Jamie McDowell jamie23

## gist:d84f1c11aa307940d7afbbb903a68c34
https://api.datamarket.azure.com/Bing/Search/v1/Image?Query=%27playstation%27&ImageFilters=%27Size%3AWidth%3A300%2BSize%3AHeight%3A300%27&$format=json

## Hangman.py
Making a hangman game, woohoo

#Get input and convert to list
word = raw_input("Please input your word: ");
word = list(word);

guesses = 5;

#Setting up our hashes list
guessingWord = len(word)*["*"];

## gist:bbf56059883e195e76c4
# Pi2Go Motor Test
# Moves: Forward, Reverse, turn Right, turn Left, Stop - then repeat
# Press Ctrl-C to stop
#
# Also demonstrates writing to the LEDs
#
# To check wiring is correct ensure the order of movement as above is correct
# Run using: sudo python motorTest.py

import pi2go, time

## gist:a149d5fef3dfa15cc524
https://www.youtube.com/watch?v=byeqpfBUyaU

## gist:a3495349ff5103a9b59b
$n
[1] 573

$cluster.number
[1] 3

$cluster.size
[1] 112 310 151

$min.cluster.size

## Hierarchical clustering
d = dist(cl, method = "euclidean")

fit = hclust(d, method = "ward")
plot(fit, labels=rownames(d), xaxt = "n")
groups = cutree(fit, k=3)
x = rect.hclust(fit, k=3, border="red", cluster = groups)

## 4.1.1 Initial updates to include all data source
Removed comments for dissertation.

dataSmall <- read.delim("//Users//jamie//Programming//R//Dissertation//SmallData.out", row.names = 1)

dataBig <- read.delim("//Users//jamie//Programming//R//Dissertation//LargeData.tab", row.names = 1)

dataComplete <- merge(dataSmall,dataBig,by="row.names")

dataComplete[1]<-NULL

## Classification tree
check = as.factor(groups)

#fit tree model with training data
tree_model = tree(check~., training_data)
plot(tree_model)
text(tree_model, pretty=0)

#model precision check
tree_pred = predict(tree_model, testing_data, type="class")

## gist:11197652
acm
siteseerx
research gate

## gist:9422825
import java.util.Scanner;

public class Solution {
	  public static void main(String[] args){
		  	Solution x = new Solution();
		  	int[] A = {-7,1,5,2,-4,3,0};

		  	System.out.println(x.solution(A));
	  }
	Making a hangman game, woohoo

	#Get input and convert to list
	word = raw_input("Please input your word: ");
	word = list(word);

	guesses = 5;

	#Setting up our hashes list
	guessingWord = len(word)[""];
	# Pi2Go Motor Test
	# Moves: Forward, Reverse, turn Right, turn Left, Stop - then repeat
	# Press Ctrl-C to stop
	#
	# Also demonstrates writing to the LEDs
	#
	# To check wiring is correct ensure the order of movement as above is correct
	# Run using: sudo python motorTest.py

	import pi2go, time
	$n
	[1] 573

	$cluster.number
	[1] 3

	$cluster.size
	[1] 112 310 151

	$min.cluster.size
	d = dist(cl, method = "euclidean")

	fit = hclust(d, method = "ward")
	plot(fit, labels=rownames(d), xaxt = "n")
	groups = cutree(fit, k=3)
	x = rect.hclust(fit, k=3, border="red", cluster = groups)
	Removed comments for dissertation.

	dataSmall <- read.delim("//Users//jamie//Programming//R//Dissertation//SmallData.out", row.names = 1)

	dataBig <- read.delim("//Users//jamie//Programming//R//Dissertation//LargeData.tab", row.names = 1)

	dataComplete <- merge(dataSmall,dataBig,by="row.names")

	dataComplete[1]<-NULL
	check = as.factor(groups)

	#fit tree model with training data
	tree_model = tree(check~., training_data)
	plot(tree_model)
	text(tree_model, pretty=0)

	#model precision check
	tree_pred = predict(tree_model, testing_data, type="class")
	import java.util.Scanner;

	public class Solution {
	public static void main(String[] args){
	Solution x = new Solution();
	int[] A = {-7,1,5,2,-4,3,0};

	System.out.println(x.solution(A));
	}