Develop Reference

Count how many list entries have a string property that ends with a particular char

I have an array list with some names inside it (first and last names). What I have to do is go through each "first name" and see how many times a character (which the user specifies) shows up at the end of every first name in the array list, and then print out the number of times that character showed up.
public int countFirstName(char c) {
int i = 0;
for (Name n : list) {
if (n.getFirstName().length() - 1 == c) {
i++;
}
}
return i;
}
That is the code I have. The problem is that the counter (i) doesn't add 1 even if there is a character that matches the end of the first name.

You're comparing the index of last character in the string to the required character, instead of the last character itself, which you can access with charAt:
String firstName = n.getFirstName()
if (firstName.charAt(firstName.length() - 1) == c) {
i++;
}

When you're setting out learning to code, there is a great value in using pencil and paper, or describing your algorithm ahead of time, in the language you think in. Most people that learn a foreign language start out by assembling a sentence in their native language, translating it to foreign, then speaking the foreign. Few, if any, learners of a foreign language are able to think in it natively
Coding is no different; all your life you've been speaking English and thinking in it. Now you're aiming to learn a different pattern of thinking, syntax, key words. This task will go a lot easier if you:
work out in high level natural language what you want to do first
write down the steps in clear and simple language, like a recipe
don't try to do too much at once
Had I been a tutor marking your program, id have been looking for something like this:
//method to count the number of list entries ending with a particular character
public int countFirstNamesEndingWith(char lookFor) {
//declare a variable to hold the count
int cnt = 0;
//iterate the list
for (Name n : list) {
//get the first name
String fn = n.getFirstName();
//get the last char of it
char lc = fn.charAt(fn.length() - 1);
//compare
if (lc == lookFor) {
cnt++;
}
}
return cnt;
}
Taking the bullet points in turn:
The comments serve as a high level description of what must be done. We write them aLL first, before even writing a single line of code. My course penalised uncommented code, and writing them first was a handy way of getting the requirement out of the way (they're a chore, right? Not always, but..) but also it is really easy to write a logic algorithm in high level language, then translate the steps into the language learning. I definitely think if you'd taken this approach you wouldn't have made the error you did, as it would have been clear that the code you wrote didn't implement the algorithm you'd have described earlier
Don't try to do too much in one line. Yes, I'm sure plenty of coders think it looks cool, or trick, or shows off what impressive coding smarts they have to pack a good 10 line algorithm into a single line of code that uses some obscure language features but one day it's highly likely that someone else is going to have to come along to maintain that code, improve it or change part of what it does - at that moment it's no longer cool, and it was never really a smart thing to do
Aominee, in their comment, actually gives us something like an example of this:
return (int)list.stream().filter(e -> e.charAt.length()-1)==c).count();
It's a one line implementation of a solution to your problem. Cool huh? Well, it has a bug* (for a start) but it's not the main thrust of my argument. At a more basic level: have you got any idea what it's doing? can you look at it and in 2 seconds tell me how it works?
It's quite an advanced language feature, it's trick for sure, but it might be a very poor solution because it's hard to understand, hard to maintain as a result, and does a lot while looking like a little- it only really makes sense if you're well versed in the language. This one line bundles up a facility that loops over your list, a feature that effectively has a tiny sub method that is called for every item in the list, and whose job is to calculate if the name ends with the sought char
It p's a brilliant feature, a cute example and it surely has its place in production java, but it's place is probably not here, in your learning exercise
Similarly, I'd go as far to say that this line of yours:
if (n.getFirstName().length() - 1 == c) {
Is approaching "doing too much" - I say this because it's where your logic broke down; you didn't write enough code to effectively implement the algorithm. You'd actually have to write even more code to implement this way:
if (n.getFirstName().charAt(n.getFirstName().length() - 1) == c) {
This is a right eyeful to load into your brain and understand. The accepted answer broke it down a bit by first getting the name into a temporary variable. That's a sensible optimisation. I broke it out another step by getting the last char into a temp variable. In a production system I probably wouldn't go that far, but this is your learning phase - try to minimise the number of operations each of your lines does. It will aid your understanding of your own code a great deal
If you do ever get a penchant for writing as much code as possible in as few chars, look at some code golf games here on the stack exchange network; the game is to abuse as many language features as possible to make really short, trick code.. pretty much every winner stands as a testament to condense that should never, ever be put into a production system maintained by normal coders who value their sanity
*the bug is it doesn't get the first name out of the Name object

How can I build a model to distinguish tweets about Apple (Inc.) from tweets about apple (fruit)?

See below for 50 tweets about "apple." I have hand labeled the positive matches about Apple Inc. They are marked as 1 below.
Here are a couple of lines:
1|“#chrisgilmer: Apple targets big business with new iOS 7 features http://bit.ly/15F9JeF ”. Finally.. A corp iTunes account!
0|“#Zach_Paull: When did green skittles change from lime to green apple? #notafan” #Skittles
1|#dtfcdvEric: #MaroneyFan11 apple inc is searching for people to help and tryout all their upcoming tablet within our own net page No.
0|#STFUTimothy have you tried apple pie shine?
1|#SuryaRay #India Microsoft to bring Xbox and PC games to Apple, Android phones: Report: Microsoft Corp... http://dlvr.it/3YvbQx #SuryaRay
Here is the total data set: http://pastebin.com/eJuEb4eB
I need to build a model that classifies "Apple" (Inc). from the rest.
I'm not looking for a general overview of machine learning, rather I'm looking for actual model in code (Python preferred).

What you are looking for is called Named Entity Recognition. It is a statistical technique that (most commonly) uses Conditional Random Fields to find named entities, based on having been trained to learn things about named entities.
Essentially, it looks at the content and context of the word, (looking back and forward a few words), to estimate the probability that the word is a named entity.
Good software can look at other features of words, such as their length or shape (like "Vcv" if it starts with "Vowel-consonant-vowel")
A very good library (GPL) is Stanford's NER
Here's the demo: http://nlp.stanford.edu:8080/ner/
Some sample text to try:
I was eating an apple over at Apple headquarters and I thought about
Apple Martin, the daughter of the Coldplay guy
(the 3class and 4class classifiers get it right)

I would do it as follows:
Split the sentence into words, normalise them, build a dictionary
With each word, store how many times they occurred in tweets about the company, and how many times they appeared in tweets about the fruit - these tweets must be confirmed by a human
When a new tweet comes in, find every word in the tweet in the dictionary, calculate a weighted score - words that are used frequently in relation to the company would get a high company score, and vice versa; words used rarely, or used with both the company and the fruit, would not have much of a score.

I have a semi-working system that solves this problem, open sourced using scikit-learn, with a series of blog posts describing what I'm doing. The problem I'm tackling is word-sense disambiguation (choosing one of multiple word sense options), which is not the same as Named Entity Recognition. My basic approach is somewhat-competitive with existing solutions and (crucially) is customisable.
There are some existing commercial NER tools (OpenCalais, DBPedia Spotlight, and AlchemyAPI) that might give you a good enough commercial result - do try these first!
I used some of these for a client project (I consult using NLP/ML in London), but I wasn't happy with their recall (precision and recall). Basically they can be precise (when they say "This is Apple Inc" they're typically correct), but with low recall (they rarely say "This is Apple Inc" even though to a human the tweet is obviously about Apple Inc). I figured it'd be an intellectually interesting exercise to build an open source version tailored to tweets. Here's the current code:
https://github.com/ianozsvald/social_media_brand_disambiguator
I'll note - I'm not trying to solve the generalised word-sense disambiguation problem with this approach, just brand disambiguation (companies, people, etc.) when you already have their name. That's why I believe that this straightforward approach will work.
I started this six weeks ago, and it is written in Python 2.7 using scikit-learn. It uses a very basic approach. I vectorize using a binary count vectorizer (I only count whether a word appears, not how many times) with 1-3 n-grams. I don't scale with TF-IDF (TF-IDF is good when you have a variable document length; for me the tweets are only one or two sentences, and my testing results didn't show improvement with TF-IDF).
I use the basic tokenizer which is very basic but surprisingly useful. It ignores # # (so you lose some context) and of course doesn't expand a URL. I then train using logistic regression, and it seems that this problem is somewhat linearly separable (lots of terms for one class don't exist for the other). Currently I'm avoiding any stemming/cleaning (I'm trying The Simplest Possible Thing That Might Work).
The code has a full README, and you should be able to ingest your tweets relatively easily and then follow my suggestions for testing.
This works for Apple as people don't eat or drink Apple computers, nor do we type or play with fruit, so the words are easily split to one category or the other. This condition may not hold when considering something like #definance for the TV show (where people also use #definance in relation to the Arab Spring, cricket matches, exam revision and a music band). Cleverer approaches may well be required here.
I have a series of blog posts describing this project including a one-hour presentation I gave at the BrightonPython usergroup (which turned into a shorter presentation for 140 people at DataScienceLondon).
If you use something like LogisticRegression (where you get a probability for each classification) you can pick only the confident classifications, and that way you can force high precision by trading against recall (so you get correct results, but fewer of them). You'll have to tune this to your system.
Here's a possible algorithmic approach using scikit-learn:
Use a Binary CountVectorizer (I don't think term-counts in short messages add much information as most words occur only once)
Start with a Decision Tree classifier. It'll have explainable performance (see Overfitting with a Decision Tree for an example).
Move to logistic regression
Investigate the errors generated by the classifiers (read the DecisionTree's exported output or look at the coefficients in LogisticRegression, work the mis-classified tweets back through the Vectorizer to see what the underlying Bag of Words representation looks like - there will be fewer tokens there than you started with in the raw tweet - are there enough for a classification?)
Look at my example code in https://github.com/ianozsvald/social_media_brand_disambiguator/blob/master/learn1.py for a worked version of this approach
Things to consider:
You need a larger dataset. I'm using 2000 labelled tweets (it took me five hours), and as a minimum you want a balanced set with >100 per class (see the overfitting note below)
Improve the tokeniser (very easy with scikit-learn) to keep # # in tokens, and maybe add a capitalised-brand detector (as user #user2425429 notes)
Consider a non-linear classifier (like #oiez's suggestion above) when things get harder. Personally I found LinearSVC to do worse than logistic regression (but that may be due to the high-dimensional feature space that I've yet to reduce).
A tweet-specific part of speech tagger (in my humble opinion not Standford's as #Neil suggests - it performs poorly on poor Twitter grammar in my experience)
Once you have lots of tokens you'll probably want to do some dimensionality reduction (I've not tried this yet - see my blog post on LogisticRegression l1 l2 penalisation)
Re. overfitting. In my dataset with 2000 items I have a 10 minute snapshot from Twitter of 'apple' tweets. About 2/3 of the tweets are for Apple Inc, 1/3 for other-apple-uses. I pull out a balanced subset (about 584 rows I think) of each class and do five-fold cross validation for training.
Since I only have a 10 minute time-window I have many tweets about the same topic, and this is probably why my classifier does so well relative to existing tools - it will have overfit to the training features without generalising well (whereas the existing commercial tools perform worse on this snapshop, but more reliably across a wider set of data). I'll be expanding my time window to test this as a subsequent piece of work.

You can do the following:
Make a dict of words containing their count of occurrence in fruit and company related tweets. This can be achieved by feeding it some sample tweets whose inclination we know.
Using enough previous data, we can find out the probability of a word occurring in tweet about apple inc.
Multiply individual probabilities of words to get the probability of the whole tweet.
A simplified example:
p_f = Probability of fruit tweets.
p_w_f = Probability of a word occurring in a fruit tweet.
p_t_f = Combined probability of all words in tweet occurring a fruit tweet
= p_w1_f * p_w2_f * ...
p_f_t = Probability of fruit given a particular tweet.
p_c, p_w_c, p_t_c, p_c_t are respective values for company.
A laplacian smoother of value 1 is added to eliminate the problem of zero frequency of new words which are not there in our database.
old_tweets = {'apple pie sweet potatoe cake baby https://vine.co/v/hzBaWVA3IE3': '0', ...}
known_words = {}
total_company_tweets = total_fruit_tweets =total_company_words = total_fruit_words = 0
for tweet in old_tweets:
company = old_tweets[tweet]
for word in tweet.lower().split(" "):
if not word in known_words:
known_words[word] = {"company":0, "fruit":0 }
if company == "1":
known_words[word]["company"] += 1
total_company_words += 1
else:
known_words[word]["fruit"] += 1
total_fruit_words += 1
if company == "1":
total_company_tweets += 1
else:
total_fruit_tweets += 1
total_tweets = len(old_tweets)
def predict_tweet(new_tweet,K=1):
p_f = (total_fruit_tweets+K)/(total_tweets+K*2)
p_c = (total_company_tweets+K)/(total_tweets+K*2)
new_words = new_tweet.lower().split(" ")
p_t_f = p_t_c = 1
for word in new_words:
try:
wordFound = known_words[word]
except KeyError:
wordFound = {'fruit':0,'company':0}
p_w_f = (wordFound['fruit']+K)/(total_fruit_words+K*(len(known_words)))
p_w_c = (wordFound['company']+K)/(total_company_words+K*(len(known_words)))
p_t_f *= p_w_f
p_t_c *= p_w_c
#Applying bayes rule
p_f_t = p_f * p_t_f/(p_t_f*p_f + p_t_c*p_c)
p_c_t = p_c * p_t_c/(p_t_f*p_f + p_t_c*p_c)
if p_c_t > p_f_t:
return "Company"
return "Fruit"

If you don't have an issue using an outside library, I'd recommend scikit-learn since it can probably do this better & faster than anything you could code by yourself. I'd just do something like this:
Build your corpus. I did the list comprehensions for clarity, but depending on how your data is stored you might need to do different things:
def corpus_builder(apple_inc_tweets, apple_fruit_tweets):
corpus = [tweet for tweet in apple_inc_tweets] + [tweet for tweet in apple_fruit_tweets]
labels = [1 for x in xrange(len(apple_inc_tweets))] + [0 for x in xrange(len(apple_fruit_tweets))]
return (corpus, labels)
The important thing is you end up with two lists that look like this:
([['apple inc tweet i love ios and iphones'], ['apple iphones are great'], ['apple fruit tweet i love pie'], ['apple pie is great']], [1, 1, 0, 0])
The [1, 1, 0, 0] represent the positive and negative labels.
Then, you create a Pipeline! Pipeline is a scikit-learn class that makes it easy to chain text processing steps together so you only have to call one object when training/predicting:
def train(corpus, labels)
pipe = Pipeline([('vect', CountVectorizer(ngram_range=(1, 3), stop_words='english')),
('tfidf', TfidfTransformer(norm='l2')),
('clf', LinearSVC()),])
pipe.fit_transform(corpus, labels)
return pipe
Inside the Pipeline there are three processing steps. The CountVectorizer tokenizes the words, splits them, counts them, and transforms the data into a sparse matrix. The TfidfTransformer is optional, and you might want to remove it depending on the accuracy rating (doing cross validation tests and a grid search for the best parameters is a bit involved, so I won't get into it here). The LinearSVC is a standard text classification algorithm.
Finally, you predict the category of tweets:
def predict(pipe, tweet):
prediction = pipe.predict([tweet])
return prediction
Again, the tweet needs to be in a list, so I assumed it was entering the function as a string.
Put all those into a class or whatever, and you're done. At least, with this very basic example.
I didn't test this code so it might not work if you just copy-paste, but if you want to use scikit-learn it should give you an idea of where to start.
EDIT: tried to explain the steps in more detail.

Using a decision tree seems to work quite well for this problem. At least it produces a higher accuracy than a naive bayes classifier with my chosen features.
If you want to play around with some possibilities, you can use the following code, which requires nltk to be installed. The nltk book is also freely available online, so you might want to read a bit about how all of this actually works: http://nltk.googlecode.com/svn/trunk/doc/book/ch06.html
#coding: utf-8
import nltk
import random
import re
def get_split_sets():
structured_dataset = get_dataset()
train_set = set(random.sample(structured_dataset, int(len(structured_dataset) * 0.7)))
test_set = [x for x in structured_dataset if x not in train_set]
train_set = [(tweet_features(x[1]), x[0]) for x in train_set]
test_set = [(tweet_features(x[1]), x[0]) for x in test_set]
return (train_set, test_set)
def check_accurracy(times=5):
s = 0
for _ in xrange(times):
train_set, test_set = get_split_sets()
c = nltk.classify.DecisionTreeClassifier.train(train_set)
# Uncomment to use a naive bayes classifier instead
#c = nltk.classify.NaiveBayesClassifier.train(train_set)
s += nltk.classify.accuracy(c, test_set)
return s / times
def remove_urls(tweet):
tweet = re.sub(r'http:\/\/[^ ]+', "", tweet)
tweet = re.sub(r'pic.twitter.com/[^ ]+', "", tweet)
return tweet
def tweet_features(tweet):
words = [x for x in nltk.tokenize.wordpunct_tokenize(remove_urls(tweet.lower())) if x.isalpha()]
features = dict()
for bigram in nltk.bigrams(words):
features["hasBigram(%s)" % ",".join(bigram)] = True
for trigram in nltk.trigrams(words):
features["hasTrigram(%s)" % ",".join(trigram)] = True
return features
def get_dataset():
dataset = """copy dataset in here
"""
structured_dataset = [('fruit' if x[0] == '0' else 'company', x[2:]) for x in dataset.splitlines()]
return structured_dataset
if __name__ == '__main__':
print check_accurracy()

Thank you for the comments thus far. Here is a working solution I prepared with PHP. I'd still be interested in hearing from others a more algorithmic approach to this same solution.
<?php
// Confusion Matrix Init
$tp = 0;
$fp = 0;
$fn = 0;
$tn = 0;
$arrFP = array();
$arrFN = array();
// Load All Tweets to string
$ch = curl_init();
curl_setopt($ch, CURLOPT_URL, 'http://pastebin.com/raw.php?i=m6pP8ctM');
curl_setopt($ch, CURLOPT_RETURNTRANSFER, 1);
$strCorpus = curl_exec($ch);
curl_close($ch);
// Load Tweets as Array
$arrCorpus = explode("\n", $strCorpus);
foreach ($arrCorpus as $k => $v) {
// init
$blnActualClass = substr($v,0,1);
$strTweet = trim(substr($v,2));
// Score Tweet
$intScore = score($strTweet);
// Build Confusion Matrix and Log False Positives & Negatives for Review
if ($intScore > 0) {
if ($blnActualClass == 1) {
// True Positive
$tp++;
} else {
// False Positive
$fp++;
$arrFP[] = $strTweet;
}
} else {
if ($blnActualClass == 1) {
// False Negative
$fn++;
$arrFN[] = $strTweet;
} else {
// True Negative
$tn++;
}
}
}
// Confusion Matrix and Logging
echo "
Predicted
1 0
Actual 1 $tp $fp
Actual 0 $fn $tn
";
if (count($arrFP) > 0) {
echo "\n\nFalse Positives\n";
foreach ($arrFP as $strTweet) {
echo "$strTweet\n";
}
}
if (count($arrFN) > 0) {
echo "\n\nFalse Negatives\n";
foreach ($arrFN as $strTweet) {
echo "$strTweet\n";
}
}
function LoadDictionaryArray() {
$strDictionary = <<<EOD
10|iTunes
10|ios 7
10|ios7
10|iPhone
10|apple inc
10|apple corp
10|apple.com
10|MacBook
10|desk top
10|desktop
1|config
1|facebook
1|snapchat
1|intel
1|investor
1|news
1|labs
1|gadget
1|apple store
1|microsoft
1|android
1|bonds
1|Corp.tax
1|macs
-1|pie
-1|clientes
-1|green apple
-1|banana
-10|apple pie
EOD;
$arrDictionary = explode("\n", $strDictionary);
foreach ($arrDictionary as $k => $v) {
$arr = explode('|', $v);
$arrDictionary[$k] = array('value' => $arr[0], 'term' => strtolower(trim($arr[1])));
}
return $arrDictionary;
}
function score($str) {
$str = strtolower($str);
$intScore = 0;
foreach (LoadDictionaryArray() as $arrDictionaryItem) {
if (strpos($str,$arrDictionaryItem['term']) !== false) {
$intScore += $arrDictionaryItem['value'];
}
}
return $intScore;
}
?>
The above outputs:
Predicted
1 0
Actual 1 31 1
Actual 0 1 17
False Positives
1|Royals apple #ASGame #mlb # News Corp Building http://instagram.com/p/bBzzgMrrIV/
False Negatives
-1|RT #MaxFreixenet: Apple no tiene clientes. Tiene FANS// error.... PAGAS por productos y apps, ergo: ERES CLIENTE.

In all the examples that you gave, Apple(inc) was either referred to as Apple or apple inc, so a possible way could be to search for:
a capital "A" in Apple
an "inc" after apple
words/phrases like "OS", "operating system", "Mac", "iPhone", ...
or a combination of them

To simplify answers based on Conditional Random Fields a bit...context is huge here. You will want to pick out in those tweets that clearly show Apple the company vs apple the fruit. Let me outline a list of features here that might be useful for you to start with. For more information look up noun phrase chunking, and something called BIO labels. See (http://www.cis.upenn.edu/~pereira/papers/crf.pdf)
Surrounding words: Build a feature vector for the previous word and the next word, or if you want more features perhaps the previous 2 and next 2 words. You don't want too many words in the model or it won't match the data very well.
In Natural Language Processing, you are going to want to keep this as general as possible.
Other features to get from surrounding words include the following:
Whether the first character is a capital
Whether the last character in the word is a period
The part of speech of the word (Look up part of speech tagging)
The text itself of the word
I don't advise this, but to give more examples of features specifically for Apple:
WordIs(Apple)
NextWordIs(Inc.)
You get the point. Think of Named Entity Recognition as describing a sequence, and then using some math to tell a computer how to calculate that.
Keep in mind that natural language processing is a pipeline based system. Typically, you break things in to sentences, move to tokenization, then do part of speech tagging or even dependency parsing.
This is all to get you a list of features you can use in your model to identify what you're looking for.

There's a really good library for processing natural language text in Python called nltk. You should take a look at it.
One strategy you could try is to look at n-grams (groups of words) with the word "apple" in them. Some words are more likely to be used next to "apple" when talking about the fruit, others when talking about the company, and you can use those to classify tweets.

Use LibShortText. This Python utility has already been tuned to work for short text categorization tasks, and it works well. The maximum you'll have to do is to write a loop to pick the best combination of flags. I used it to do supervised speech act classification in emails and the results were up to 95-97% accurate (during 5 fold cross validation!).
And it comes from the makers of LIBSVM and LIBLINEAR whose support vector machine (SVM) implementation is used in sklearn and cran, so you can be reasonably assured that their implementation is not buggy.

Make an AI filter to distinguish Apple Inc (the company) from apple (the fruit). Since these are tweets, define your training set with a vector of 140 fields, each field being the character written in the tweet at position X (0 to 139). If the tweet is shorter, just give a value for being blank.
Then build a training set big enough to get a good accuracy (subjective to your taste). Assign a result value to each tweet, a Apple Inc tweet get 1 (true) and an apple tweet (fruit) gets 0. It would be a case of supervised learning in a logistic regression.
That is machine learning, is generally easier to code and performs better. It has to learn from the set you give it, and it's not hardcoded.
I don't know Python, so I can not write the code for it, but if you were to take more time for machine learning's logic and theory you might want to look the class I'm following.
Try the Coursera course Machine Learning by Andrew Ng. You will learn machine learning on MATLAB or Octave, but once you get the basics you will be able to write machine learning in about any language if you do understand the simple math (simple in logistic regression).
That is, getting the code from someone won't make you able to understand what is going in the machine learning code. You might want to invest a couple of hours on the subject to see what is really going on.

I would recommend avoiding answers suggesting entity recognition. Because this task is a text-classification first and entity recognition second (you can do it without the entity recognition at all).
I think the fastest path to results will be spacy + prodigy.
Spacy has well thought through model for English language, so you don't have to build your own. While prodigy allows quickly create training datasets and fine tune spacy model for your needs.
If you have enough samples, you can have a decent model in 1 day.

using java to parse a csv then save in 2D array

Okay so i am working on a game based on a Trading card game in java. I Scraped all of the game peices' "information" into a csv file where each row is a game peice and each column is a type of attribute for that peice. I have spent hours upon hours writing code with Buffered reader and etc. trying to extract the information from my csv file into a 2d Array but to no avail. My csv file is linked Here: http://dl.dropbox.com/u/3625527/MonstersFinal.csv I have one year of computer science under my belt but I still cannot figure out how to do this.
So my main question is how do i place this into a 2D array that way i can keep the rows and columns?

Well, as mentioned before, some of your strings contain commas, so initially you're starting from a bad place, but I do have a solution and it's this:
--------- If possible, rescrape the site, but perform a simple encoding operation when you do. You'll want to do something like what you'll notice tends to be done in autogenerated XML files which contain HTML; reserve a 'control character' (a printable character works best, here, for reasons of debugging and... well... sanity) that, once encoded, is never meant to be read directly as an instance of itself. Ampersand is what I like to use because it's uncommon enough but still printable, but really what character you want to use is up to you. What I would do is write the program so that, at every instance of ",", that comma would be replaced by "&c" before being written to the CSV, and at every instance of an actual ampersand on the site, that "&" would be replaced by "&a". That way, you would never have the issue of accidentally separating a single value into two in the CSV, and you could simply decode each value after you've separated them by the method I'm about to outline in...
-------- Assuming you know how many columns will be in each row, you can use the StringTokenizer class (look it up- it's awesome and built into Java. A good place to look for information is, as always, the Java Tutorials) to automatically give you the values you need in the form of an array.
It works by your passing in a string and a delimiter (in this case, the delimiter would be ','), and it spitting out all the substrings which were separated by those commas. If you know how many pieces there are in total from the get-go, you can instantiate a 2D array at the beginning and just plug in each row the StringTokenizer gives them to you. If you don't, it's still okay, because you can use an ArrayList. An ArrayList is nice because it's a higher-level abstraction of an array that automatically asks for more memory such that you can continue adding to it and know that retrieval time will always be constant. However, if you plan on dynamically adding pieces, and doing that more often than retrieving them, you might want to use a LinkedList instead, because it has a linear retrieval time, but a much better relation than an ArrayList for add-remove time. Or, if you're awesome, you could use a SkipList instead. I don't know if they're implemented by default in Java, but they're awesome. Fair warning, though; the cost of speed on retrieval, removal, and placement comes with increased overhead in terms of memory. Skip lists maintain a lot of pointers.
If you know there should be the same number of values in each row, and you want them to be positionally organized, but for whatever reason your scraper doesn't handle the lack of a value for a row, and just doesn't put that value, you've some bad news... it would be easier to rewrite the part of the scraper code that deals with the lack of values than it would be to write a method that interprets varying length arrays and instantiates a Piece object for each array. My suggestion for this would again be to use the control character and fill empty columns with &n (for 'null') to be interpreted later, but then specifics are of course what will individuate your code and coding style so it's not for me to say.
edit: I think the main thing you should focus on is learning the different standard library datatypes available in Java, and maybe learn to implement some of them yourself for practice. I remember implementing a binary search tree- not an AVL tree, but alright. It's fun enough, good coding practice, and, more importantly, necessary if you want to be able to do things quickly and efficiently. I don't know exactly how Java implements arrays, because the definition is "a contiguous section of memory", yet you can allocate memory for them in Java at runtime using variables... but regardless of the specific Java implementation, arrays often aren't the best solution. Also, knowing regular expressions makes everything much easier. For practice, I'd recommend working them into your Java programs, or, if you don't want to have to compile and jar things every time, your bash scripts (if your using *nix) and/or batch scripts (if you're using Windows).

I think the way you've scraped the data makes this problem more difficult than it needs to be. Your scrape seems inconsistent and difficult to work with given that most values are surrounded by quotes inconsistently, some data already has commas in it, and not each card is on its own line.
Try re-scraping the data in a much more consistent format, such as:
R1C1|R1C2|R1C3|R1C4|R1C5|R1C6|R1C7|R1C8
R2C1|R2C2|R2C3|R2C4|R2C5|R2C6|R2C7|R3C8
R3C1|R3C2|R3C3|R3C4|R3C5|R3C6|R3C7|R3C8
R4C1|R4C2|R4C3|R4C4|R4C5|R4C6|R4C7|R4C8
A/D Changer|DREV-EN005|Effect Monster|Light|Warrior|100|100|You can remove from play this card in your Graveyard to select 1 monster on the field. Change its battle position.
Where each line is definitely its own card (As opposed to the example CSV you posted with new lines in odd places) and the delimiter is never used in a data field as something other than a delimiter.
Once you've gotten the input into a consistently readable state, it becomes very simple to parse through it:
BufferedReader br = new BufferedReader(new FileReader(new File("MonstersFinal.csv")));
String line = "";
ArrayList<String[]> cardList = new ArrayList<String[]>(); // Use an arraylist because we might not know how many cards we need to parse.
while((line = br.readLine()) != null) { // Read a single line from the file until there are no more lines to read
StringTokenizer st = new StringTokenizer(line, "|"); // "|" is the delimiter of our input file.
String[] card = new String[8]; // Each card has 8 fields, so we need room for the 8 tokens.
for(int i = 0; i < 8; i++) { // For each token in the line that we've read:
String value = st.nextToken(); // Read the token
card[i] = value; // Place the token into the ith "column"
}
cardList.add(card); // Add the card's info to the list of cards.
}
for(int i = 0; i < cardList.size(); i++) {
for(int x = 0; x < cardList.get(i).length; x++) {
System.out.printf("card[%d][%d]: ", i, x);
System.out.println(cardList.get(i)[x]);
}
}
Which would produce the following output for my given example input:
card[0][0]: R1C1
card[0][1]: R1C2
card[0][2]: R1C3
card[0][3]: R1C4
card[0][4]: R1C5
card[0][5]: R1C6
card[0][6]: R1C7
card[0][7]: R1C8
card[1][0]: R2C1
card[1][1]: R2C2
card[1][2]: R2C3
card[1][3]: R2C4
card[1][4]: R2C5
card[1][5]: R2C6
card[1][6]: R2C7
card[1][7]: R3C8
card[2][0]: R3C1
card[2][1]: R3C2
card[2][2]: R3C3
card[2][3]: R3C4
card[2][4]: R3C5
card[2][5]: R3C6
card[2][6]: R3C7
card[2][7]: R4C8
card[3][0]: R4C1
card[3][1]: R4C2
card[3][2]: R4C3
card[3][3]: R4C4
card[3][4]: R4C5
card[3][5]: R4C6
card[3][6]: R4C7
card[3][7]: R4C8
card[4][0]: A/D Changer
card[4][1]: DREV-EN005
card[4][2]: Effect Monster
card[4][3]: Light
card[4][4]: Warrior
card[4][5]: 100
card[4][6]: 100
card[4][7]: You can remove from play this card in your Graveyard to select 1 monster on the field. Change its battle position.
I hope re-scraping the information is an option here and I hope I haven't misunderstood anything; Good luck!
On a final note, don't forget to take advantage of OOP once you've gotten things worked out. a Card class could make working with the data even simpler.

I'm working on a similar problem for use in machine learning, so let me share what I've been able to do on the topic.
1) If you know before you start parsing the row - whether it's hard-coded into your program or whether you've got some header in your file that gives you this information (highly recommended) - how many attributes per row there will be, you can reasonably split it by comma, for example the first attribute will be RowString.substring(0, RowString.indexOf(',')), the second attribute will be the substring from the first comma to the next comma (writing a function to find the nth instance of a comma, or simply chopping off bits of the string as you go through it, should be fairly trivial), and the last attribute will be RowString.substring(RowString.lastIndexOf(','), RowString.length()). The String class's methods are your friends here.
2) If you are having trouble distinguishing between commas which are meant to separate values, and commas which are part of a string-formatted attribute, then (if the file is small enough to reformat by hand) do what Java does - represent characters with special meaning that are inside of strings with '\,' rather than just ','. That way you can search for the index of ',' and not '\,' so that you will have some way of distinguishing your characters.
3) As an alternative to 2), CSVs (in my opinion) aren't great for strings, which often include commas. There is no real common format to CSVs, so why not make them colon-separated-values, or dash-separated-values, or even triple-ampersand-separated-values? The point of separating values with commas is to make it easy to tell them apart, and if commas don't do the job there's no reason to keep them. Again, this applies only if your file is small enough to edit by hand.
4) Looking at your file for more than just the format, it becomes apparent that you can't do it by hand. Additionally, it would appear that some strings are surrounded by triple double quotes ("""string""") and some are surrounded by single double quotes ("string"). If I had to guess, I would say that anything included in a quotes is a single attribute - there are, for example, no pairs of quotes that start in one attribute and end in another. So I would say that you could:
Make a class with a method to break a string into each comma-separated fields.
Write that method such that it ignores commas preceded by an odd number of double quotes (this way, if the quote-pair hasn't been closed, it knows that it's inside a string and that the comma is not a value separator). This strategy, however, fails if the creator of your file did something like enclose some strings in double double quotes (""string""), so you may need a more comprehensive approach.

Printing an ArrayList of Strings to a PrintWriter with word wrap

Some classmates and I are working on a homework assignment for Java that requires we print an ArrayList of Strings to a PrintWriter using word wrap, so that none of the output passes 80 characters. We've extensively Googled this and can't find any Java API based way to do this.
I know it's generally "wrong" to ask a homework question on SO, but we're just looking for recommendations of the best way to do this, or if we missed something in the API. This isn't the major part of the homework, just a small output requirement.
Ideally, I'd like to be able to wordwrap the ArrayList's toString since it's nicely formatted already.

Well, this is a first for me, it's the first time one of my students has posted a question about one of the projects I've assigned them. The way it was phrased, that he was looking for an algorithm, and the answers you've all shared are just fine with me. However, this is a typical case of trying to make things too complicated. A part of the spec that was not mentioned was that the 80 characters limit was not a hard limit. I said that each line of the output file had to be roughly 80 characters long. It was OK to go over 80 a little. In my version of the solution, I just had a running count and did a modulus of the count to add the line end. I varied the value of the modulus until the output file looked right. This resulted in lines with small numbers being really short so I used a different modulus when the numbers were small. This wasn't a big part of the project and it's interesting that this got so much attention.

Our solution was to create a temporary string and append elements one by one, followed by a comma. Before adding an element, check if adding it will make the string longer than 80 characters and choose whether to print it and reset or just append.
This still has the issue with the extra trailing comma, but that's been dealt with so many times we'll be fine. I was looking to avoid this because it was originally more complicated in my head than it really is.

I think that better solution is to create your own WrapTextWriter that wraps any other writer and overrides method public void write(String str, int off, int len) throws IOException. Here it should run in loop and perform logic of wrapping.
This logic is not as simple as str.substring(80). If you are dealing with real text and wish to wrap it correctly (i.e. do not cut words, do not move comas or dots to the next line etc) you have to implement some logic. it is probably not too complicated but probably language dependent. For example in English there is not space between word and colon while in French they put space between them.
So, I performed 5 second googling and found the following discussion that can help you.

private static final int MAX_CHARACTERS = 80;
public static void main(String[] args)
throws FileNotFoundException
{
List<String> strings = new ArrayList<String>();
int size = 0;
PrintWriter writer = new PrintWriter(System.out, true); // Just as example
for (String str : strings)
{
size += str.length();
if (size > MAX_CHARACTERS)
{
writer.print(System.getProperty("line.separator") + str);
size = 0;
}
else
writer.print(str);
}
}
You can simply write a function, like "void printWordWrap(List<String> strings)", with that algorithm inside. I think, it`s a good way to solve your problem. :)

Problem with terminated paths in simple recursive algorithm

First of all: this is not a homework assignment, it's for a hobby project of mine.
Background:
For my Java puzzle game I use a very simple recursive algorithm to check if certain spaces on the 'map' have become isolated after a piece is placed. Isolated in this case means: where no pieces can be placed in.
Current Algorithm:
public int isolatedSpace(Tile currentTile, int currentSpace){
if(currentTile != null){
if(currentTile.isOpen()){
currentTile.flag(); // mark as visited
currentSpace += 1;
currentSpace = isolatedSpace(currentTile.rightNeighbor(),currentSpace);
currentSpace = isolatedSpace(currentTile.underNeighbor(),currentSpace);
currentSpace = isolatedSpace(currentTile.leftNeighbor(),currentSpace);
currentSpace = isolatedSpace(currentTile.upperNeighbor(),currentSpace);
if(currentSpace < 3){currentTile.markAsIsolated();} // <-- the problem
}
}
return currentSpace;
}
This piece of code returns the size of the empty space where the starting tile is part of. That part of the code works as intented. But I came across a problem regarding the marking of the tiles and that is what makes the title of this question relevant ;)
The problem:
The problem is that certain tiles are never 'revisited' (they return a value and terminate, so never get a return value themselves from a later incarnation to update the size of the empty space). These 'forgotten' tiles can be part of a large space but still marked as isolated because they were visited at the beginning of the process when currentSpace had a low value.
Question:
How to improve this code so it sets the correct value to the tiles without too much overhead? I can think of ugly solutions like revisiting all flagged tiles and if they have the proper value check if the neighbors have the same value, if not update etc. But I'm sure there are brilliant people here on Stack Overflow with much better ideas ;)
Update:
I've made some changes.
public int isolatedSpace(Tile currentTile, int currentSpace, LinkedList<Tile> visitedTiles){
if(currentTile != null){
if(currentTile.isOpen()){
// do the same as before
visitedTiles.add();
}
}
return currentSpace;
}
And the marktiles function (only called when the returned spacesize is smaller than a given value)
marktiles(visitedTiles){
for(Tile t : visitedTiles){
t.markAsIsolated();
}
}
This approach is in line with the answer of Rex Kerr, at least if I understood his idea.

This isn't a general solution, but you only mark spaces as isolated if they occur in a region of two or fewer spaces. Can't you simplify this test to "a space is isolated iff either (a) it has no open neighbours or (b) precisely one open neighbour and that neighbour has no other open neighbours".

You need to have a two-step process: gathering info about whether a space is isolated, and then then marking as isolated separately. So you'll need to first count up all the spaces (using one recursive function) and then mark all connected spaces if the criterion passes (using a different recursive function).