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@anirudhjayaraman
anirudhjayaraman / lm_linear_algebra.R
Created Nov 12, 2019
Linear regression implementation using linear algebra in R
View lm_linear_algebra.R
### Linear Regression Using lm() ----------------------------------------
data("swiss")
dat <- swiss
linear_model <- lm(Fertility ~ ., data = dat)
summary(linear_model)
# Call:
# lm(formula = Fertility ~ ., data = dat)
#
View remove_missing_levels.R
remove_missing_levels <- function(fit, test_data) {
library(magrittr)
# https://stackoverflow.com/a/39495480/4185785
# drop empty factor levels in test data
test_data %>%
droplevels() %>%
as.data.frame() -> test_data
View factor_new_levels.R
library(data.table)
train <- fread('train.csv'); test <- fread('test.csv')
# consolidate the 2 data sets after creating a variable indicating train / test
train$flag <- 0; test$flag <- 1
dat <- rbind(train,test)
# change outcome, var_b and var_e into factor var
dat$outcome <- factor(dat$outcome)
View AirPassengers.Rmd
---
title: "ARIMA Modeling in R"
output: html_document
---
Let's start off by loading relevant R libraries!
```{r include = FALSE}
library(tseries)
library(zoo)
library(forecast)
View rice_strucchange.R
library(xlsx)
library(forecast)
library(tseries)
library(strucchange)
## load the data from a CSV or Excel file. This example is done with an Excel sheet.
prod_df <- read.xlsx(file = 'agricultural_productivity.xls', sheetIndex = 'Sheet1', rowIndex = 8:65, colIndex = 2, header = FALSE)
colnames(prod_df) <- c('Rice')
## store rice data as time series objects
rice <- ts(prod_df$Rice, start=c(1951, 1), end=c(2008, 1), frequency=1)
View strucchange_usage.R
# assuming you have a 'ts' object in R
# 1. install package 'strucchange'
# 2. Then write down this code:
library(strucchange)
# store the breakdates
bp_ts <- breakpoints(ts ~ 1)
@anirudhjayaraman
anirudhjayaraman / experimentsWithData.ipynb
Last active Sep 1, 2016
Experiments With Data (Hackathon)
View experimentsWithData.ipynb
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@anirudhjayaraman
anirudhjayaraman / graphUndirected_output.txt
Created Jul 28, 2016
Output file to graphUndirected.py
View graphUndirected_output.txt
{}
{}
["A:['B', 'C', 'E']", "C:['A', 'B', 'D', 'E']", "B:['A', 'C', 'D']", "E:['A', 'C']", "D:['B', 'C']"]
[[ 0. 1. 1. 0. 1.]
[ 1. 0. 1. 1. 0.]
[ 1. 1. 0. 1. 1.]
@anirudhjayaraman
anirudhjayaraman / graphUndirected.py
Last active Oct 26, 2019
Implementing Undirected Graphs in Python
View graphUndirected.py
class Vertex:
def __init__(self, vertex):
self.name = vertex
self.neighbors = []
def add_neighbor(self, neighbor):
if isinstance(neighbor, Vertex):
if neighbor.name not in self.neighbors:
self.neighbors.append(neighbor.name)
neighbor.neighbors.append(self.name)
@anirudhjayaraman
anirudhjayaraman / graphUndirected.ipynb
Created Jul 28, 2016
Python Implementation of Undirected Graphs (Adjacency List and Adjacency Matrix)
View graphUndirected.ipynb
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