probability-density

Im trying to achieve a density map from network output of dimension 20x20x1x50. Here 20x20 is the output map and 50 is the batch size. The issue is that the value of output X is equal 0.098 across each output matrix..20x20. There is no gaussian shape like density map but a flat similar valued output map 20x20x1x50. The issue is shown in the figure attached. What am i missing here? The euclidean loss for backpropagation is given as: case {'l2loss'} res=(c-X); n=1; if isempty(dzdy) %forward Y = sum((res(:).^2))/numel(res); else Y_= -1.*(c-X); Y = 2*single (Y_ * (dzdy / n) ); end Found the

A kernel density estimator is used to estimate a particular probability density function (see mvstat.net and sckit-learn docs for references) My confusion is about what exactly does kde2d() do? Does it estimate the joint distribution probability density function of two random variables f(a,b) in the below example? And what does the color mean? Here is the code example I am referring to. b <- log10(rgamma(1000, 6, 3)) a <- log10((rweibull(1000, 8, 2))) density <- kde2d(a, b, n=100) colour_flow <- colorRampPalette(c('white', 'blue', 'yellow', 'red', 'darkred')) filled.contour(density, color

Is it the case that the exact derivative of a cumulative density function is the probability density function (PDF)? I am calculating the derivative using the numpy.diff() , is this correct? See below code below: import scipy.stats as s import matplotlib.pyplot as plt import numpy as np wei = s.weibull_min(2, 0, 2) # shape, loc, scale - creates weibull object sample = wei.rvs(1000) shape, loc, scale = s.weibull_min.fit(sample, floc=0) x = np.linspace(np.min(sample), np.max(sample)) plt.hist(sample, normed=True, fc="none", ec="grey", label="frequency") plt.plot(x, wei.cdf(x), label="cdf") plt

This question already has answers here : Closed 2 years ago . Find the probability density of a new data point using “density” function in R (3 answers) Density Value for each Return (3 answers) I was wondering if there is a R base way to obtain the exact kernel density at any point desired? As an example, how can I get the exact kernel density at the 3 following points -2, 0, +2 on X-Axis in a plot like below? set.seed(2937107) plot( density(rnorm(1e4)) ) Use linear interpolation to find it. d <- density(rnorm(10000)) approx(d$x, d$y, xout = c(-2, 0, 2)) The precision of interpolation can be

I have a specific density function and I want to generate random variables knowing the expression of the density function. For example, the density function is : df=function(x) { - ((-a1/a2)*exp((x-a3)/a2))/(1+exp((x-a3)/a2))^2 } From this expression I want to generate 1000 random elements with the same distribution. I know I should use the inverse sampling method. For this, I use the CDF function of my PDF which is calculated as follows: cdf=function(x) { 1 - a1/(1+exp((x-a3)/a2)) The idea is to generate uniformly distributed samples and then map them with my CDF functions to get an inverse

Say I have a simple array, with a corresponding probability distribution. library(stats) data <- c(0,0.08,0.15,0.28,0.90) pdf_of_data <- density(data, from= 0, to=1, bw=0.1) Is there a way I could generate another set of data using the same distribution. As the operation is probabilistic, it need not exactly match the initial distribution anymore, but will be just generated from it. I did have success finding a simple solution on my own. Thanks! Your best bet is to generate the empirical cumulative density function, approximate the inverse, and then transform the input. The compound expression

How can I get the area under overlapping density curves? How can I solve the problem with R? (There is a solution for python here: Calculate overlap area of two functions ) set.seed(1234) df <- data.frame( sex=factor(rep(c("F", "M"), each=200)), weight=round(c(rnorm(200, mean=55, sd=5), rnorm(200, mean=65, sd=5))) ) (Source: http://www.sthda.com/english/wiki/ggplot2-density-plot-quick-start-guide-r-software-and-data-visualization ) ggplot(df, aes(x=weight, color=sex, fill=sex)) + geom_density(aes(y=..density..), alpha=0.5) "The points used in the plot are returned by ggplot_build(), so you can