View cbers.py
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__author__ = 'rischan - <--rischan@kartoza.com-->' | |
__date__ = '3/4/16' | |
import os | |
import sys | |
import glob | |
from cmath import log | |
from datetime import datetime | |
from xml.dom.minidom import parse | |
import traceback |
View tweet_dumper.py
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#!/usr/bin/env python | |
# encoding: utf-8 | |
import tweepy #https://github.com/tweepy/tweepy | |
import csv | |
#Twitter API credentials | |
consumer_key = "" | |
consumer_secret = "" | |
access_key = "" |
View delete_all_tweets.py
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# -*- coding: utf-8 -*- | |
""" | |
This script is forked originally from Dave Jeffery. The original implementation | |
was very slow and deleted around 2 tweets per second. Making it multithreaded I | |
am able to delete 30-50 tweets per second. | |
@author: vik-y | |
---------------------------------------------------------------------------- | |
This script will delete all of the tweets in the specified account. |
View av_layer_better.py
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__author__ = 'rischan' | |
from django.core.exceptions import ObjectDoesNotExist | |
from django.core.management.base import BaseCommand | |
from agri_mapper.utilities.script import CallWMS, CallWFS, UpdateModel | |
import logging | |
logger = logging.getLogger(__name__) |
View preprocessing2.R
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### Author Information ##### | |
### Coded by Rischan Mafrur ### | |
### 01/01/2015 ### | |
### For Thesis Purpose ### | |
### Language : R ### | |
### Dataset : Private Data ### | |
#Function for loading data | |
f_load_data <- function(path){ | |
library(shiny) |
View preprocessing_data.R
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### Author Information ##### | |
### Coded by Rischan Mafrur ### | |
### 01/01/2015 ### | |
### For Thesis Purpose ### | |
### Language : R ### | |
### Dataset : Private Data ### | |
#Function for loading data | |
f_load_data <- function(path){ | |
library(shiny) |
View parallel.R
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#to make the final table, I changed manually the n size every trial by: 1e3, 1e4, 1e5, 1e6, 1e7. Then I also chnaged the benchmark object 'res' | |
n <- 1e7 | |
set.seed(51) | |
process <- data.frame(id=sample(100, n, rep=T), x=rnorm(n), y=runif(n), z=rpois(n, 1) pexp(2, rate=1/3) ) | |
all <- multicore:::detectCores(all.tests=TRUE) | |
if(!require(rbenchmark)){ | |
install.packages("rbenchmark") | |
} else{ |
View RandomForest.R
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install.packages("randomForest",dependencies=TRUE) | |
library(randomForest) | |
fit <- randomForest(data$user ~ data$academic+data$email | |
+data$socnet+data$porn+data$comic+data$music | |
+data$shop+data$blog+data$video+data$search | |
+data$news, data=data) | |
print(fit) | |
importance(fit) |
View subfigure_span.tex
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\begin{figure*}% | |
\centering | |
\begin{subfigure}{.95\columnwidth} | |
\includegraphics[width=\columnwidth]{tolakpartaipoligami_pie3D.png}% | |
\caption{\#TolakPartaiPoligami hashtag} | |
\label{fig:sub1} | |
\end{subfigure}\hfill% | |
\begin{subfigure}{.95\columnwidth} | |
\includegraphics[width=\columnwidth]{sayapilihpks_pie3D.png}% | |
\caption{\#SayaPilihPKS hashtag} |
View NaiveBayes_MyProject.R
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#author : Rischan Mafrur | |
#email : rischanlab@gmail.com | |
#website : rischanlab.github.io, ourmasjid.me | |
#May 15, 2014 | |
#install requirement packages | |
install.packages("klaR") | |
install.packages("caret") | |
install.packages("plotrix") |
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