ayota

def sandwich (Slices, HasPeanutButter, HasJelly):
  Bread = Slices/2
	HasPeanutButter = HasPeanutButter
	HasJelly = HasJelly
	
	if Bread >= 1 & HasPeanutButter == True: #check for enough bread
		if Bread >= 2: #assign plural or not plural
			Plural = "es"
		else:
			Plural = ""

ayota

contacts = {'Shannon': {'phone': '202-555-1234', 'twitter': '@svt827', 'github': '@shannonturner' },
'Anupama': {'phone': '410-333-9876','twitter': '@iamtheanupama', 'github':''}}


# for contact in sorted(contacts.keys()):
# 	print "{0} : {1}".format(contact,contacts[contact]['phone'])
# 	
# for contact,info in sorted(contacts.items()):
# 	print "{0} : {1}".format(contact,info)

ayota

def deduplicate(file1, file2):
	with open(file1,"r") as file_1:
		event_1 = file_1.read().split("\n")
	
	with open(file2,"r") as file_2:
		event_2 = file_2.read().split("\n")

	master_list = list(set(event_1 + event_2))
	
	doubles = []

ayota

codelist = ""

#this one splits the states.csv file 
with open("states.csv","r") as states_file:
	states = states_file.read().split("\n")
	
	for index, state in enumerate(states):
		states[index] = state.split(",")

#this formats the states in a pretty table		

ayota

norm <- function(x) {
  norm <- sqrt(sum(x^2))
  return(norm)
}

x.1 <- c(-2,1,0)
x.2 <- c(1,-1,1)

y.1 <- c(0,1,0)
y.2 <- c(1,1,2)

ayota

#using distance formula
planesDist <- function(x1, x2, x3, y1, y2, y3) {   

        dist <- function(s1, s2, s3, s4) {
        d <- sqrt(
        t(x1 + s1*x2 + s2*x3 - (y1 + s3*y2 + s4*y3)) %*% 
        (x1 + s1*x2 + s2*x3 - (y1 + s3*y2 + s4*y3))) 
        return(d)}  

   start <- cbind(seq(-100,100,.01),seq(-100,100,.01),seq(-100,100,.01),seq(-100,100,.01)) 

ayota

x.dat <- model.matrix(Y ~ ., data=data)[,-1]

set.seed(1)
train <- sample(1:nrow(data), nrow(data)/2)
y.train <- data$Y[train]
x.train <- x.dat[train,]
test <- (-train)
y.test <- data$Y[test]
x.test <- x.dat[test,]

ayota

data(prostate)
prostate <- prostate[,1:9]

#scale all the predictors
data.st <- scale(prostate[,-9])
data.st <- sapply(1:8, function(x) data.st[,x]/fxn.4$norm(data.st[,x]))
data.st <- cbind(data.st, prostate[,9])

#roughly unit vectors
sapply(1:9, function(x) fxn.4$norm(data.st[,x]))

ayota

data <- read.table("~/Dropbox/numericalmethods/nonlinear.txt", header=TRUE, quote="\"")

#param order: d, r
fxn.4 <- list(
  f.x = function(params, data) return( sum((data$y - params[1]*exp(-params[2] * data$x) )^2) ),
  grad = function(params, data, f.x, h) { 
    d.d <- (f.x(params = c(params[1] + h, params[2]), data) - f.x(params, data)) / h
    d.r <- (f.x(params = c(params[1], params[2] + h), data) - f.x(params, data)) / h
    return(c(d.d,d.r))
    },

ayota

Elaine Ayo

Math 504 / Homework 10

March 29, 2015

```{r echo=FALSE, message=FALSE}
library(MASS)
library(Matrix)
library(ggplot2)