convergence

Out of memory using svmtrain in Matlab

由 跟風遠走 提交于 2019-12-22 21:14:11
问题 I have a set of data that I am trying to learn using SVM. For context, the data has a dimensionality of 35 and contains approximately 30'000 data-points. I have previously trained decision trees in Matlab with this dataset and it took approximately 20 seconds. Not being totally satisfied with the error rate, I decided to try SVM. I first tried svmtrain(X,Y) . After about 5 seconds, I get the following message: ??? Error using ==> svmtrain at 453 Error calculating the kernel function: Out of

Solving error message “step halving factor reduced below minimum in NLS step”: adjusting nlsTols not working

由 痞子三分冷 提交于 2019-12-22 17:46:50
问题 I am trying to fit a logistic growth curve to some data using the gnls function. Data: structure(list(Nest = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 16L, 10L, 4L, 5L, 7L, 12L, 4L, 6L, 20L, 8L, 14L, 16L, 3L, 9L, 15L, 19L, 6L, 7L, 17L, 18L, 12L, 13L, 10L, 20L, 5L, 8L, 11L, 16L, 6L, 12L, 1L, 2L, 4L, 6L, 9L, 18L, 21L, 16L, 3L, 20L), .Label = c("WTSN01", "WTSN02", "WTSN04", "WTSN05", "WTSN06", "WTSN07", "WTSN08", "WTSN09",

Strange convergence in simple Neural Network

由 送分小仙女□ 提交于 2019-12-20 05:44:38
问题 I've been struggling for some time with building a simplistic NN in Java. I've been working on and off on this project for a few months and I wanna finish it. My main issue is that I dunno how to implement backpropagation correctly (all sources use Python, math jargon, or explain the idea too briefly). Today I tried deducing the ideology by myself and the rule that I'm using is: the weight update = error * sigmoidDerivative(error) * weight itself; error = output - actual; (last layer) error =

Convergence criterion for (batch) SOM (Self-Organizing Map, aka “Kohonen Map”)?

由 南楼画角 提交于 2019-12-10 13:21:19
问题 I like to stop the execution when Batch SOM becomes converged. What error function can I use to determine the convergence? 回答1: When talking about convergence for SOMs, for a given map size (n x m), you want to know whether sufficient iterations of the algorithm have run to ensure the map is "Stable". This means, loosely speaking, do new inputs (observations) to the map get placed at the same neurons /codebook vectors if the map is retrained many times?(Ignoring the issue of the fact that the

R neuralnet does not converge within stepmax for time series

由 梦想的初衷 提交于 2019-12-08 05:46:16
问题 I'm writing a neural network for prediction of elements in a time series x + sin(x^2) in R, using the neuralnet package. This is how training data is being generated, assuming a window of 4 elements, and that the last one is the one that has to be predicted: nntr0 <- ((1:25) + sin((1:25)^2)) nntr1 <- ((2:26) + sin((2:26)^2)) nntr2 <- ((3:27) + sin((3:27)^2)) nntr3 <- ((4:28) + sin((4:28)^2)) nntr4 <- ((5:29) + sin((5:29)^2)) Then, I turn these into a data.frame: nntr <- data.frame(nntr0,

Geotools distance calculation fails with no convergence exception for several lat lon points

由 故事扮演 提交于 2019-12-05 12:19:16
I have a number of points which make getOrthodromicDistance method to fail with exception in geotools lib, while these points are valid lat lon points: Point which throws the exception (lat,lon): val p1= (5.318765,-75.786109) val p2= (-6.32907,106.09254) eg exception: No convergence for points 75°47,2'W 06°19,7'S and 106°05,6'E 05°19,1'N. java.lang.ArithmeticException: No convergence for points 75°47,2'W 06°19,7'S and 106°05,6'E 05°19,1'N. at org.geotools.referencing.GeodeticCalculator.computeDirection(GeodeticCalculator.java:1073) Code used in Scala: def latlonDistance(p1:(Double,Double), p2:

How to prevent genetic algorithm from converging on local minima?

由 ▼魔方 西西 提交于 2019-12-03 08:53:38
I am trying to build a 4 x 4 sudoku solver by using the genetic algorithm. I have some issues with values converging to local minima. I am using a ranked approach and removing the bottom two ranked answer possibilities and replacing them with a crossover between the two highest ranked answer possibilities. For additional help avoiding local mininma, I am also using mutation. If an answer is not determined within a specific amount of generation, my population is filled with completely new and random state values. However, my algorithm seems to get stuck in local minima. As a fitness function, I

Strange convergence in simple Neural Network

由 隐身守侯 提交于 2019-12-02 07:26:05
I've been struggling for some time with building a simplistic NN in Java. I've been working on and off on this project for a few months and I wanna finish it. My main issue is that I dunno how to implement backpropagation correctly (all sources use Python, math jargon, or explain the idea too briefly). Today I tried deducing the ideology by myself and the rule that I'm using is: the weight update = error * sigmoidDerivative(error) * weight itself; error = output - actual; (last layer) error = sigmoidDerivative(error from previous layer) * weight attaching this neuron to the neuron giving the

nls - convergence error

由 断了今生、忘了曾经 提交于 2019-12-01 06:16:40
问题 For this dataset: dat = structure(list(x = c(5L, 5L, 5L, 5L, 10L, 10L, 10L, 10L, 15L, 15L, 15L, 15L, 17L, 17L, 17L, 17L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 22L, 22L, 22L, 22L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 25L, 27L, 27L, 27L, 27L, 30L, 30L, 30L, 30L, 35L, 35L, 35L, 35L), y = c(2.2, 2.2, 1.95, 1.9, 4.1, 3.95, 3.75, 3.4, 5.15, 4.6, 4.75, 5.15, 3.7, 4.1, 3.9, 3.5, 7, 6.7, 6.7, 6.95, 4.95, 6, 6.45, 6.4, 7, 4.45, 6.15, 6.4, 7, 6.6, 6.7, 7, 4.5, 4.7, 5.75, 4.35, 5.4, 5.15, 5.7, 5.7, 0, 0,

Convergence error for development version of lme4

由 断了今生、忘了曾经 提交于 2019-11-28 03:07:01
I am attempting to do a power analysis for a mixed-effects model using the development version of lme4 and this tutorial. I notice in the tutorial that lme4 throws a convergence error: ## Warning: Model failed to converge with max|grad| = 0.00187101 (tol = ## 0.001) The same warning comes up when I run the code for my dataset, with: ## Warning message: In checkConv(attr(opt, "derivs"), opt$par, checkCtrl = control$checkConv, : Model failed to converge with max|grad| = 0.774131 (tol = 0.001) The estimates from a regular glmer call with this updated version are also slightly different from when
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