FirebaseUI Authentication

Adding Sign In
-Email/Password
-Google
-Facebook
-Twitter
-GitHub

mPhotoPickerButton.setOnClickListener(new View.OnClickListener(){
	@Override
	public void onClick(View view){
		Intent intent = new Intent(Intent.ACTION_GET_CONTENT);
		intent.setType("image/jpeg");
		intent.putExtra(Intent.EXTRA_LOCAL_ONLY, true);
		startActivityForResult(Intent.createChooser(intent, "Complete action using"), RC_PHOTO_PICKER);
	}
});
service firebase.storage {
	match /b/friendlychat-12987.appspot.com/o {
		match /{allPaths=**} {
			allow read, write: if request.auth != null;
		}
	}
}

match /funGifs/cats/{catPics=**}

resource: existing storage data
request: incoming request data

allow read: if request.auth != null && imageId.matches(“.*.gif”)

String name = "Firebase":
name = 3;

var name = "Firebase";
name = 3;

final x = 42;
x = 1024;

const x = 42;
x = 1024

var x = 42;
var y = 1024;

System.out.println("Message");
Log.d(TAG, "Message");

public int multiply(int a, int b){
	return a * b;
}

String[] list = {"apple", "pear", "orange"};

for (int i = 0; i < list.length; i++){
	System.out.println(list[i])
}

Databse implementation

mSendButton.setOnClickListener(new View.OnClickListener(){
	@Override
	public void onClick(View view){
		mMessageEditText.setText("");
	}
});

{
	"rules": {
		".read": true,
		".write": ture
	}
}

Database Security
-Database security rules
-Security rule configuration examples
-Authentication with Firebase

{
	"chat": {
		"messages": {
			"-KS3PV-iwUZp5wkNq70s":{
				"name": "person1",
				"text": "hey!"
			},
			"-KS3PXhIhs8J_inrExy4":{
				"name": "person2",
				"text": "what's up?"
			}
		}
	},
	"special_chat": {
		"messages": {
			"-KR-DwqtKzlWGxSn9P0y":{
				"name": "person1",
				"text":"want to go to the movies?"
			},
			"-KR4tIpWmNn-EYxquSrw": {
				"name": "person3",
				"text": "Yeah! let's meet at 7."
			}
		}
	},
	"users": {
		"uid1":{
			"paid":true
		},
		"uid2": {
			"paid": false
		},
		"uid3": {
			"paid": true
		}
	}

}

Firebase Realtime Database

what is firebase databse?
what can it be used for?
realtime database in action
database structure best practices

““:
“golden”: 1.618
“fog”: true
“palindrome”: “tacocat”
“messages”: “message1”: “name”:”person”, “text”:”Hello?”
“message2”: “name”:”Individual”, “text”:”Hi!”

node should be “child” and “parent

{
	"questions": {
		"ABCDakarandomkey": {
			"question": "Who was the 13th president of the United States?"
			"choice_1": "Millard Fillmore",
			"choice_2": "Zachary Taylor",
			"choice_3": "Franklin Pierce",
			"choice_4": "James K. Polk",
			"answer" :"choice_1"
		},
		"EFGHakarandomkey":{
			"question": "In what year was the first gasoline combustion engine invented?",
			"choice_1":"1769",
			"choice_2":"1886",
			"choice_3":"1807",
			"choice_4":"1864",
			"answer": "choice_4"
		}
	},
	"players":{
		"user_key_1":{
			"name":"Person",
			"opponents":{
				"IJKLakarandomkey":"user_key_2",
				"MNOPakarandomkey":"user_key_6"
			},
			"questions":{
				"ABCDakarandomkey": "Correct",
				"EFGHakarandomkey": "Incorrect"
			}
		},
		"user_key_2": {
			"name": "Mai",
			"opponents": {
				"QRAAakarandomkey": "user_key_1",
				"SQUEakarandomkey": "user_key_6"
			},
			"questions": {
				"ABCDakarandomkey":"Incorrect",
				"EFGHakarandomkey":"Incorrect"
			}
		}
	},
	"opponents": {
		"couple_Key_1": "user_key_1_user_key_2",
		"user_1": "user_key_1",
		"user_2": "user_key_2",
		"winner": "user_key_1"
	}
}

Firebase

Be comfortable with
– making a simple mobile application
– basic command line
– calling a web or cloud API

FriendlyChat
APP, DB, FILE

package com.google.firebase.xxxx.friendlychat;
public class FriendlyMessage {
	private String text;
	private String name;
	private String photoUrl;

	public FriendlyMessage(){
	}

	public FriendlyMessage(String text, String name, String photoUrl){
		this.text = text;
		this.name = name;
		this.photoUrl = photoUrl;
	}

	public String getText(){
		return text;
	}

	public void setText(string text){
		this.text = text;
	}

	public String getName(){
		return name;
	}

	public void setName(String name){
		this.name = name;
	}
	public String getPhotoUrl{
		return photoUrl;
	}

	public void setPhotoUrl(String photoUrl){
		this.photoUrl = photoUrl;
	}
}

package com.google.firebase.xxxx.friendlychat;

import android.os.Bundle;
import android.support.v7.app.AppCompactActivity;
import android.text.Editable;
import android.text.TextWatcher;
import android.view.Menu;
import android.view.MenuInflater;
import android.view.MenuItem;
import android.view.View;
import android.widget.Button;
import android.widget.EditText;
import android.widget.ImageButton;
import android.widget.ListView;
import android.widget.ProgressBar;

import java.util.ArrayList;
import java.util.List;

public class MainActivity extends(Bundle savedInstanceState){
	super.onCreate(savedInstanceState);
	setContentView(R.layout.activity_main);

	mUsername = ANONYMOUS;

	mProgressBar = (ProgressBar) findViewById(R.id.progressBar);
	mMessageListView = (ListView) findViewById(R.id.messageListView);
	mPhotoPickerButton = (ImageButton) findViewById(R.id.photoPickerButton);
	mMessageEditText = (EditText) findViewById(R.id.messageEditText);
	mSendButton = (Button) findViewById(R.id.sendButton);

	List<FriendlyMessage> friendlyMessages = new ArrayList<>();
	mMessageAdapter = new MessageAdapter(this, R.layout.item_message, friendlyMessages);
	mMessageListView.setAdapter(mMessageAdapter);

	mProgressBar.setVisibility(ProgressBar.INVISIBLE);

	mPhotoPickerButton.setOnClickListener(new View.OnClickListener(){
		@Override
		public void onClick(View view){

		}
	});

	mMessageEditText.addTextChangedListener(new TextWatcher(){
		@Override
		public void beforeTextChanged(CharSequence charSequence, int i, int i1, int i2){			
		}

		@Override
		public void onTestChanged(CharSequence charSequence, int i, int i1, int i2){
			if (charSequence.toString().trim().length() > 0){
				mSendButton.setEnable(true);
			} else {
				mSendButton.setEnabled(false);
			}
		}

		@Override
		public void afterTextChanged(Editable editable){
		}
	});
	mMessageEditText.setFilter(new InputFilter[]{new InputFilter.LengthFilter(DEFALT_MSG_LENGTH_LIMIT)});

	mSendButton.setOnClickListener(new View.OnClickListener(){
		@Override
		public void onClick(View view){
			mMessageEditText.setText("");
		}
	});

	@Override
	public boolean onCreateOptionsMenu(Menu menu){
		MenuInflater inflater = getMenuInflater();
		inflater.inflate(R.menu.main_menu, menu);
		return true;
	}

	@Override
	public boolean onOptionsItemSelected(MenuItem item){
		return super.onOptionsItemSelected(item);
	}
}

Evaluation Metrics

accuracy = no of items in a class labeled correctly / all items in that class

positive – negative
percision = true positive / true positive + false positive
recall = true positive / true positive + negative positive

predictions = [0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1]
true labels = [0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0]

data set, features, algorithms, evaluation

GridSearchCV

parameters = {'kerne':('linear','rbf'),'c':[1,10]}
svr = svm.SVC()
clf = grid_search.GridSearchCV(svr, parameters)
clf.fit(iris.data, iris.target)

parameters = {'kernel':('linear','rbf'),'C':[1,10]}

svr = svm.SVC()
clf = grid_search.GridSearchCV(svr,parameters)
clf.fit(iris.data, iris.target)
clf.best_params_

Validation

import numpy as np
from sklearn import cross_validation
from sklearn imort datasets
from sklearn import svm

iris = datasets.load_iris()
iris.data.shape, iris.target.shape((150, 4), (150,))

X_train, X_test, y_train, y_test = cross_validation.train_test_split(
	iris.data, iris.target, test_size=0.4, random_state=0)

X_train.shape, y_train.shape((90, 4), (90,))
X_test.shape, y_test.shape((60, 4), (60,))

clf = svm.SVC(kernel='linear',C=1).fit(X_train, y_train)
clf.score(X_test, y_test)0.96

Training, Transforms, Predicting
Train/test split -> pca -> svm

clf = GaussianNB()
t0 = time()
kf = KFold(len(authors), 2)
for train_indices, test_indicies in kf:
	features_train = [word_data[ii] for ii in train_indices]
	features_test = [word_data[ii] for ii in test_indices]
	authors_train = [authors[ii] for ii in train_indices]
	authors_test = [authors[ii] for ii in test_indices]

	vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5,
			stop_words='english')
	features_train_transformed = vectorizer.fit_transform(features_train)
	features_test_transformed = vectorizer.transform(features_test)
	selector = SelectPercentile(f_classif, percentile=10)
	selector.fit(features_train_transformed, authors_train)
	features_train_transformed = selector.transform(features_train_transformed).toarray()
	features_test_transformed = selector.transform(features_test_transformed).toarray()

	clf.fit(features_train_transformed, authors_train)
	print "training time:", round(time()-t0, 3), "s"
	t0 = time()
	pred = clf.predict( features_test_transformed )

When to use PCA

When to use PCA
-> latent features driving the patterns in data
-> dimensional reduction
-> visualize high-dimensional data, reduce noise
-> make other algorithms(regression, classification) work better fewer inputs

PCA for facial recognition

X_train, X_test, y_train, y_test = train_split(X, y, test_size=0.25)

n_components = 150

print "Extracting the top %d eigenfaces from %d faces" % (n_components, X_train.shape[0])
t0 = time()
pca = RandomizePCA(n_components=n_components, whiten=True).fit(X_train)
print "done in %0.3fs" % (time() - t0)

eigenfaces = pca.components_.reshape((n_component, h, w))

print "Projecting the input data on the eigenfaces orthnormal basis"
t0 = time()
X_train_pca = pca.tranform(X_train)
X_test_pca = pca.transform(X_test)
print "done in %0.3fs" % (time() - t0)

print "Fitting the classifier to the training set"

http://scikit-learn.org/stable/auto_examples/applications/face_recognition.html

PCA

Principal Component Analysis – PCA
Dimensional of data:2

x = 2
y = 3
Δx = 1
Δy = 2

square footage + No.Rooms -> Size

How to determine the principal component
variance – the willingness/flexibility of an algorithm to learn
technical term in statistics – roughly the “spread” of a data distribution(similar to standard duration)

– maximum variance and information loss

def doPCA():
	from sklearn.decomposition import PCA
	pca = PCA(n_components=2)
	pca.fit(data)
	return pca

pca = doPCA()
print pca.explained_variance_ratio_
first_pc = pca.component_[0]
second_pc = pca.components_[1]

transformed_data = pca.transform(data)
for ii, jj in zip(transofrmed_data, data):
	plt.scatter( first_pc[0]*ii[0],  first_pc[1]*ii[0], color="r")
	plt.scatter( second_pc[0]*ii[1], second_pc[1]*ii[1], color="c")
	plt.scatter( jj[0], jj[i], color="b")

plt.xlabel("bonus")
plt.ylabel("long-term incentive")
plt.show()

Features != Information

There are two big univariate feature selection tools in sklearn: SelectPercentile and SelectKBest. The difference is pretty apparent by the names: SelectPercentile selects the X% of features that are most powerful (where X is a parameter) and SelectKBest selects the K features that are most powerful (where K is a parameter).

high bias
pays little attention to data over simplified, high error on training set
high variance
pays too much attention to data(does not generalize well) over fit

Regularization in Regression
method for automatically penalizing extra features
-Lasso Regression: minimize SSE + γ|β|

m1 – m4: coefficients of regression
x1-x4: features

import sklearn, linear_model Lasso
features, labes = GetMyData()
reguression = Lasso()
regression fit(features)
regression predict([2, 4])