Avoid division by zero in C when taking log with respect to a random number
You just need to make sure that the result of rand() isn't 0 so that you won't have to do the conversion to double and division again and again int r = rand(); while (r == 0) r = rand(); u1 = (double) rand() / RAND_MAX; An even simpler solution...
Rotating a gaussian function - matlab
If you consult the article on Wikipedia about the general elliptical version of the Gaussian 2D PDF, it doesn't look like you're rotating it properly. In general, the equation is: Source: Wikipedia where: Usually, A = 1 and we'll adopt that here. The angle theta will rotate the PDF counter-clockwise,...
Generate array with 3 gaussian distributions MATLAB
Let's use a struct to store the meta-parameters action.awake_in_bed = [1 5*60 1*60]; action.out_of_bad = [3 30 10]; action.out_of_bedroom = [2 2*60 15]; ACTIVITY = {'awake_in_bed','out_of_bad','out_of_bedroom'}; After these pre-definitions, we can sample an activity vector ACTIVITY_WAKE = cell(1,numel(ACTIVITY)); for ii = 1:numel( ACTIVITY ) %// foreach activity cp = action.(ACTIVITY{ii});...
How would I produce random numbers between two values with a Gaussian distrubution
A Gaussian distribution isn't bounded, but you can make it unlikely that you will sample outside your range. For example, you can sample numbers with a mean of 400 and a standard deviation of 200/3, meaning being outside the range [200, 600] will be outside of 3 standard deviations. mean...
gaussian sum filter for irregular spaced points
python,numpy,scipy,gaussian,smooth
This will blow up for very large datasets, but the proper calculaiton you are asking for would be done as follows: import numpy as np import matplotlib.pyplot as plt np.random.seed(0) # for repeatability x = np.random.rand(30) x.sort() y = np.random.rand(30) x_eval = np.linspace(0, 1, 11) sigma = 0.1 delta_x =...
Inverse of the cumulative gaussian distribution in R
r,gaussian,normal-distribution
With qnorm: qnorm(.025) # [1] -1.959964 qnorm(.5) # [1] 0 qnorm(.975) # [1] 1.959964 ...
normal Gaussian curve on Histogram in R [duplicate]
r,histogram,curve-fitting,gaussian
lines(density(rnorm(1000,mean=mean(f),sd = sd(f))),col=1,lwd=3)
Scikit-learn's Pipeline: Error with multilabel classification. A sparse matrix was passed
python,scikit-learn,gaussian,text-classification
You can do the following: class DenseTransformer(TransformerMixin): def transform(self, X, y=None, **fit_params): return X.todense() def fit_transform(self, X, y=None, **fit_params): self.fit(X, y, **fit_params) return self.transform(X) def fit(self, X, y=None, **fit_params): return self classifier = Pipeline([ ('vectorizer', CountVectorizer ()), ('TFIDF', TfidfTransformer ()), ('to_dense', DenseTransformer()), ('clf', OneVsRestClassifier (GaussianNB()))]) classifier.fit(X_train,Y) predicted = classifier.predict(X_test) Now,...
why the integral-image contains extra row and column of zeros?
matlab,opencv,image-processing,javacv,gaussian
There are 2 reasons. First one is purely mathematical. Say you have a row of 3 numbers (pixels). How many possible cumulative sums it generates? the answer is 4. You can take the sum of 0 first pixels, 1 pixel, 2 pixels or all the 3 pixels. The amount of...
Animated Frosted Glass
If you want a dynamic blurred effect you can use the UIVisualEffectView with a UIBlurEffect. Documentation: https://developer.apple.com/library/prerelease/ios/documentation/UIKit/Reference/UIBlurEffect_Ref/index.html ...
SciPy 1D Gaussian fit
The fit actually works perfectly - I get mu == 646.6 and std = 207.07, which are exactly equal to the mean and standard deviation of your y values. I think you're just confused about what you're actually plotting. norm.pdf evaluates the probability density function of the Gaussian distribution. That...
How to apply Difference of Gaussian(DoG) approach to extract pores in fingerprint image in MATLAB?
matlab,image-processing,gaussian,feature-detection
You actually need to apply a gaussian filter with 2 different sets of parameters, then subtract the filters and perform a convolution of the input image with that new filter, i.e. the difference of gaussians. Here is an example with the coins.png demo image...The code is commented; don't hesitate to...
Blur a matrix using Fast Fourier Transforms
c++,wolfram-mathematica,gaussian,convolution,fftw
Using FFT to do convolutions is only efficient when you have very large convolution kernels. In most blurring applications the kernel is much much smaller than the image, e.g. 3x3, so FFT would be significantly slower. There are many implementations for doing small-kernel convolutions. Most modern hardware supports such intrinsic...
how to choose the delta value in EM clustering in ELKI
cluster-analysis,data-mining,gaussian,elki
The delta parameter in EM is necessary to detect convergence. Since EM uses soft assignments internally, it will continue updating the values to arbitrary digits (technically, it will eventually run out of precision, and stop). As long as you choose a small enough value, you should be fine. However, EM...
How to plot a gaussian over histogram
python,matplotlib,histogram,gaussian
You probably want to use numpy to generate a Gaussian, and then simply plot it on the same axes. There is a good example here: Fitting a Gaussian to a histogram with MatPlotLib and Numpy - wrong Y-scaling? If you actually want to automatically generate a fitted gaussian from the...
Gauss Elimination C++ segmentation fault
c++,matrix,segmentation-fault,gaussian
The problem is that in C/C++ the first element of an array should have index 0, so your for(int i=1; i<=order; i++) should be for(int i=0; i<order; i++) in the gaussElimination function....
Implementing Gaussian elimination with partial pivoting [closed]
Your error is pretty simple. You're not pivoting properly - specifically here inside your if statement: for j = i+1:n if abs(A(array(i),i)) < abs(A(array(i),i)) %row interchange <------- temp = array(i); array(i) = array(j); array(j) = temp; end end Your check to see which coefficient to pivot from is not correct...
How to deal with arbitrary size for Laplacian Pyramid?
math,image-processing,graphics,gaussian,imaging
One approach is to create an image with a width and height equal to the next 2^m+1,2^n+1, but instead of up-sampling the image to fill the expanded dimensions, just place it in the top-left corner and fill the empty space to the right and below with a constant value (the...
NaN error for gaussian random distribution
java,random,distribution,nan,gaussian
In your code dRandom1 can be negative, while real logarithms only take arguments from (0, +inf)
gaussian class in java
Use this implementation and all will be happy: public class GInt { private int real; private int imag; public GInt(int r) { imag=0; real=r; } public GInt(int r, int i) { real = r; imag = i; } GInt add(GInt rhs) { GInt added; int nReal = this.real + rhs.real;...
gaussian fitting not working using Python
You're experiencing the classical problem of supplying an incorrect guess to the curve fitting algorithm. That is entirely due to your unnecessary upside down flipping of the matrix T and then not taking into account the new locations of the gaussians (the parameter called center, passed to gaussian() - I...
Analysis of peaks in MATLAB mesh plot
matlab,image-processing,3d,signal-processing,gaussian
The logic behind findpeaks for 1D arrays / vectors is that it looks at local 3 element neighbourhoods and sees whether the centre of the window is the maximum element. If it is, this could potentially be a peak. You would also need to apply a threshold to ensure that...
Fitting Guassian curve to data in python
python,python-2.7,optimization,gaussian
Probably your callback is called in curve_fit with a different number of parameters. Have a look at the documentation where it says: The model function, f(x, ...). It must take the independent variable as the first argument and the parameters to fit as separate remaining arguments. To make sure this...
tweak intensity of blur effect inside UIVisualEffectView in Swift
swift,gaussian,blurry,uiblureffect,uivisualeffectview
Since there is no other parameter in UIBlurEffect , I think the only way is to use the CIFilter preset CIGaussianBlur to blur the background View and use its key inputRadius to adjust the level. If you want to achieve the same effect as so called light/dark/ExtraLight, you can compose...
OpenCV Error: Assertion failed (ksize.width > … for GaussianBlur
As I understood from the trace, you are only allowed to use new Size(x,y) where x & y are odd
What is the difference between random.normalvariate() and random.gauss() in python?
python,random,gaussian,normal-distribution
Thread-safe pieces of code must account for possible race conditions during execution. This introduces overhead as a result of synchronization schemes like mutexes, semaphores, etc. However, if you are writing non-reentrant code, no race conditions normally arise, which essentially means that you can write code that executes a bit faster....
How to make Gaussian Blur work properly?
I assume uintMatrix is a two-dimensional array of 32-bit ints, and that you've packed the red, green, and blue channels into that. If so, that's your problem. You need to blur each channel independently....
Java Random.nextGaussian between 0 and 1
In that case you should use nextDouble(). The Gaussian distribution is a distribution that ranges over the entire collection of double values (mathematically speaking, from minus infinity to positive infinity) with a peak around zero. The Gaussian distribution is thus not uniform. The nextDouble() method draws numbers uniformly between 0...
Sample a random number following a distribution between two values
matlab,random,distribution,gaussian,sampling
Might use Irwin-Hall from https://en.wikipedia.org/wiki/Irwin%E2%80%93Hall_distribution Basically, min(IH(n)) = 0 max(IH(n)) = n peak(IH(n)) = n/2 Scaling to your [1.9...2.1] range v = 1.9 + ((2.1-1.9)/n) * IH(n) It is bounded, very easy to sample, and at large n it is pretty much gaussian. You could vary n to get narrow...
My use of Scipy curve_fit does not seem to work well
python,numpy,scipy,curve-fitting,gaussian
You have to pass an initial guess for popt, otherwise the fit starts with [1,1,1] as initial guess which is pretty poor for your dataset! The following gives reasonable results for me: popt, pcov = curve_fit(func, xk, Kp4, p0=[20,630,5]) The initial guess could be [np.mean(Kp4), np.mean(xk),5*(max(xk)-min(xk))/len(xk)], to have a general...
Why Gaussian radial basis function maps the examples into an infinite-dimensional space?
machine-learning,classification,svm,gaussian,supervised-learning
The other answers are correct but don't really tell the right story here. Importantly, you are correct. If you have m distinct training points then the gaussian radial basis kernel makes the SVM operate in an m dimensional space. We say that the radial basis kernel maps to a space...
NaiveBayes classifier handling different data types in python
python,scikit-learn,gaussian,naivebayes
Yes, you will need to convert the strings to numerical values The naive Bayes classifier can not handle strings as there is not a way an string can enter in a mathematical equation. If your strings have some "scalar value" for example "large, medium, small" you might want to classify...
Generating random numbers from a truncated Gaussian
So we might have to differentiate between mean values before and after truncation, and you apparently intend to control the observable mean values that truncated samples would presumably converge to, although rnorm() (and probably rtruncnorm(), which I do not know) expect "before"-means; while some statisticians at stats.stackexchange.com might provide you...
Finding conditional Gaussian Mixture Model using scikit-learn.mixture.GMM
python,scikit-learn,gaussian,normal-distribution
Try looking into pypr. From the documentation, here is how you would find a GMM conditioned on one or more of the variables: # Now we will find the conditional distribution of x given y (con_cen, con_cov, new_p_k) = gmm.cond_dist(np.array([np.nan, y]), \ cen_lst, cov_lst, p_k) As far as i remember,...
Gaussian distribution with mean and sigma in C++11
There are two parts of the algorithm: uniform random number generator, and convert the uniform random number to a random number according to Gaussian distribution. In your case, e2 is your uniform random number generator given the seed rd, std::normal_distribution<float>(m, s) generates an object which does the 2nd part of...
Gauss(-Legendre) quadrature in python
python,numpy,integration,gaussian
As Will says you're getting confused between arrays and functions. You need to define the function you want to integrate separately and pass it into gauss. E.g. def my_f(x): return 2*x**2 - 3*x +15 gauss(m_f,2,1,-1) You also don't need to loop as numpy arrays are vectorized objects. def gauss1(f,n): [x,w]...
Python 2D Gaussian Fit with NaN Values in Data
The obvious thing to do is remove the NaNs from data. Doing so, however, also requires that the corresponding positions in the 2D X, Y location arrays also be removed: X, Y = np.indices(data.shape) mask = ~np.isnan(data) x = X[mask] y = Y[mask] data = data[mask] Now you can use...
implement Gaussian distribution with singular sigma
You only need to model one dimension of the data with a 1D gaussian distribution in this case. If you have two-dimensional data {(x1,x2)_i} whose covariance matrix is singular, this means that the data lies along a straight line. The {x2} data is a deterministic function of the {x1} data,...
Gaussian blur with opencv in ios
android,ios,opencv,blur,gaussian
By the looks of things, You have to convert it to a cv::Mat, then you can use the normal guassian blur c++ method and then convert it back to ULLImage The above link demonstrates how to convert from and to the two image types. Once you have converted it to...
Pandas rolling window function offsets data
python,pandas,time-series,gaussian
As there has been no specific Pandas solution posted for this question (or the similar linked question), I am posting a solution using standard numpy and scipy functions. This will produce a smoothed curve using gaussian weighting, and works for any magnitude data (does not have offset issues). def smooth_gaussian(data,window,std):...
Calculate the gaussian kernel density in python
Dr Vanderplas has written a blog post detailing how to do this with three separate libraries: Kernel Density Estimation in Python: Scipy, Statsmodels, and scikit-learn. Should be a good start....
Incorrect probability density [closed]
excel,distribution,gaussian,normal-distribution
The results from NORM.DIST are correct... if you directly implement the Gaussian function in your sheet using: =(1/($F$8*SQRT(2*PI())))EXP( -((M3-$F$7)^2)/(2$F$8^2)) which is an implementation of the standard Gaussian function e.g. f(x) on: http://mathworld.wolfram.com/GaussianFunction.html then the results exactly match Excel's NORM.DIST built in function. When you say the values "should be" in...
Trying to plot multivariate Gaussian dist. in a 3D plot matplotlib returns an empty figure
I'm no expert with 3D-plots in matplotlib, but I believe your data wrong. As you can see in the sourcecode in this tutorial, your X,Y and Z data have to be 2-dimensional arrays. Your X and Y are one-dimensional, and your Z is a simple list. Try reshaping your data...
gaussian smoother in r
Answer found in previous post - works great. f<-function(x, theta) { m<-theta[1]; s<-theta[2]; a<-theta[3]; b<-theta[4]; a*exp(-0.5*((x-m)/s)^2) + b } fit<-nls(y~f(x,c(m,s,a,b)), data.frame(x,y), start=list(m=12, s=5, a=12, b=-2)) ...