About

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18 thoughts on “About

  1. hi. im doing research regarding forecasting using neural network. i read the data should be separated into training and testing data. my question is how to separate the data into testing and training?im totally lost here.where can i refer if u have suggestion on good reference. tq

      • I’m not an expert, but I don’t believe there are any explicit rules defining the best method for splitting the data… the important part is that you always evaluate the model using an independent (testing) dataset. Neural networks are really good at overfitting and modelling noise in the data that might be a specific characteristic of the training dataset. I’ve often split the data into groups using an arbitrary ratio, e.g., 3:1, such that 75% of the observations are the training and 25% are the testing. The split has to be random so that the training/testing datasets are similar. There are many ways to approach the issue… a quick literature search should provide some clues.

  2. Hi. I saw you are developing a package for minnig data from Strava with R. I’m also working on functions to get data from the Strava API. I have no experience making libraries or packages, and I’m just a beginner programming in R and working with APIs. So there might be way better ways to acomplish the tasks, but so far I have implemented functions for authentication and getting most of the resources from the API with the httr library. If you want to check it out: https://github.com/ptdrow/Rtrava

    • Hi Pedro, I knew it was only a matter of time before someone else started working on this! This helps immensely as I haven’t had any success accessing the API… My plan was to develop two sets of functions, one that scrapes the data w/o using authentication and a second that accesses the API. I will try some of your functions to see if I can access the API w/ my token. Maybe we can co-author a package if this works out??

      • That would be great. My plan is to study mobility patterns of urban cyclists in the cities and maybe creating advanced features for Strava users. I’m learning R and data science through the John Hopkins University’s Data Science specialization on Coursera, and so far it has been a great introduction to R and getting data from the web (including APIs). Please send me and email with your feedback of the code and info on how we could collaborate if you want.
        PS.: I just updated the code to add comments on the input variables for the functions

  3. Hey, I’m currently trying to use my neural network. I have trained it and tested and now I am using a readline function to ask the user for input. My question is how would I use these variables or answers as an input for the neural network and see what the neural network comes up with. I’m currently using the neuralnet package. Could you tell me how I would do this. Thanks

    • Hi Nick,

      This seems like a strange way to get predictions from a neural network. I don’t think there is a predict method for neuralnet models. Try using the nnet (nnet function) or the RSNNS (mlp function) packages. Then you can use the predict method for the model, i.e., predict(my_model, new = newdata), where my_model is your trained/tested model and newdata is a dataframe of user supplied explanatory variables to use for prediction. Hope that helps.

      -Marcus

      • Here is my current code.

        ##nueral network
        setwd(“C:/Users/Nick/Desktop/canmam”)
        set.seed(1234)
        library(“neuralnet”)
        library(“nnet”)
        library(“MASS”)

        dataset <- read.csv("C:/Users/Nick/Desktop/canmam/mamm.csv")
        trainset <- dataset[1:150, ]
        testset <- dataset[151:200, ]
        creditnet <- neuralnet(status ~ x1 + x2 + x3 + x4 + x5, trainset, hidden = 8, lifesign = "minimal", linear.output = FALSE, threshold = 0.1)
        temp_test <- subset(testset, select = c("x1","x2","x3","x4","x5"))
        creditnet.results <- compute(creditnet, temp_test)
        results <- data.frame(actual = testset$status, prediction = creditnet.results$net.result)
        results[1:150, ]
        results$prediction <- round(results$prediction)
        results[1:49, ]
        x1 <- readline("enter the Bi-Radr: ")
        x2 <- readline("enter the age: ")
        x3 <- readline("enter the shape: ")
        x4 <- readline("enter the margin: ")
        x5 <- readline("enter the density: ")
        df1 = data.frame(x1, x2, x3, x4, x5)
        table[df] <- factor(table[[df1]])
        df <- model.matrix( ~ x1 + x2 + x3 + x4 + x5, data = df1)
        #df <- factor(df)
        user.results <- compute(creditnet, new = df)
        #prdct <- predict(creditnet, df)
        #x <- data.matrix(a)

        I tried your method but I keep getting an error. Could you help me out?? I really need to finish this program.

  4. I have a backpropagation algorithm of my own. The main result is a weight matrix. If I call your function with these matrix in the first parameter, what would I obtain?

    • Hi Rafael,

      Most of the functions in the NeuralNetTools package have methods for numeric inputs (e.g., a vector of weight values from a model). The lekprofile function is the only one that does not since it requires predictions from a fitted model in R. See the examples in the help files for the plotnet and garson functions for using numeric functions. The input weights must be a numeric vector with a specific order. For example…

      wts_in <- c(13.12, 1.49, 0.16, -0.11, -0.19, -0.16, 0.56, -0.52, 0.81)
      struct <- c(2, 2, 1) #two inputs, two hidden, one output
      
      plotnet(wts_in, struct = struct)
      

      The weight vector shows the weights for each hidden node in sequence, starting with the bias input for each node, then the weights for each output node in sequence, starting with the bias input for each output node. There is an example in the blog post here that illustrates the order.

      Hope that helps.

      -Marcus

  5. Hi “beckmw”….i want to mention your post on average dissertation length in my ABD Survival Guide newsletter as a fun statistic . How would you like me to list your name and any other identifying characteristics? Is it Marcus Beck at U Michigan? Are you a doctoral student at this time? Please email me your answer. Thanks! Gayle

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