pattoM

#Load library and data
library("Matching")
data("lalonde")

#Separate data into subsets based on the degree status
nodegree <- subset(lalonde, nodegr == 1)
degree <- subset(lalonde, nodegr == 0)

#Make scatterplot of the data
plot(re78 ~ treat+educ+age+re74+re75,data=nodegree, xlab="Variables", ylab="Real earnings 1978", main="Linear regression for without degree")

pattoM

#QUESTION 1

#read data
control <- read.table("nswre74_control.txt", header=FALSE)
treated <- read.table("nswre74_treated.txt", header=FALSE)

#simple difference in means for 1978 earnings
diff_means <- mean(treated$V10) - mean(control$V10)
#output diff in means
diff_means

pattoM

# [INSERT CODE HERE] Initial state
state = 1 

# [INSERT CODE HERE] Define the goal here
goal = 1024

# Frontier
from collections import deque
frontier = deque([state])

pattoM

#using dictionaries 
class TrieDict:
    def __init__(self):
        self.hub = {"*":{}} 
    
    
    def add_new(self,word):
        #set head to hub 
        cur_node = self.hub["*"] 
        #loop through word