variance

问题 I'm new to the forum and a beginner in programming. I have the task to program a random walk in Matlab (1D or 2D) with a variance that I can adjust. I found the code for the random walk, but I'm really confused where to put the variance. I thought that the random walk always has the same variance (= t ) so maybe I'm just lost in the math. How do I control the variance? 回答1: For a simple random walk, consider using the Normal distribution with mean 0 (also called 'drift') and a non-zero

问题 I have a dataset consisting of 132 observations and 10 variables. These variables are all categorical. I am trying to see how my observations cluster and how they are different based on the percentage of variance. i.e I want to find out if a) there are any variables which helps to draw certain observation points apart from one another and b) if yes, what is the percentage of variance explained by it? I was advised to run a PCoA (Principle Coordinates Analysis) on my data. I ran it using vegan

问题 This question already has an answer here : Why Sklearn TruncatedSVD's explained variance ratios are not in descending order? (1 answer) Closed 9 months ago . This question is actually a duplicate of this one, which however remains unanswered at the time of writing. Why is the explained_variance_ratio_ from TruncatedSVD not in descending order like it would be from PCA ? In my experience it seems that the first element of the list is always the lowest, and then at the second element the value

问题 I am trying to compute the blurriness of an image by using LaplacianFilter. According to this article: https://www.pyimagesearch.com/2015/09/07/blur-detection-with-opencv/ I have to compute the variance of the output image. The problem is I don't understand conceptually how do I compute variance of an image. Every pixel has 4 values for every color channel, therefore I can compute the variance of every channel, but then I get 4 values, or even 16 by computing variance-covariance matrix, but

问题 I am working with sklearn and specifically the linear_model module. After fitting a simple linear as in import pandas as pd import numpy as np from sklearn import linear_model randn = np.random.randn X = pd.DataFrame(randn(10,3), columns=['X1','X2','X3']) y = pd.DataFrame(randn(10,1), columns=['Y']) model = linear_model.LinearRegression() model.fit(X=X, y=y) I see how I can access to coefficients and intercept via coef_ and intercept_, prediction is straightforward as well. I would like to

来源： https://stackoverflow.com/questions/40167425/error-in-calculating-vif-variance-inflation-factor

来源： https://stackoverflow.com/questions/40167425/error-in-calculating-vif-variance-inflation-factor

来源： https://stackoverflow.com/questions/40167425/error-in-calculating-vif-variance-inflation-factor

来源： https://stackoverflow.com/questions/40167425/error-in-calculating-vif-variance-inflation-factor

来源： https://stackoverflow.com/questions/40167425/error-in-calculating-vif-variance-inflation-factor