+id/fab"
	android:src="@drawable/fab_plus"

	android:layout_height="wrap_content"
	android:layout_width="wrap_content"
	android:layout_margin="16dp"
/>

styles.xml

<?xml version="1.0" encoding="utf-8"?>
<resources>
	<style name="AppTheme" parent="android:Theme.Material.Light">
	</style>
</resources>

<?xml version="1.0" encoding="utf-8"?>
<resources>
	<style name="AppTheme" parent="Theme.AppCompat.Light">
	</style>
</resources>

Android Design

Working with density-independent pixels
1px / 1dp = 160dpi / 160dpi
2px / 1dp = 320dpi / 160dpi
※ dpi = dot per inch
7inch nexus^7 1280x800pixel => 960x600dp

Density buckets
LDPI, MDPI, HDPI, XHDPI, XXHDPI, XXXHDPI

res/drawable/checkbox.xml

<selector>
	<item android:state_pressed="true"
		android:state_checked="true"
		android:drawable="@drawable/box_checked_pressed">

	<item android:state_pressed="true"
		android:drawable="@drawable/box_pressed">

	<item android:state_checked="true"
		android:drawable="@drawable/box_checked">

	<item android:drawable="@drawable/box_default">

</selector>

content, padding, margins
FrameLayout, LinearLayout, RelativeLayout, GridLayout, ScrollView, ListView, ViewPager

styles.xml

<resources>

	<!-- Base application theme. -->
	<style name="AppTheme" parent="Theme.AppCompat.Light.DarkActionBar">
		<!-- Customize theme here -->
	</style>

	<style name="MyStyle">
		<item name="android:textColor">#FF255F26</item>
	</style>

	<style name="AnotherStyle">
		<item name="android:textColor">#1D175F</item>
		<item name="android:textStyle">bold</item>
	</style>
</resources>

Time Series Forecasting

time series forecasting to predict values for following business situations.
– Monthly beach bike rentals
– A stock’s daily closing value
– Annual sheep population

Average Method
The best predictor of what will happen tomorow is the average of everything that has happened up until now.

Moving average method

Naive Method
If there is not enough data to create a predictive model, the Naive method can supplement forecasts for the near future.

Seasonal Naive Method
Assumes that the magnitude of the seasonal pattern will remain constant.

Exponential Smoothing Model
Past Observations, Weighted Average

Bayesian Interface

Representing and reasoning with probabilities
Bayesian Networks

Joint Distribution

Strom lightning thonder
T, T .25 .20,.05
T, F .40 .04,0.36
F, T .05 .04,0.01
F, F .30 .03,0.27
Random day 2pm – look outside summer
Pr(￢storm) = 0.35
pr(lightning|storm) = .4615 (.25/.65)

X is conditionally independent of Y given Z fi the probability distribution governing X is independent of the value of y given the value of Z; that is, if
P(X=x|Y=y,Z=z)= P(X=x|Z=x)
more compactly write
P(X|Y,Z) = P(X|Z)

sampling
two things distribution are for -probability of value, generate value
simulation of a complex process
approximate inference

P(x)= Σy*P(x,y)
P(x,y)= P(x)*P(y|x)
P(y|x) = P(x|y)*P(y)/P(z)

Bayesian Learning

Learn the best hypothesis given data
$ some domain knowledge

Learn the most probable H given data
$ domain knowledge

Pr(h|D)
Pr(h|D) = Pr(D|h)*Pr(h) / Pr(D) … Bayes’ rule
Pr(a,b) = Pr(a|b)P(b)
Pr(b,a) = Pr(b|a)P(a)

Bayesian Learning
For each h e H
calculate Pr(h|D) = P(D|h)P(h)/P(D)
Output:
h = argmax Pr(h|D)
h = argmax Pr(D|h)