adelehedrick

all:
	java -cp ~/antlr-4.6-complete.jar:. org.antlr.v4.Tool -no-listener -no-visitor *.g4
	javac -cp ~/antlr-4.6-complete.jar:. *.java

run:
	java -cp ~/antlr-4.6-complete.jar:. ParseFile inputfile 
	

clean:

adelehedrick

import pandas as pd
import numpy as np

df = pd.read_csv('data/train.csv')
df = df.drop(['Name', 'Ticket', 'Cabin'], axis=1)

###########################################################
# 1. We need to fill the NaN values rather than drop them
# Improvments go here for training
###########################################################

adelehedrick

all:
	antlr4 -no-listener -no-visitor *.g4
	javac *.java

run:
	@java -cp C:\libs\antlr-4.5.2-complete.jar;. ExprParser < inputfile

clean:
	del *.java *.tokens *.class

adelehedrick

import java.util.regex.*;
import static java.lang.System.out;

class Demo1 {
  public static void main(String[] args) {
    String pattern_string = args[0];
    String content_string = args[1];

    Pattern pattern = Pattern.compile(pattern_string);
    Matcher matcher = pattern.matcher(content_string);

adelehedrick

import numpy as np
import networkx as nx
import matplotlib.pyplot as plt

# This wonderful bellman-ford algorithm is brought
# to you by https://gist.github.com/joninvski/701720

# Step 1: For each node prepare the destination and predecessor
def initialize(graph, source):
    d = {} # Stands for destination

adelehedrick

import matplotlib.pyplot as plt

with open('runtime.txt') as f:
    runtimes = [int(line.strip()) for line in f]

plt.hist(runtimes)
plt.savefig('runtime.png')
plt.show()

adelehedrick

prepare:
	javac World/Person.java
	javac World/Community.java

n ?= 10
run: 	
	n=$(n); \
	while [ $${n} -gt 0 ] ; do \
		echo $$n ; \
		n=`expr $$n - 1`; \

adelehedrick

import java.util.Arrays;

/*
 * 

Insertion sort algorithm
 
for j = 2 to A.length
	key = A[j]
	i = j-1

adelehedrick

import java.util.Arrays;

/*
 * 

Insertion sort algorithm
 
for j = 2 to A.length
	key = A[j]
	i = j-1