Shilpa AB shilpavijay

## Angular
Start server: cd my-app; ng serve

Start New component:
ng generate component <name>

## funcpool.py
from multiprocessing import Pool
import threading
import timeit

def func(x): return x**1000

#multiprocessing
start = timeit.timeit()
p = Pool(4)
print(p.map(func,[2,4,6,8]))

## tasks.py
from celery import Celery, shared_task
import time


#Redis Result backend config:
app = Celery('tasks', broker='amqp://localhost//', backend='redis://localhost:6379/0')
#Mysql Result backend config:
# app = Celery('tasks', broker='amqp://localhost//', backend='db+mysql://root:python098@localhost/test')


## docker quick access
Starting nginx
docker run -d -p 80:80 --name webserver nginx

docker images

docker ps -a

remove images:
---------------
docker rmi <>

## No_of_Road_Acc.csv

          
            States
            2003
            2004
            2005
            2006
            2007
            2008
            2009
            2010
            2011
            2012
            2013
            2014
            2015
            2016

            
              Andhra Pradesh
              9800
              10981
              10026
              10953
              9979
              10634
              9887
              10681
              11211
              8353
              9621
              9267
              9134
              9729

            
              Arunachal Pradesh
              144
              84
              111
              82
              88
              115
              114
              128
              0
              0
              0
              0
              0
              75

            
              Assam
              784
              746
              947
              946
              1010
              849
              1034
              1345
              1552
              1553
              1817
              1777
              1898
              1999

            
              Bihar
              1493
              1195
              1076
              1702
              1971
              1499
              2719
              2837
              3177
              2833
              2837
              2535
              3255
              2434

            
              Chhattisgarh
              2593
              2837
              3543
              3356
              3265
              3814
              3564
              3363
              3156
              3654
              3804
              3758
              3898
              3164

            
              Goa
              356
              323
              400
              421
              536
              610
              787
              925
              675
              654
              422
              434
              451
              356

            
              Gujarat
              10242
              10229
              10133
              9071
              9630
              10167
              9210
              9177
              9252
              8188
              7731
              7437
              6739
              6309

            
              Haryana
              2529
              2931
              2917
              3202
              3752
              3611
              3693
              3436
              3425
              3108
              3253
              3247
              3389
              2887

            
              Himachal Pradesh
              699
              816
              787
              792
              845
              597
              806
              703
              742
              533
              643
              698
              634
              820

## New Project: super-quiz
Index.html
----------
<!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="utf-8">
    <title>Vue Animations</title>
	    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" integrity="sha384-BVYiiSIFeK1dGmJRAkycuHAHRg32OmUcww7on3RYdg4Va+PmSTsz/K68vbdEjh4u" crossorigin="anonymous">
	  <link rel="stylesheet"
	  href="https://cdnjs.cloudflare.com/ajax/libs/animate.css/3.5.2/animate.min.css">

## frozen flask for MDW
to install:
Frozen-Flask

to run:
python <pyfilename> build

import sys
from flask import Flask
from flask import render_template
import pandas as pd

## ML quick finds
Sigmoid function:
sigmoid(x) = 1/(1+e^-x)
A sigmoid function is a mathematical function having a characteristic "S"-shaped curve or sigmoid curve.

Softmax:
The softmax function is often used in the final layer of a neural network-based classifier. Such networks are commonly trained
under a log loss (or cross-entropy) regime, giving a non-linear variant of multinomial logistic regression.

W -> Vector representing weight (that represented on the line btw input(x) and first activation layer(z))
b -> bias (each activation layer(z) has a bias term)

## Pandas, Numpy - quick access

#If this is set to True, the axes which are reduced are left in the result as dimensions with size one
<np array>.sum(axis=1, keepdims=True)
np.vstack - stack array row-wise

#Convert to NP Array:
df = pd.read_csv(csvfile)
nparr = np.array(df)

#finding max of a dataframe

## Stacked Bar
var h = 500;
var w = 800;

var svg = 	d3.select("body").append("svg")
			.attr("id","chart")
			.attr("width",w)
			.attr("height",h);

var num = 51;
var types = 4;
	Start server: cd my-app; ng serve

	Start New component:
	ng generate component <name>
	from multiprocessing import Pool
	import threading
	import timeit

	def func(x): return x**1000

	#multiprocessing
	start = timeit.timeit()
	p = Pool(4)
	print(p.map(func,[2,4,6,8]))
	from celery import Celery, shared_task
	import time


	#Redis Result backend config:
	app = Celery('tasks', broker='amqp://localhost//', backend='redis://localhost:6379/0')
	#Mysql Result backend config:
	# app = Celery('tasks', broker='amqp://localhost//', backend='db+mysql://root:python098@localhost/test')
	Starting nginx
	docker run -d -p 80:80 --name webserver nginx

	docker images

	docker ps -a

	remove images:
	---------------
	docker rmi <>
States	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016
Andhra Pradesh	9800	10981	10026	10953	9979	10634	9887	10681	11211	8353	9621	9267	9134	9729
Arunachal Pradesh	144	84	111	82	88	115	114	128	0	0	0	0	0	75
Assam	784	746	947	946	1010	849	1034	1345	1552	1553	1817	1777	1898	1999
Bihar	1493	1195	1076	1702	1971	1499	2719	2837	3177	2833	2837	2535	3255	2434
Chhattisgarh	2593	2837	3543	3356	3265	3814	3564	3363	3156	3654	3804	3758	3898	3164
Goa	356	323	400	421	536	610	787	925	675	654	422	434	451	356
Gujarat	10242	10229	10133	9071	9630	10167	9210	9177	9252	8188	7731	7437	6739	6309
Haryana	2529	2931	2917	3202	3752	3611	3693	3436	3425	3108	3253	3247	3389	2887
Himachal Pradesh	699	816	787	792	845	597	806	703	742	533	643	698	634	820
	Index.html
	----------
	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="utf-8">
	<title>Vue Animations</title>
	<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" integrity="sha384-BVYiiSIFeK1dGmJRAkycuHAHRg32OmUcww7on3RYdg4Va+PmSTsz/K68vbdEjh4u" crossorigin="anonymous">
	<link rel="stylesheet"
	href="https://cdnjs.cloudflare.com/ajax/libs/animate.css/3.5.2/animate.min.css">
	to install:
	Frozen-Flask

	to run:
	python <pyfilename> build

	import sys
	from flask import Flask
	from flask import render_template
	import pandas as pd
	Sigmoid function:
	sigmoid(x) = 1/(1+e^-x)
	A sigmoid function is a mathematical function having a characteristic "S"-shaped curve or sigmoid curve.

	Softmax:
	The softmax function is often used in the final layer of a neural network-based classifier. Such networks are commonly trained
	under a log loss (or cross-entropy) regime, giving a non-linear variant of multinomial logistic regression.

	W -> Vector representing weight (that represented on the line btw input(x) and first activation layer(z))
	b -> bias (each activation layer(z) has a bias term)

	#If this is set to True, the axes which are reduced are left in the result as dimensions with size one
	<np array>.sum(axis=1, keepdims=True)
	np.vstack - stack array row-wise

	#Convert to NP Array:
	df = pd.read_csv(csvfile)
	nparr = np.array(df)

	#finding max of a dataframe
	var h = 500;
	var w = 800;

	var svg = d3.select("body").append("svg")
	.attr("id","chart")
	.attr("width",w)
	.attr("height",h);

	var num = 51;
	var types = 4;