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  • Reinforcement learning with neural networks

    - by Betamoo
    I am working on a project with RL & NN I need to determine the action vector structure which will be fed to a neural network.. I have 3 different actions (A & B & Nothing) each with different powers (e.g A100 A50 B100 B50) I wonder what is the best way to feed these actions to a NN in order to yield best results? 1- feed A/B to input 1, while action power 100/50/Nothing to input 2 2- feed A100/A50/Nothing to input 1, while B100/B50/Nothing to input 2 3- feed A100/A50 to input 1, while B100/B50 to input 2, while Nothing flag to input 3 4- Also to feed 100 & 50 or normalize them to 2 & 1 ? I need reasons why to choose one method Any suggestions are recommended Thanks

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  • Genetic Programming Online Learning

    - by Lirik
    Has anybody seen a GP implemented with online learning rather than the standard offline learning? I've done some stuff with genetic programs and I simply can't figure out what would be a good way to make the learning process online. Please let me know if you have any ideas, seen any implementations, or have any references that I can look at.

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  • How do you unit test a method containing a LINQ expression?

    - by Phil.Wheeler
    I'm struggling to get my head around how to accommodate a mocked method that only accepts a Linq expression as its argument. Specifically, the repository I'm using has a First() method that looks like this: public T First(Expression<Func<T, bool>> expression) { return All().Where(expression).FirstOrDefault(); } The difficulty I'm encountering is with my MSpec tests, where I'm (probably incorrectly) trying to mock that call: public abstract class with_userprofile_repository { protected static Mock<IRepository<UserProfile>> repository; Establish context = () => { repository = new Mock<IRepository<UserProfile>>(); repository.Setup<UserProfile>(x => x.First(up => up.OpenID == @"http://testuser.myopenid.com")).Returns(GetDummyUser()); }; protected static UserProfile GetDummyUser() { UserProfile p = new UserProfile(); p.OpenID = @"http://testuser.myopenid.com"; p.FirstName = "Joe"; p.LastLogin = DateTime.Now.Date.AddDays(-7); p.LastName = "Bloggs"; p.Email = "[email protected]"; return p; } } I run into trouble because it's not enjoying the Linq expression: System.NotSupportedException: Expression up = (up.OpenID = "http://testuser.myopenid.com") is not supported. So how does one test these sorts of scenarios?

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  • VS2010 Developer Image

    - by David Ward
    I am about to create a new developer PC image for developing WPF applications using VS2010, WCF, SQL2008 and SharePoint2010. What OS should I opt for? Windows 7? Windows Server 2008 R2? I'd have thought Windows 7 to make sure that I have a similar experience during development as an end user, however I can't install SharePoint on a client OS and so thought about Windows Server 2008 R2 to help with the SharePoint development process. Thoughts?

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  • computing z-scores for 2D matrices in scipy/numpy in Python

    - by user248237
    How can I compute the z-score for matrices in Python? Suppose I have the array: a = array([[ 1, 2, 3], [ 30, 35, 36], [2000, 6000, 8000]]) and I want to compute the z-score for each row. The solution I came up with is: array([zs(item) for item in a]) where zs is in scipy.stats.stats. Is there a better built-in vectorized way to do this? Also, is it always good to z-score numbers before using hierarchical clustering with euclidean or seuclidean distance? Can anyone discuss the relative advantages/disadvantages? thanks.

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  • using Multi Probe LSH with LSHKIT

    - by Yijinsei
    Hi Guys, I have read through the source code for mplsh, but I still unsure on how to use the indexes generated by lshkit to speed up the process in comparing feature vector in Euclidean Distance. Do you guys have any experience regarding this?

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  • plotting results of hierarchical clustering ontop of a matrix of data in python

    - by user248237
    How can I plot a dendrogram right on top of a matrix of values, reordered appropriately to reflect the clustering, in Python? An example is in the bottom of the following figure: http://www.coriell.org/images/microarray.gif I use scipy.cluster.dendrogram to make my dendrogram and perform hierarchical clustering on a matrix of data. How can I then plot the data as a matrix where the rows have been reordered to reflect a clustering induced by the cutting the dendrogram at a particular threshold, and have the dendrogram plotted alongside the matrix? I know how to plot the dendrogram in scipy, but not how to plot the intensity matrix of data with the right scale bar next to it. Any help on this would be greatly appreciated.

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  • Naive Bayesian for Topic detection using "Bag of Words" approach

    - by AlgoMan
    I am trying to implement a naive bayseian approach to find the topic of a given document or stream of words. Is there are Naive Bayesian approach that i might be able to look up for this ? Also, i am trying to improve my dictionary as i go along. Initially, i have a bunch of words that map to a topics (hard-coded). Depending on the occurrence of the words other than the ones that are already mapped. And depending on the occurrences of these words i want to add them to the mappings, hence improving and learning about new words that map to topic. And also changing the probabilities of words. How should i go about doing this ? Is my approach the right one ? Which programming language would be best suited for the implementation ?

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  • hierarchical clustering with gene expression matrix in python

    - by user248237
    how can I do a hierarchical clustering (in this case for gene expression data) in Python in a way that shows the matrix of gene expression values along with the dendrogram? What I mean is like the example here: http://www.mathworks.cn/access/helpdesk/help/toolbox/bioinfo/ug/a1060813239b1.html shown after bullet point 6 (Figure 1), where the dendrogram is plotted to the left of the gene expression matrix, where the rows have been reordered to reflect the clustering. How can I do this in Python using numpy/scipy or other tools? Also, is it computationally practical to do this with a matrix of about 11,000 genes, using euclidean distance as a metric? thanks.

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  • How to perform FST (Finite State Transducer) composition

    - by Tasbeer
    Consider the following FSTs : T1 0 1 a : b 0 2 b : b 2 3 b : b 0 0 a : a 1 3 b : a T2 0 1 b : a 1 2 b : a 1 1 a : d 1 2 a : c How do I perform the composition operation on these two FSTs (i.e. T1 o T2) I saw some algorithms but couldn't understand much. If anyone could explain it in a easy way it would be a major help. Please note that this is NOT a homework. The example is taken from the lecture slides where the solution is given but I couldn't figure out how to get to it.

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  • Reinforcement learning And POMDP

    - by Betamoo
    I am trying to use Multi-Layer NN to implement probability function in Partially Observable Markov Process.. I thought inputs to the NN would be: current state, selected action, result state; The output is a probability in [0,1] (prob. that performing selected action on current state will lead to result state) In training, I fed the inputs stated before, into the NN, and I taught it the output=1.0 for each case that already occurred. The problem : For nearly all test case the output probability is near 0.95.. no output was under 0.9 ! Even for nearly impossible results, it gave that high prob. PS:I think this is because I taught it happened cases only, but not un-happened ones.. But I can not at each step in the episode teach it the output=0.0 for every un-happened action! Any suggestions how to over come this problem? Or may be another way to use NN or to implement prob function? Thanks

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  • How to compute the probability of a multi-class prediction using libsvm?

    - by Cuga
    I'm using libsvm and the documentation leads me to believe that there's a way to output the believed probability of an output classification's accuracy. Is this so? And if so, can anyone provide a clear example of how to do it in code? Currently, I'm using the Java libraries in the following manner SvmModel model = Svm.svm_train(problem, parameters); SvmNode x[] = getAnArrayOfSvmNodesForProblem(); double predictedValue = Svm.svm_predict(model, x);

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  • Neural Network with softmax activation

    - by Cambium
    This is more or less a research project for a course, and my understanding of NN is very/fairly limited, so please be patient :) ============== I am currently in the process of building a neural network that attempts to examine an input dataset and output the probability/likelihood of each classification (there are 5 different classifications). Naturally, the sum of all output nodes should add up to 1. Currently, I have two layers, and I set the hidden layer to contain 10 nodes. I came up with two different types of implementations 1) Logistic sigmoid for hidden layer activation, softmax for output activation 2) Softmax for both hidden layer and output activation I am using gradient descent to find local maximums in order to adjust the hidden nodes' weights and the output nodes' weights. I am certain in that I have this correct for sigmoid. I am less certain with softmax (or whether I can use gradient descent at all), after a bit of researching, I couldn't find the answer and decided to compute the derivative myself and obtained softmax'(x) = softmax(x) - softmax(x)^2 (this returns an column vector of size n). I have also looked into the MATLAB NN toolkit, the derivative of softmax provided by the toolkit returned a square matrix of size nxn, where the diagonal coincides with the softmax'(x) that I calculated by hand; and I am not sure how to interpret the output matrix. I ran each implementation with a learning rate of 0.001 and 1000 iterations of back propagation. However, my NN returns 0.2 (an even distribution) for all five output nodes, for any subset of the input dataset. My conclusions: o I am fairly certain that my gradient of descent is incorrectly done, but I have no idea how to fix this. o Perhaps I am not using enough hidden nodes o Perhaps I should increase the number of layers Any help would be greatly appreciated! The dataset I am working with can be found here (processed Cleveland): http://archive.ics.uci.edu/ml/datasets/Heart+Disease

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  • Neural Network: Handling unavailable inputs (missing or incomplete data)

    - by Mike
    Hopefully the last NN question you'll get from me this weekend, but here goes :) Is there a way to handle an input that you "don't always know"... so it doesn't affect the weightings somehow? Soo... if I ask someone if they are male or female and they would not like to answer, is there a way to disregard this input? Perhaps by placing it squarely in the centre? (assuming 1,0 inputs at 0.5?) Thanks

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  • How to create a container that holds different types of function pointers in C++?

    - by Alex
    I'm doing a linear genetic programming project, where programs are bred and evolved by means of natural evolution mechanisms. Their "DNA" is basically a container (I've used arrays and vectors successfully) which contain function pointers to a set of functions available. Now, for simple problems, such as mathematical problems, I could use one type-defined function pointer which could point to functions that all return a double and all take as parameters two doubles. Unfortunately this is not very practical. I need to be able to have a container which can have different sorts of function pointers, say a function pointer to a function which takes no arguments, or a function which takes one argument, or a function which returns something, etc (you get the idea)... Is there any way to do this using any kind of container ? Could I do that using a container which contains polymorphic classes, which in their turn have various kinds of function pointers? I hope someone can direct me towards a solution because redesigning everything I've done so far is going to be painful.

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  • Update Rule in Temporal difference

    - by Betamoo
    The update rule TD(0) Q-Learning: Q(t-1) = (1-alpha) * Q(t-1) + (alpha) * (Reward(t-1) + gamma* Max( Q(t) ) ) Then take either the current best action (to optimize) or a random action (to explorer) Where MaxNextQ is the maximum Q that can be got in the next state... But in TD(1) I think update rule will be: Q(t-2) = (1-alpha) * Q(t-2) + (alpha) * (Reward(t-2) + gamma * Reward(t-1) + gamma * gamma * Max( Q(t) ) ) My question: The term gamma * Reward(t-1) means that I will always take my best action at t-1 .. which I think will prevent exploring.. Can someone give me a hint? Thanks

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  • Using CallExternalMethodActivity/HandleExternalEventActivity in StateMachine

    - by AngrySpade
    I'm attempting to make a StateMachine execute some database action between states. So I have a "starting" state that uses CallExternalMethodActivity to call a "BeginExecuteNonQuery" function on an class decorated with ExternalDataExchangeAttribute. After that it uses a SetStateActivity to change to an "ending" state. The "ending" state uses a HandleExternalEventActivity to listen to a "EndExecuteNonQuery" event. I can step through the local service, into the "BeginExecuteNonQuery" function. The problem is that the "EndExecuteNonQuery" is null. public class FailoverWorkflowController : IFailoverWorkflowController { private readonly WorkflowRuntime workflowRuntime; private readonly FailoverWorkflowControlService failoverWorkflowControlService; private readonly DatabaseControlService databaseControlService; public FailoverWorkflowController() { workflowRuntime = new WorkflowRuntime(); workflowRuntime.WorkflowCompleted += workflowRuntime_WorkflowCompleted; workflowRuntime.WorkflowTerminated += workflowRuntime_WorkflowTerminated; ExternalDataExchangeService dataExchangeService = new ExternalDataExchangeService(); workflowRuntime.AddService(dataExchangeService); databaseControlService = new DatabaseControlService(); workflowRuntime.AddService(databaseControlService); workflowRuntime.StartRuntime(); } ... } ... public void BeginExecuteNonQuery(string command) { Guid workflowInstanceID = WorkflowEnvironment.WorkflowInstanceId; ThreadPool.QueueUserWorkItem(delegate(object state) { try { int result = ExecuteNonQuery((string)state); EndExecuteNonQuery(null, new ExecuteNonQueryResultEventArgs(workflowInstanceID, result)); } catch (Exception exception) { EndExecuteNonQuery(null, new ExecuteNonQueryResultEventArgs(workflowInstanceID, exception)); } }, command); } What am I doing wrong with my implementation? -Stan

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  • Training sets for AdaBoost algorithm

    - by palau1
    How do you find the negative and positive training data sets of Haar features for the AdaBoost algorithm? So say you have a certain type of blob that you want to locate in an image and there are several of them in your entire array - how do you go about training it? I'd appreciate a nontechnical explanation as much as possible. I'm new to this. Thanks.

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  • Mahout Naive Bayes Classifier for Items

    - by Nimesh Parikh
    Team, I am working on a project where i need to classify Items into certain category. I have a single file as input; which contains target variable and space separated features. My training data will look like Category Name [Tab] DataString Plumbing [Tab] Pipe Tap Plastic Pipe PVC Pipe Cold Water Line Hot Water Line Tee outlet up Elbow turned up Elbow turned down Gate valve Globe valve Paint [Tab] Ivory Black Burnt Umber Caput Mortuum Violet Earth Red Yellow Ochre Titanium White Cadmium Yellow Light Cadmium Yellow Deep Cloths [Tab] Shirt T-Shirt Pent Jeans Tee Cargo Well, I have really big set of Category. I have couple of question here am i using correct data for Training? If no then what should i use? Once I train and Test my model, what is next step? How can i use output? Please help me with this Thanks, Nimesh

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  • hierarchical clustering on correlations in Python scipy/numpy?

    - by user248237
    How can I run hierarchical clustering on a correlation matrix in scipy/numpy? I have a matrix of 100 rows by 9 columns, and I'd like to hierarchically clustering by correlations of each entry across the 9 conditions. I'd like to use 1-pearson correlation as the distances for clustering. Assuming I have a numpy array "X" that contains the 100 x 9 matrix, how can I do this? I tried using hcluster, based on this example: Y=pdist(X, 'seuclidean') Z=linkage(Y, 'single') dendrogram(Z, color_threshold=0) however, pdist is not what I want since that's euclidean distance. Any ideas? thanks.

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