I can't figure out how to do a Two-sample KS test in Scipy. After reading the documentation scipy kstest
For SciPy sparse matrix, one can use todense() or toarray() to transform to NumPy matrix or array. What are th
I am using Python 2.7 and trying to get PyBrain to work. But I get this error even though scipy is installed
How to calculate probability in normal distribution given mean, std in Python? I can always explicitly code my
So I have a little problem. I have a data set in scipy that is already in the histogram format, so I have the
This Q&A is intended as a canonical(-ish) concerning two-dimensional (and multi-dimensional) interpolatio
I am getting the following error while trying to import from sklearn: >>> from sklearn import svm T
In R I can create the desired output by doing: data = c(rep(1.5, 7), rep(2.5, 2), rep(3.5, 8), rep(
I can't seem to find any python libraries that do multiple regression. The only things I find only do sim
I have sample data which I would like to compute a confidence interval for, assuming a normal distribution. I
I am trying to read an image with scipy. However it does not accept the scipy.misc.imread part. What could be
I can write something myself by finding zero-crossings of the first derivative or something, but it seems like
INTRODUCTION: I have a list of more than 30,000 integer values ranging from 0 to 47, inclusive, e.g.[0,0,0,0,.
After doing some processing on an audio or image array, it needs to be normalized within a range before it can
I have a set of data and I want to compare which line describes it best (polynomials of different orders, expo
I know I could implement a root mean squared error function like this: def rmse(predictions, targets): re
Is there a SciPy function or NumPy function or module for Python that calculates the running mean of a 1D arra
Lets assume we have a dataset which might be given approximately by import numpy as np x = np.linspace(0,2*np