variance

I need to use pca to identify the dimensions with the highest variance of a certain set of data. I'm using scikit-learn's pca to do it, but I can't identify from the output of the pca method what are the components of my data with the highest variance. Keep in mind that I don't want to eliminate those dimensions, only identify them. My data is organized as a matrix with 150 rows of data, each one with 4 dimensions. I'm doing as follow: pca = sklearn.decomposition.PCA() pca.fit(data_matrix) When I print pca.explained_variance_ratio_ , it outputs an array of variance ratios ordered from highest

I've read about Scala having covariant return types for functions . But what about its argument types? What does FunctionX(T1,...,R) have to do with all this? Gabriele Petronella If you look at the documentation for any FunctionX class, you'll see that the return type is co-variant and the argument types are contravariant. For instance, Function2 has the signature: Function2[-T1, -T2, +R] extends AnyRef You can spot the - and + before the type parameters, where - means contravariant and + covariant. This means that given class Animal class Dog extends Animal then Function1[Animal, Dog] <:

I'm working on a project in which need to get the variance of an image. Currently I'm taking 2 approaches (both work but are very slow): Calculating the variance for each pixel individually: This is the code using numpy, varianceMatrix is the output varianceMatrix = np.zeros(im.shape,np.uint8) w = 1 # the radius of pixels neighbors ny = len(im) nx = len(im[0]) for i in range(w,nx-w): for j in range(w,ny-w): sampleframe = im[j-w:j+w, i-w:i+w] variance = np.var(sampleframe) varianceMatrix[j][i] = int(variance) return varianceMatrix Using an existing scipy function: This is the scipy function:

I'm trying to find an efficient, numerically stable algorithm to calculate a rolling variance (for instance, a variance over a 20-period rolling window). I'm aware of the Welford algorithm that efficiently computes the running variance for a stream of numbers (it requires only one pass), but am not sure if this can be adapted for a rolling window. I would also like the solution to avoid the accuracy problems discussed at the top of this article by John D. Cook. A solution in any language is fine. Mike Taylor I've run across this problem as well. There are some great posts out there in