I'm working on a task that I would like to automate,I'm new to looping and variables assigments so any help will be great. The task has two steps: first get few data set with one different character from each other and second apply an lm model with v
The following code is using dgesv library in C to calculate linear regression. It has X observations and Y predictions, with X and Y saved as double arrays. I am wondering 1) Is this code calculating linear regression with intercept or not? 2) How ca
Currently my convergence criteria for SGD checks whether the MSE error ratio is within a specific boundary. def compute_mse(data, labels, weights): m = len(labels) hypothesis = np.dot(data,weights) sq_errors = (hypothesis - labels) ** 2 mse = np.sum(
I'm fairly new to R and I'm trying to create a model to work on Kaggle's Facial Keypoint Detection sample project. The ultimate issue is that creating any model (I'm trying a neural net using the neuralnet project, but I've also tried simpler linear
I'm not seeing what is wrong with my code for regularized linear regression. Unregularized I have simply this, which I'm reasonably certain is correct: import numpy as np def get_model(features, labels): return np.linalg.pinv(features).dot(labels) He
I have created a scatterplot3d with a linear model applied. Unfortunately the results of the LM are subtle and need to be emphasised, my question is how can I extend the LM grid outside of the 'cube'. Plot: Code: Plot1 <-scatterplot3d( d$MEI, d$YY
I need to analyse unbalanced data through linear regression: modJuin=lm(TleafMax~TairMax*orientation, na.action="na.exclude", data=aJuin) "TairMax" is a continuous numerical variable and "orientation" is a factor with two levels. Hence, I used drop1(
I have to identify an ARX under some linear constraints, this means that I have a quadratic programming with linear equality constraints problem. One way is to use the following equations in the red boxes. A possible disadvantage in this case is the
I have a data-set which has columns as x1 x2 x3 x4 x5 y all of them has integer / float value and Y values ranges from 98,000 to 1,10,000 If I want to find the relationship between x1 and y , x2 and y ... x5 and y and come up with y = A.x1+c how shou
I am confused. I input a .csv file in R and want to fit a linear multivariate regression model. However, R declares all my obvious numeric variables to be factors and my categorial variables to be integers. Therefore, I cannot fit the model. Does any
Scikit-learn offers a large variety of useful linear models. However, I am working on a problem which is linear with non-negativity constraints (i.e. solution variables should be non-negative). I would like to use scikit-learn, but the only function
After running a multiple regression in R, the regression summary indicates the significant variables with stars. In a dataset that I am working on there are nearly 2000 variables and the significant variables identified by R includes more than 50 var
I have created a script like the one below to do something I called as "weighted" regression: library(plyr) set.seed(100) temp.df <- data.frame(uid=1:200, bp=sample(x=c(100:200),size=200,replace=TRUE), age=sample(x=c(30:65),size=200,replace=TRUE),
I like to extract the coefficients and standard errors of each lm object and combine them into a data.frame with NA fill in for the missing predictors. set.seed(12345) x<-matrix(rnorm(1000),nrow=100,ncol=10) colnames(x)<-paste("x",1:10,sep="")
I know that similar questions have been asked in the past but mine has to do with weighted regression in which only the coefficients are needed. The computation should be as fast as possible. I know that ls.fit and some Rcpp package functions are opt
I have a classic linear regression problem of the form: y = X b where y is a response vector X is a matrix of input variables and b is the vector of fit parameters I am searching for. Python provides b = numpy.linalg.lstsq( X , y ) for solving proble
The code below returns the number of resolved tickets and the number of opened tickets for a period (period is YYYY,WW) going back a certain number of days. For example if @NoOfDays is 7: resolved | opened | week | year | period 56 | 30 | 13 | 2012 |
I'm a bit of a newby so apologies if this question has already been answered, I've had a look and couldn't find specifically what I was looking for. I have some more or less linear data of the form x = [0.1, 0.2, 0.4, 0.6, 0.8, 1.0, 2.0, 4.0, 6.0, 8.
I've got a dataset with 9 continuous independent variables that I'm trying to select between to fit a model to a single percentage (dependent) variable: Score. Unfortunately, I know there will be serious collinearity between several of the variables.
I have 2 data frames. One is training data (pubs1), the other (pubs2) test data. I can create a linear regression object but am unable to create a prediction. This is not my first time doing this and can't figure out what is going wrong. > head(pu