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To survive in the forest one must consider multiple aspects, these aspects consist of:Food, Shelter, Defences and supplies.
Even knowing that navigating in the forest can be quite hard (seeing as you currently have no map) it can be of extreme importance when it comes to finding a spot to set up camp.
So let's say you have found the perfect spot to create your over-complex extreme super fortress of violent murder and cassette raves.
Sadly you're never truly alone on the island, instead there are cannibalistic mutant tribes that will do their darndest to terrorize, hurt, and haunt you forever.
So you've tried everything to avoid or evade those nasty mutants we have been mentioning but they just won't let off?Well then there remains only one reall solution, combat!
This part of the guide will be focusing on gear which you are currently not able to craft and are only possible to acquire in game through looting.
In this current build there is not really much that you can do besides build a super awesome radical base and raid the fearsome mutant camps and caves, but let's say that you have done all of that and you want something more. First of all I would like to thank doctor dentist for the awesome map that he has created, secondly not all of the images used where made by me. Easy to find spider, go to middle west of island, u will find boat like that map, go to boat, u will find dead human in bed with cross, and u will find picture of spider canibal, go to island, say hi~! A Huge Forest with a Bedrock Walls encasing it, it has tall mountains it also has a bunch of items to start with. First, download the test and training set from the data page of the competition (here is a zip of the two small files in case the page from kaggle is removed in the future). The Amelia R package is a toolbox around missing values, in particular for performing imputation of the missing data. From those maps, you can immediately observe that only the age feature is badly suffering from missing data. The caret package is a kind of toolbox for homogenising the many existing R packages for classification and regression and also provide out of the box a standard way to perform common tasks like model parameters tuning and more. Here is a snippet of code where i successively train a random forest and a gradient boosting machine (GBM) using the same train function from caret.
We’ll discuss later the features used in the formula but note the fitControl parameter which is passed in the call for training the GBM.
I’ve hinted earlier that the number of missing ages was too high and the training set too small to just ignore the records having a missing age.
We explored on a comprehensive example how R can be used to build and tune quickly robust predictive models which are significantly outperforming the baseline. Also, note that a competition settings might be very different than a real production settings. You have a collection of text documents, and you want to build their TF-IDF weight vectors, probably before doing some clustering on the collection or other related tasks.
You would like to be able for instance to see what are the tokens with the biggest TF-IDF weights in any given document of the collection. If you don’t know how to build a Lucene index, check the links at the end of the post. The two only important things in that step are to have in your index a field that can serve as a document id and to enable term vectors on the text field representing the content of your documents. A binary SequenceFile (a class coming from hadoop) that will contains all the TF-IDF weighted vectors.
Now, let’s say that you want for a given document id, to see what are the tokens that received the biggest weights in order to feel what are the most significant tokens of that document (as the weighting scheme sees it). To do so, you can for instance easily load the content of the generated dictionary file into a Map with token index as keys and the tokens as values. From there you’ll be able easily to plug your code to do whatever you want with the tokens and their weights, like printing the token having the biggest weights in a given document. It is also useful just to understand a little bit more of how mahout represents the data internally. Not updated for the latest Lucene version but that blog post should get you quickly started on how to build a lucene index.
I’m regularly using the excellent google collections library (now final and part of the more general guava libraries). I need those kind of statistics  so frequently that I was surprised to not find an existing utility method allowing to sort the entries of a multiset per entry count (or number of occurrences). If you got any other better or most efficient way to do it (or if you know an existing utility method that does it), please share. If you never used google collections, in addition to the official website, you might find those tutorials (1 and 2)  useful for an introduction. Lately I wrote a post on how to build a relevant real time search engine prototype in few hundreds lines of code. I wrote a small program on top of this prototype to run an experiment: each hour, the program crawl the new list of hot queries from google hot trends, then it runs the prototype on each of those queries and keep the hottest link found in twitter for the corresponding hot query. So I let ran the program for a week, collected the  links (more than a thousand), expanded all those into their long URLs version (using an improved version of my java universal URL expander),  extracted the domain names and compiled the whole into a top 10 list of the most cited websites. All the links spotted by my prototype and that appear in the table are coming from real tweets around those google hot trends queries. You’ll notice that apple iPad announcement is a theme that was covered by 4 of those top 10 websites!
Sometimes the hot query seems to be not connected with the related article at first view (like with fred baron). To finish, find below a picasa collage that I built using the most cited twitpic pictures in twitter for this past week of hot trends (not only the 5 cited in the table).
In general you can even use the same reducer method as both your reducer and your combiner.
That sounds pretty simple and useful and at first look you would be ready to use combiners all the time by adding this simple line, but there is a small catch. More generally, combiners can be used when the function you want to apply is both commutative and associative (that’s pretty intuitive to understand why). Note that for the mean function you can use a workaround for using combiners by using two separate reduce methods, a first one that would be used as the addition function (and thus that can be set as the combiner) that would emit the intermediate sum as the key and the number of addition involved as the value, and a second reduce function that would compute the mean by taking into account the number of addition involved (see the references for more details on that). As usual in this series, let’s observe the lesson learned in action using our learning playground. See the 4th tip of this must read blog post by Todd Lipcon for feeling better the benefit of combiners on a 40GB wordcount job benchmark. For a deeper understanding of when and how combiners are used in the mapReduce data flow, check this section of the (quiet heavy but) excellent Yahoo! To extend the intuition given in the post on why combiners help, you can go over this walk-through. Both Hadoop the definitive guide and Hadoop in Action contains interesting information on combiners (part of both of them inspired this post). Even if you have never defined any counters in hadoop, you can see some of them each time you are running an hadoop job. It is very handy to define your own counters to track any kind of statistics about the records you are manipulating in the mapper and the reducer.
If you are executing a job  and you see an abnormally high number of malformed records, it can give a good hint that you perhaps have a bug in your code or some problem with your data (note this is actually a much simpler way to spot issues than tracking error messages in a distributed set of log files).
Since we are interested in unique tokens, we will put the code related with the counter into the reduce method. So we can see now that we have 33783 unique tokens, 32511 starting with a letter and 263 starting with a digit. Basically it should take as input a  search query and return as output a ranked set of URLs that would correspond to the latest hot news around that search query.

In some way it is similar to what you would expect to find on google news or in one of the dozens real time search engine that were released last year (let’s cite oneriot, crowdeye and collecta). The goal of my prototype is to demonstrate how to leverage twitter and a simple ranking algorithm to obtain most of the time relevant URLs in response of hot queries, without having to crawl a single web page! Basically what I did is to use the twitter API through a java library called twitter4j to retrieve all the latest tweets containing the input query and that contains a link. As most of the URLs in tweets are shortened URLs, the trick is to spot the same URLs that were shortened by different shortening services. Note that you can improve the ranking algorithm in tons of way, by exploiting the text in the tweets or who actually wrote the tweet (reputation) or using other sources like digg and much more, but as we’ll see, even in its simplest form, the ranking algorithm presented above works pretty well. To find some hot queries to play with, you can for instance take one of the google hot trends queries (unfortunately down from 100 to 40 to 20).
Given the fact that at the time I issued the query, the Nexus one was not yet released, I would say that the article that the prototype found is the best one since it is the only one that present an exclusive video demonstrating the not yet released phone. Again this is a very relevant result (oneRiot and Google News gave the same one at that time). What is interesting with hot queries is that you expect the result to change even within a short amount of time. In the previous section, the prototype’s first result was a very relevant article from engadget.
You must  have java 6 installed (you can check by opening a console and type java -version). Also I tried to wrote at least a minimal amount of comments, in particular where I think the code should be improved a lot for better performance but remember: the prototype is of course not scalable as it does not rely on any indexing strategy (it computes the results at query time). In that sense, among the dozens of real time search engines I have tested, my favorite one remains oneriot. As you can see, the tokens are correctly ordered by number of occurrences on each reducer (which is what hadoop guarantees by default) but this is not what you need!
Please see the instructions page for reasons why this item might not work within The Forest.
The easiest way to navigate is to go to the beach and use a user created map to find your bearings. Just bear in mind that killing of the mutants can be extremely difficult if you are unpreppared or if you only have a melee weapon. Well the only thing that resembles a storyline in the game currently is the option to find all the dead passengers. If you make a decent base and PM me your map, I will personally create you a map that you can share to anyone you want. Considering how small is the training set, you can hardly just ignore records having a missing age.
Also, the author (Max Khun) did an amazing job at documenting the package in the vignettes (here or here for a longer but older version) and on the package dedicated website. Here, we’re interested in the threshold corresponding to the top left corner of the curve maximising sensitivity and specificity. At least for me, any attempt to impute the missing ages (either in naive or more sophisticated ways) didn’t lead to any significant accuracy improvement on the 10-fold cross validation test. And it also makes sense intuitively that Title is a good proxy for the too many missing ages, allowing for totally ignoring the age feature and thus keep all the data in the training set, without introducing any potential noise with an imputation method.
Note also that this particular competition lasts 1 year and was just for learning purpose, so there are thousands and thousands of participants, including not few people who obviously spent useless time to extract the answers from publicly available lists (e.g. Of course, it is somehow a toy example but it was interesting enough to explore some important aspects needed when building predictive models.
You’ll identify easily some sarcastic pictures before the iPad announcement or pics around the election of Miss USA. Combiners are another important Hadoop’s feature that every hadoop developer should be aware of. This is why a combiner must implement the Reducer interface (or extend the Reducer class as of hadoop 0.20). That’s the case for the addition function, this is why the word count example can benefit from combiners but not for the mean function (which is not associative as shown in the counter example above). In particular the first contains a great section on when exactly the combiners comes into play in the mapReduce data flow. As my primary target is relevancy, I won’t invest any effort on performance or scalability of the prototype (retrieved results will be build at query time).
This is also why so much people were twitting about this link: because it was the best at that precise time! As it is a toy project and to keep things simple I voluntarily didn’t use any DI framework like spring or guice and tried to use as less external libraries as possible unless necessary (even no log4j!). Building a real a real search engine would at first involve building an index offline (using lucene for instance).
This is made possible by judiciously using the tremendous power that twitter provide with its open API. In this issue, we’ll use our playground to investigate the partitioning features offered by Hadoop. If you don't want any cannibals in your base I would highly recommend building a system with stairs and platforms seeing as mutants can't walk stairs very well.As an example in this guide I will use my old base which had 2 defense perimeters. However these are not your basic game AI enemies that take just 2 hits to kill together with super-uber predictable pathfinding mechanics. To make sure that you are going to surthrive in every single situation imagenable we will be featuring multiple combat scenarios.1.
Axes(all are currently believed to be identical)The plane axe The rusty axeThe modern axe (it is no longer obtainable in v0.02) 2. I have not done this myself nor do I think that I ever will but if you're bored and are playing the forest this is something that you could go and try to pursue.
All creations copyright of the creators.Minecraft is copyright Mojang AB and is not affiliated with this site.
We’ll see later in the post what kind of strategy we can use to deal with that issue. For a standalone very clear example in R, this post is what you need (the code below is inspired from it).
I used lucene because my documents were in a data store and building the lucene index out of it was just much more flexible and convenient.
But most of the time, when I need a multiset to track the number of occurrences of particular entries, I almost always also need to know what is the most occurring entry (or the top N occurring entries). You can easily imagine the impact for much larger jobs (see the reference below for real numbers). We will create a counter group called WordsNature that will count how many unique tokens there is in all, how many unique tokens starts with a digit and how many unique tokens starts with a letter.
The first ranked result returned by my prototype gives me now a different result: still another article from engadget (see picture below), but that time with a much more in depth review of the phone with more videos including a very funny comparison between the android, iphone and nexus one. Even if duplicate short URLs happen all the time, going with OFF gives a good approximation of which are the leading results. I did wrote a minimal amount of unit tests since I cannot code without it and I did use the google-collections library for the same reason :). The mechanism sending specific key-value pairs to specific reducers is called partitioning (the key-value pairs space is partitioned among the reducers). Suppose that your job’s input is a (huge) set of tokens and their number of occurrences (for instance the output of the canonical word count hadoop example) and that you want to sort them by number of occurrences. The map also shows an ideal base location which is very useful when starting.However if you wish to build your own place in a custom spot there are a few things that you will have to consider before spending all those hours of labor and precious resources on a giant main base camp only to find out that there is no food in a range of 5 kilometers.

No these are creatures that dynamically adapt to their surroundings and their threats; You!Normally during the daytime you will see them patrolling through the woods and around the shorelines.
As far as I know nothing happens once you have found all the 130 passengers.Now for a little bit of lore, the aircraft that you were on was an aircraft from the carrier Lufthansa.
That’s just the intuition (see the references at the end of the post for more details).
The first days before and after that the movie were released, all you got is links to see the trailer or even the movie. Unless a problem with Twitter, putting OFF should provide you the results within few seconds. These type of situations can be extremely dangerous seeing as you have little gear and combat experience.
And then all you have to do is press the right mouse button on the ingredients, and when you're done press the magic gear that will appear and boom! To see how to write an universal URL expander in JAVA that would work for the 90 + existing URL shortening services check the post that is referenced by the two short URLs above. Is the player attacking other mutants?Especially the last 3 can make a huge difference in most scenarios. In both methods circling around your enemies to avoid hits and keeping them in a constant state of receiving damage making them unable to move is the best course of action.1.
On the plane was probably a tennis team seeing as there are large amounts of tennis balls to be found as well as strangely mangled up bodies with tennis rackets at the mutant base camp, at this point of time it is unclear if the player himself is a tennis player too.
For instance my old basecamp was built up against the cliff north of the sailboat (the sailboat now spawns in random locations), however I would not reccomend this if you are just getting started.Also if you are afraid of losing track of your location you can now build landmarks in the game, doing this will create a flag icon (comparable to the home icon) at the locations you've built it.
When it comes to the entrance there are 2 side entrances of which one is rigged with a trap and the other is only accessible by me. But there isn't just advanced combat mechanics and besides their extremely high health and high damage there are other intresting things about the mutants.For instance if a group attacks you and you manage to kill or severely injure one of them, they will try and drag them away from the battlefield and to safety. The first method of dealing with this is to target the group or tribe leader and focus on killing him. The forest itself is probably modeled after north american forestry when it comes to the high trees, plants, mountain range etc. Also there is an infinite food supply inside the base seeing as it was possible to create a secure and mutant proof fishing dock.So to line it up I am going to give a summary of the good and bad aspects of my base.
They also mourn their dead using effigies which they also use to try and communicate with you. If you do this there is a high chance that the rest of the group will flee but be wary, they will come back with reinforcements so you better run!2. Bombs Needed resources Coins Booze Circuit Board 3. There are multiple campsites, it is not known if they were left behind by the survivors of the crash or not. Also if you see a mutant running after you have killed one of them he is going to go and get reinforcements!!! The second but harder method is to simply kill everyone, the advantage that this strategy has is that there will be no chance of reinforcements being called or the others becoming aware of your presence.2.
Stone Axe Needed resources Stick Rock Right now these are the only 3 items that you are able to craft inside the forest, however more craftable objects are planned and being worked on at the moment. Sometimes you can see proof of survivors that try to defend themselves in the form of for instance a spear jammed into a sleeping bag. Also inside the caves you can find some older type of boxes (perhaps wo2 erra?) which may indicate that there were survivors beside the mutants on the islands before you. Starving mutantsThese mutants have been seperated from the main tribe for unkown reasons and are identified by their blood covered bodies.
These types of fights are quite easy seeing as you have molotovs, all you have to do is get close and throw them (not too close though!). There is also a little mental game going on while you are playing the forest seeing as you are the one that literally just flew into their land, started using their resources and killing their people which begs the question of who really is the savage? While they are much more aggressive compared to normal mutants they are much weaker and are quite easy to kill.
One warning when it comes to molotovs though is that if you still have a molotov in your hand and you try to grab something out of your backpack the molotov will drop and set you on fire. Another option is to snipe them with your bow (if you have one), this can be very effective and you can drop them from quite a distance. Just keep in mind that it takes about 3-4 arrows to kill a regular mutant so have a reasonable ammo supply.3. They react to situations less predictably then starving mutants and they are also harder to kill.
The regular mutantThey look the most human of all the cannibals and are usually found in parties together with a leader class mutant.
Anyway you need to take care of them rather soon then later, this also is a fight where running is not always possible adding to the risk.
They are easy to kill with traps and fire but they are still able to absorb a lot of melee damage and hand out some pretty hard punches.2. The arsonist mutantThis mutant is almost the same as the regular mutant, everything is exactly the same except for one very specific thing.The arsonist mutant is currently the only type of mutant that uses any type of sophisticated weapon (other mutants have been seen with some type of skull mace) that deals additional damage. The first and definatively the best one is to try and use the knockback effect of your axe to knock them in either a firepit or a standing fire.
The nice thing about this strategy is that once you have knocked them in the fire, you can forget about them. When hit by this type of cannibal you will be on fire which can be extremely dangerous as well as make you unable to move normally. They can't do anything because they are on fire and it is 100% guaranteed that they will die because of this.2. If you see this type of mutant and do not have a weapon you should proceed very carefully.3. The second and way more terrible method is to just like in a starter fight scenario method two, just kill everyone.
The pack leader mutant While still looking human just like the regular mutant these type of mutants decorate themselves by wearing an effigie. Seeing as here you are further in the game and you may or may not have armor it can help out a bit but close quarter combat is still very risky. They are usually the leaders of patrols an raiding parties which in turn makes them important people inside the tribe.
The tribe leader mutantThere is not a lot known about this specific mutant, he stands out from the other mutants because he is wearing many human arms in a circular pattern on his back.
Seeing as this is more frightening than the pack leaders type of effigie and the fact that he wears not one, but two skulls on his head makes you think that he would also be a higher ranked mutant than the pack leader.
Currently it is unconfirmed if this mutant deals any additional of damage or if he is harder to kill (I did not test this) but what we do know is that he is just like the pack leader capable of leading other mutant groups.5.
They are able to effectively destroy defensive walls and in the current version they are only killable with bombs.
The weird female spider thing So this is probably the mutant with the highest wtf rating yet. Featuring what seems to be 2 or more female bodies merged together with some weird fleece covering it.

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