How does condensed distance matrix work? (pdist)
scipy.spatial.distance.pdist returns a condensed distance matrix. From the documentation:
scipy.spatial.distance.pdist returns a condensed distance matrix. From the documentation:
I am trying to fit piecewise linear fit as shown in fig.1 for a data set
I have a bunch of MATLAB code from my MS thesis which I now want to convert to Python (using numpy/scipy and matplotlib) and distribute as open-source. I know the similarity between MATLAB and Python scientific libraries, and converting them manually will be not more than a fortnight (provided that I work towards it every … Read more
numpy.distutils.system_info.BlasNotFoundError: Blas (http://www.netlib.org/blas/) libraries not found. Directories to search for the libraries can be specified in the numpy/distutils/site.cfg file (section [blas]) or by setting the BLAS environment variable. Which tar do I need to download off this site? I’ve tried the fortrans, but I keep getting this error (after setting the environment variable obviously). Answers: … Read more
I have some points and I am trying to fit curve for this points. I know that there exist scipy.optimize.curve_fit function, but I do not understand documentation, i.e how to use this function.
I can’t seem to find any python libraries that do multiple regression. The only things I find only do simple regression. I need to regress my dependent variable (y) against several independent variables (x1, x2, x3, etc.).
I am currently going through numpy and there is a topic in numpy called “strides”. I understand what it is. But how does it work? I did not find any useful information online. Can anyone let me understand in a layman’s terms?
I am getting the following error while trying to import from sklearn:
After doing some processing on an audio or image array, it needs to be normalized within a range before it can be written back to a file. This can be done like so:
I have access to NumPy and SciPy and want to create a simple FFT of a data set. I have two lists, one that is y values and the other is timestamps for those y values.