| import dash | |
| import dash_core_components as dcc | |
| import dash_html_components as html | |
| import plotly.graph_objs as go | |
| import pandas as pd | |
| app = dash.Dash() | |
| df = pd.read_csv( | |
| 'https://gist.githubusercontent.com/chriddyp/' |
Free Basics in India - some links. Note these are not meant to be an exhaustive survey - just what I've come across in late Dec 2015 after Mark Zuckerberg wrote an op-ed piece for Times Of India
Times of India on Net Neutrality
Alex Howard's article on alternate methods of subsidized Internet access in the US
Indian regulators ask Facebook's Indian telco partner - Reliance - to put Free Basics on hold
| __author__ = 'nitin' | |
| # -*- coding: utf-8 -*- | |
| """ | |
| =========== | |
| pgmagic | |
| =========== | |
| Magics for interacting with Postgres database server via psycopg2. |
| # pgdb.py | |
| __author__ = 'nitin' | |
| import psycopg2 as pspg2 | |
| import sys | |
| # import pandas as pd, then use pandas.io.sql | |
| db_data = { | |
| 'host':'localhost', | |
| 'dbname': 'nitin', |
| # compute error measures for predictive model for activity (act) as dependent variable | |
| # note this is computed for all 6 activities and a total computed which is an aggregate value | |
| errormeasures <- function(orig, pred, act) { | |
| N <- length(orig) | |
| origtrue <-as.vector(NULL) | |
| origfalse <-as.vector(NULL) | |
| predtrue <-as.vector(NULL) | |
| predfalse <-as.vector(NULL) | |
| === | |
| > rs <- dbSendQuery(con, statement = paste( | |
| + "SELECT * FROM", +"MADlib.c45_train('infogain','public.golf_data','trained_tree_infogain',null,'temperature,humidity','outlook,temperature,humidity,windy','id','class',100,'explicit', 5,0.001,0.001,0)")); | |
| > df <- fetch(rs, n = -1) | |
| > df | |
| training_set_size tree_nodes tree_depth training_time split_criterion | |
| 14 8 3 00:00:00.311362 infogain |
| Here's an R session mimicking the steps in the doc for decision trees. At this point not meant to show anything like the final result just a probe of the R<->Pg semantics. | |
| Note that the driver seems to create a result set in a format that the data frame constructor in R understands. | |
| Not only that but the equivalent of "to_string" for df seems to output it in the familiar tabular format by default. | |
| So the happy result to report here is that the native impedance mismatch is small. | |
| We'll need some validation tests to identify R<-->GP issues if any. | |
| Right now I am using an R<-->Postgres driver (MADlib 0.4 on Pg 9.1 on MacOSX Lion., RStudio 0.9.4, R 2.13.2). |
| Session 1 | |
| --------- | |
| * Intro | |
| * Motivation | |
| * Installation | |
| * Overview of Material |
| #!/bin/bash | |
| # | |
| # This script installs and configures couchdb on a fresh Amazon Linux AMI instance. | |
| # | |
| # Must be run with root privileges | |
| # Tested with Amazon Linux AMI release 2011.02.1.1 (ami-8c1fece5) | |
| # | |
| export BUILD_DIR="$PWD" |
| curl -X PUT http://your:credentials@localhost:5984/profiles | |
| curl -X POST -d '{"source":"http://couchcamp.couchone.com/profiles","target":"http://localhost:5984/profiles","continuous":true}' -H "Content-type: application/json" http://localhost:5984/_replicate | |
| curl -X POST -d '{"target":"http://couchcamp.couchone.com/profiles","source":"http://localhost:5984/profiles","continuous":true}' -H "Content-type: application/json" http://localhost:5984/_replicate |