I'm doing a project to detect (classify) human activities using a ARM cortex-m0 microcontroller (Freedom - KL25Z) with an accelerometer. I intend to predict the activity of the user using machine learning.
The problem is, the cortex-m0 is not capable of processing training or predicting algorithms, so I would probably have to collect the data, train it in my computer and then embed it somehow, which I don't really know how to do it.
I saw some post in the internet saying that you can generate a matrix of weights and embed it in a microcontroller, so it would be a straightforward function to predict something ,based on the data you providing for this function. Would it be the right way of doing ?
Anyway my question is, how could I embedded a classification algorithm in a microcontroller?
I hope you guys can help me and give some guidance, I'm kind of lost here.
Thank you in advance.
I've been thinking about doing this myself to solve a problem that I've had a hard time developing a heuristic for by hand.
You're going to have to write your own machine-learning methods, because there aren't any machine learning libraries out there suitable for low-end MCUs, as far as I know.
Depending on how hard the problem is, it may still be possible to develop and train a simple machine learning algorithm that performs well on a low-end MCU. After-all, some of the older/simpler machine learning methods were used with satisfactory results on hardware with similar constraints.
Very generally, this is how I'd go about doing this:
Get the (labelled) data to a PC (through UART, SD-card, or whatever means you have available).
Experiment with the data and a machine learning toolkit (scikit-learn, weka, vowpal wabbit, etc). Make sure an off-the-shelf method is able to produce satisfactory results before moving forward.
Experiment with feature engineering and selection. Try to get the smallest feature set possible to save resources.
Write your own machine learning method that will eventually be used on the embedded system. I would probably choose perceptrons or decision trees, because these don't necessarily need a lot of memory. Since you have no FPU, I'd only use integers and fixed-point arithmetic.
Do the normal training procedure. I.e. use cross-validation to find the best tuning parameters, integer bit-widths, radix positions, etc.
Run the final trained predictor on the held-out testing set.
If the performance of your trained predictor was satisfactory on the testing set, move your relevant code (the code that calculates the predictions) and the model you trained (e.g. weights) to the MCU. The model/weights will not change, so they can be stored in flash (e.g. as a const array).
I think you may be limited by your hardware. You may want to get something a little more powerful. For your project you've chosen the M-series processor from ARM. This is the simplest platform that they offer, the architecture doesn't lend itself to the kind of processing you're trying to do. ARM has three basic classifications as follows:
M - microcontroller
R - real-time
A - applications
You want to get something that has strong hardware support for these complex calculations. You're starting point should be an A-series for this. If you need to do floating point arithmetic, you'll definitely need to start with the A-series and probably get one with NEON-FPU.
TI's Discovery series is a nice place to start, or maybe just use the Raspberry Pi (at least for the development part)?
However, if you insist on using the M0 I think you might be able to pull it off using something lightweight like ROS-C. I know there are packages with ROS that can do it, even though its mainly for robotics you may be able to adapt it to what you're doing.
Dependency Free ROS
Neural Networks and Machine Learning with ROS
Background: I am trying to find a list of floating point parameters for a low level controller that will lead to balance of a robot while it is walking.
Question: Can anybody recommend me any local search algorithms that will perform well for the domain I just described? The main criteria for me is the speed of convergence to the right solution.
Any help will be greatly appreciated!
P.S. Also, I conducted some research and found out that "Evolutianry
Strategy" algorithms are a good fit for continuous state space. However, I am not entirely sure, if they will fit well my particular problem.
More info: I am trying to optimize 8 parameters (although it is possible for me to reduce the number of parameters to 4). I do have a simulator and a criteria for me is speed in number of trials because simulation resets are costly (take 10-15 seconds on average).
One of the best local search algorithms for low number of dimensions (up to about 10 or so) is the Nelder-Mead simplex method. By the way, it is used as the default optimizer in MATLAB's fminsearch function. I personally used this method for finding parameters of some textbook 2nd or 3rd degree dynamic system (though very simple one).
Other option are the already mentioned evolutionary strategies. Currently the best one is the Covariance Matrix Adaption ES, or CMA-ES. There are variations to this algorithm, e.g. BI-POP CMA-ES etc. that are probably better than the vanilla version.
You just have to try what works best for you.
In addition to evolutionary algorithm, I recommend you also check reinforcement learning.
The right method depends a lot on the details of your problem. How many parameters? Do you have a simulator? Do you work in simulation only, or also with real hardware? Speed is in number of trials, or CPU time?
I want to program a robot which will sense obstacles and learn whether to cross over them or bypass around them.
Since my project, must be realized in week and a half period, I must use an online learning algorithm (GA or such would take a lot time to test because robot needs to try to cross over the obstacle in order to determine is it possible to cross).
I'm really new to online learning so I don't really know which online learning algorithm to use.
It would be a great help if someone could recommend me a few algorithms that would be the best for my problem and some link with examples wouldn't hurt.
Thanks!
I think you could start with A* (A-Star)
It's simple and robust, and widely used.
There are some nice tutorials on the web like this http://www.raywenderlich.com/4946/introduction-to-a-pathfinding
Online algorithm is just the one that can collect new data and update a model incrementally without re-training with full dataset (i.e. it may be used in online service that works all the time). What you are probably looking for is reinforcement learning.
RL itself is not a method, but rather general approach to the problem. Many concrete methods may be used with it. Neural networks have been proved to do well in this field (useful course). See, for example, this paper.
However, to create real robot being able to bypass obstacles you will need much then just knowing about neural networks. You will need to set up sensors carefully, preprocess data from them, work out your model and collect a dataset. Not sure it's possible to even learn it all in a week and a half.
I am working on testing several Machine Learning algorithm implementations, checking whether they can work as efficient as described in the papers and making sure they could offer a great power to our statistic NLP (Natural Language Processing) platform.
Could u guys show me some methods for testing an algorithm implementation?
1)What aspects?
2)How?
3)Do I have to follow some basic steps?
4)Do I have to consider diversity specific situations when using different programming languages?
5)Do I have to understand the algorithm? I mean, does it offer any help if I really know what the algorithm is and how it works?
Basically, we r using C or C++ to implement the algorithm and our working env is Linux/Unix. Our testing methods only focus on black box testing and testing input/output of functions. I am eager to improve them but I dont have any better idea now...
Great Thx!! LOL
For many machine learning and statistical classification tasks, the standard metric for measuring quality is Precision and Recall. Most published algorithms will make some kind of claim about these metrics, or you could implement them and run these tests yourself. This should provide a good indicative measure of the quality you can expect.
When you talk about efficiency of an algorithm, this is usually some statement about the time or space performance of an algorithm in terms of the size or complexity of its input (often expressed in Big O notation). Most published algorithms will report an upper bound on the time and space characteristics of the algorithm. You can use that as a comparative indicator, although you need to know a little bit about computational complexity in order to make sure you're not fooling yourself. You could also possibly derive this information from manual inspection of program code, but it's probably not necessary, because this information is almost always published along with the algorithm.
Finally, understanding the algorithm is always a good idea. It makes it easier to know what you need to do as a user of that algorithm to ensure you're getting the best possible results (and indeed to know whether the results you are getting are sensible or not), and it will allow you to apply quality measures such as those I suggested in the first paragraph of this answer.
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I am wanting some expert guidance here on what the best approach is for me to solve a problem. I have investigated some machine learning, neural networks, and stuff like that. I've investigated weka, some sort of baesian solution.. R.. several different things. I'm not sure how to really proceed, though. Here's my problem.
I have, or will have, a large collection of events.. eventually around 100,000 or so. Each event consists of several (30-50) independent variables, and 1 dependent variable that I care about. Some independent variables are more important than others in determining the dependent variable's value. And, these events are time relevant. Things that occur today are more important than events that occurred 10 years ago.
I'd like to be able to feed some sort of learning engine an event, and have it predict the dependent variable. Then, knowing the real answer for the dependent variable for this event (and all the events that have come along before), I'd like for that to train subsequent guesses.
Once I have an idea of what programming direction to go, I can do the research and figure out how to turn my idea into code. But my background is in parallel programming and not stuff like this, so I'd love to have some suggestions and guidance on this.
Thanks!
Edit: Here's a bit more detail about the problem that I'm trying to solve: It's a pricing problem. Let's say that I'm wanting to predict prices for a random comic book. Price is the only thing I care about. But there are lots of independent variables one could come up with. Is it a Superman comic, or a Hello Kitty comic. How old is it? What's the condition? etc etc. After training for a while, I want to be able to give it information about a comic book I might be considering, and have it give me a reasonable expected value for the comic book. OK. So comic books might be a bogus example. But you get the general idea. So far, from the answers, I'm doing some research on Support vector machines and Naive Bayes. Thanks for all of your help so far.
Sounds like you're a candidate for Support Vector Machines.
Go get libsvm. Read "A practical guide to SVM classification", which they distribute, and is short.
Basically, you're going to take your events, and format them like:
dv1 1:iv1_1 2:iv1_2 3:iv1_3 4:iv1_4 ...
dv2 1:iv2_1 2:iv2_2 3:iv2_3 4:iv2_4 ...
run it through their svm-scale utility, and then use their grid.py script to search for appropriate kernel parameters. The learning algorithm should be able to figure out differing importance of variables, though you might be able to weight things as well. If you think time will be useful, just add time as another independent variable (feature) for the training algorithm to use.
If libsvm can't quite get the accuracy you'd like, consider stepping up to SVMlight. Only ever so slightly harder to deal with, and a lot more options.
Bishop's Pattern Recognition and Machine Learning is probably the first textbook to look to for details on what libsvm and SVMlight are actually doing with your data.
If you have some classified data - a bunch of sample problems paired with their correct answers -, start by training some simple algorithms like K-Nearest-Neighbor and Perceptron and seeing if anything meaningful comes out of it. Don't bother trying to solve it optimally until you know if you can solve it simply or at all.
If you don't have any classified data, or not very much of it, start researching unsupervised learning algorithms.
It sounds like any kind of classifier should work for this problem: find the best class (your dependent variable) for an instance (your events). A simple starting point might be Naive Bayes classification.
This is definitely a machine learning problem. Weka is an excellent choice if you know Java and want a nice GPL lib where all you have to do is select the classifier and write some glue. R is probably not going to cut it for that many instances (events, as you termed it) because it's pretty slow. Furthermore, in R you still need to find or write machine learning libs, though this should be easy given that it's a statistical language.
If you believe that your features (independent variables) are conditionally independent (meaning, independent given the dependent variable), naive Bayes is the perfect classifier, as it is fast, interpretable, accurate and easy to implement. However, with 100,000 instances and only 30-50 features you can likely implement a fairly complex classification scheme that captures a lot of the dependency structure in your data. Your best bet would probably be a support vector machine (SMO in Weka) or a random forest (Yes, it's a silly name, but it helped random forest catch on.) If you want the advantage of easy interpretability of your classifier even at the expense of some accuracy, maybe a straight up J48 decision tree would work. I'd recommend against neural nets, as they're really slow and don't usually work any better in practice than SVMs and random forest.
The book Programming Collective Intelligence has a worked example with source code of a price predictor for laptops which would probably be a good starting point for you.
SVM's are often the best classifier available. It all depends on your problem and your data. For some problems other machine learning algorithms might be better. I have seen problems that neural networks (specifically recurrent neural networks) were better at solving. There is no right answer to this question since it is highly situationally dependent but I agree with dsimcha and Jay that SVM's are the right place to start.
I believe your problem is a regression problem, not a classification problem. The main difference: In classification we are trying to learn the value of a discrete variable, while in regression we are trying to learn the value of a continuous one. The techniques involved may be similar, but the details are different. Linear Regression is what most people try first. There are lots of other regression techniques, if linear regression doesn't do the trick.
You mentioned that you have 30-50 independent variables, and some are more important that the rest. So, assuming that you have historical data (or what we called a training set), you can use PCA (Principal Componenta Analysis) or other dimensionality reduction methods to reduce the number of independent variables. This step is of course optional. Depending on situations, you may get better results by keeping every variables, but add a weight to each one of them based on relevant they are. Here, PCA can help you to compute how "relevant" the variable is.
You also mentioned that events that are occured more recently should be more important. If that's the case, you can weight the recent event higher and the older event lower. Note that the importance of the event doesn't have to grow linearly accoding to time. It may makes more sense if it grow exponentially, so you can play with the numbers here. Or, if you are not lacking of training data, perhaps you can considered dropping off data that are too old.
Like Yuval F said, this does look more like a regression problem rather than a classification problem. Therefore, you can try SVR (Support Vector Regression), which is regression version of SVM (Support Vector Machine).
some other stuff you can try are:
Play around with how you scale the value range of your independent variables. Say, usually [-1...1] or [0...1]. But you can try other ranges to see if they help. Sometimes they do. Most of the time they don't.
If you suspect that there are "hidden" feature vector with a lower dimension, say N << 30 and it's non-linear in nature, you will need non-linear dimensionality reduction. You can read up on kernel PCA or more recently, manifold sculpting.
What you described is a classic classification problem. And in my opinion, why code fresh algorithms at all when you have a tool like Weka around. If I were you, I would run through a list of supervised learning algorithms (I don't completely understand whey people are suggesting unsupervised learning first when this is so clearly a classification problem) using 10-fold (or k-fold) cross validation, which is the default in Weka if I remember, and see what results you get! I would try:
-Neural Nets
-SVMs
-Decision Trees (this one worked really well for me when I was doing a similar problem)
-Boosting with Decision trees/stumps
-Anything else!
Weka makes things so easy and you really can get some useful information. I just took a machine learning class and I did exactly what you're trying to do with the algorithms above, so I know where you're at. For me the boosting with decision stumps worked amazingly well. (BTW, boosting is actually a meta-algorithm and can be applied to most supervised learning algs to usually enhance their results.)
A nice thing aobut using Decision Trees (if you use the ID3 or similar variety) is that it chooses the attributes to split on in order of how well they differientiate the data - in other words, which attributes determine the classification the quickest basically. So you can check out the tree after running the algorithm and see what attribute of a comic book most strongly determines the price - it should be the root of the tree.
Edit: I think Yuval is right, I wasn't paying attention to the problem of discretizing your price value for the classification. However, I don't know if regression is available in Weka, and you can still pretty easily apply classification techniques to this problem. You need to make classes of price values, as in, a number of ranges of prices for the comics, so that you can have a discrete number (like 1 through 10) that represents the price of the comic. Then you can easily run classification it.