non-linear-regression

问题 How to predict below and which algorithm is the best suit. Employee has Work Activity Start_Date & End_Date (Columns). Sheet has few other columns such as Work_Complexity (High & Low) , no. of sub-tasks for each activity. How to predict Work Activity End_Date for a Start_Date? Which ML Algorithm has to be used ? Is this can be considered as a realistic use case ? thanks!!! 回答1: Yes, this is a realistic use case. If you have a labelled data means, you have a sheet where employee start date and

问题 I am implementing a paper in Python, which was originally implemented in MATLAB. The paper says that a five degree polynomial was found using curve fitting from a set of sampling data points. I did not want to use their polynomial, so I started using the sample data points (given in paper) and tried to find a 5 degree polynomial using sklearn Polynomial Features and linear_model. As it is a multivariate equation f(x,y) where x and y are the length and width of a certain pond and f is the

问题 I am implementing a paper in Python, which was originally implemented in MATLAB. The paper says that a five degree polynomial was found using curve fitting from a set of sampling data points. I did not want to use their polynomial, so I started using the sample data points (given in paper) and tried to find a 5 degree polynomial using sklearn Polynomial Features and linear_model. As it is a multivariate equation f(x,y) where x and y are the length and width of a certain pond and f is the

问题 I am trying to create an ML model (regression) using various techniques like SMR, Logistic Regression, and others. With all the techniques, I'm not able to get efficiency more than 35%. Here's what I'm doing: X_data = [X_data_distance] X_data = np.vstack(X_data).astype(np.float64) X_data = X_data.T y_data = X_data_orders #print(X_data.shape) #print(y_data.shape) #(10000, 1) #(10000,) X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size=0.33, random_state=42) svr_rbf =

问题 Trying for the logarithmic fit on the dataset using the codes posted. I keep getting Optimal parameters not found: Number of calls to function has reached maxfev = 800 . Can you help me 1. Resolve the error 2. If the equation I'm using is a good one for the current dataset 3. Suggest any alternative method? Dataset Years Values 0 2000 23.0 1 2001 27.5 2 2002 46.0 3 2003 56.0 4 2004 64.8 5 2005 71.2 6 2006 80.2 7 2007 98.0 8 2008 113.0 9 2009 155.8 10 2010 414.0 11 2011 2297.8 12 2012 3628.4

问题 I am trying to convert an "nlmrt object to an "nls" object using nls2 . However, I can only manage to do it if I write explicitly the names of the parameters in the call. Can't I define the parameter names programmatically? See the reproducible example: library(nlmrt) scale_vector <- function(vector, ranges_in, ranges_out){ t <- (vector - ranges_in[1, ])/(ranges_in[2, ]-ranges_in[1, ]) vector <- (1-t) * ranges_out[1, ] + t * ranges_out[2, ] } shobbs.res <- function(x) { # UNSCALED Hobbs weeds

问题 what should I use for non-linear vector regression with C# in Accord ? Thanks (traininginputs double[][] and trainingoutput double[] NOT int[]) 回答1: Accord.NET provides a Support Vector Machine learning algorithm for regression problems in the SequentialMinimalOptimizationRegression class. There is an example application for this topic in the sample application's wiki page. Here is an example on how to use it: // Example regression problem. Suppose we are trying // to model the following

问题 I'm trying to solve the following problem: I have a lot (~80000) surface patches of an organ that's growing. I measure each of its areas over time (18 time-points) and want to fit a growth curve to it (bi-logistic model, eg. just the sum of two logistic functions bcs. there are two 'growth spurts' happening in the observed period). I have box constraints to ensure that the exponential terms don't explode and a linear constraint that one growth spurt has to happen after the other. Also, in

问题 I want to fit Isotherm models for the following data in R. The simplest isotherm model is Langmuir model given here model is given in the bottom of the page. My MWE is given below which throw the error. I wonder if there is any R package for Isotherm models. X <- c(10, 30, 50, 70, 100, 125) Y <- c(155, 250, 270, 330, 320, 323) Data <- data.frame(X, Y) LangIMfm2 <- nls(formula = Y ~ Q*b*X/(1+b*X), data = Data, start = list(Q = 1, b = 0.5), algorith = "port") Error in nls(formula = Y ~ Q * b *

问题 This question already has answers here : Predict() - Maybe I'm not understanding it (4 answers) Closed 3 years ago . I have read few answers on this here but I am afraid I have not been able to figure out an answer. My R code is: colors <- bmw[bmw$Channel=="Colors" & bmw$Hour=20,] colors_test <- tail(colors, 89) colors_train <- head(colors, 810) colors_train_agg <- aggregate(colors_train$Impressions, list(colors_train$`Position of Ad in Break`), FUN=mean, na.rm=TRUE) colnames(colors_train_agg