If I read the source code right (Numpy 1.8.2, Scipy 0.14.1 ), numpy.linalg.lstsq() uses the LAPACK routine xGELSD and scipy.linalg.lstsq() usesxGELSS. The LAPACK Manual Sec. 2.4 states The subroutine xGELSD is significantly faster than its older counterpart xGELSS, especially for large problems, but may require somewhat more workspace depending on the matrix dimensions. That means … Read more
As @AbhranilDas mentioned, just use a linear method. There’s no need for a non-linear solver like scipy.optimize.lstsq. Typically, you’d use numpy.polyfit to fit a line to your data, but in this case you’ll need to do use numpy.linalg.lstsq directly, as you want to set the intercept to zero. As a quick example: import numpy as … Read more
It’s probably what np.random.choice does in @Ophion’s answer, but you can construct a normalized cumulative density function, then choose based on a uniform random number: from __future__ import division import numpy as np import matplotlib.pyplot as plt data = np.random.normal(size=1000) hist, bins = np.histogram(data, bins=50) bin_midpoints = bins[:-1] + np.diff(bins)/2 cdf = np.cumsum(hist) cdf = … Read more
Every color in an image is represented by one byte. So to create an image array, you should set it’s dtype to uint8. And, you don’t need for-loop to set every elements to 255, you can use fill() method or slice index: import numpy as np img = np.zeros([100,100,3],dtype=np.uint8) img.fill(255) # or img[:] = 255
This worked for me Run pyinstaller and stop it to generate the spec file : pyinstaller filename.py A file with .spec as extension should be generated Now add the following lines to the beginning of the spec file : import sys sys.setrecursionlimit(5000) Now run the spec file using : pyinstaller filename.spec
Here is a method that creates a sparse scipy matrix based on data and indices of person and thing. person_u and thing_u are lists representing the unique entries for your rows and columns of pivot you want to create. Note: this assumes that your count column already has the value you want in it. from … Read more
There are three steps in hierarchical agglomerative clustering (HAC): Quantify Data (metric argument) Cluster Data (method argument) Choose the number of clusters Doing z = linkage(a) will accomplish the first two steps. Since you did not specify any parameters it uses the standard values metric=”euclidean” method = ‘single’ So z = linkage(a) will give you … Read more
EDIT: As of version 1.9, scipy has changed so that import scipy now allows access to the modules but only actually loads them when they are used. Please refer to the answer from tupui below (and upvote that answer to make it the top one!). For historical purposes, here is my answer from 2016: The … Read more
Actually, you were not far from the solution in your question. Using scipy.interpolate.splprep for parametric B-spline interpolation would be the simplest approach. It also natively supports closed curves, if you provide the per=1 parameter, import numpy as np from scipy.interpolate import splprep, splev import matplotlib.pyplot as plt # define pts from the question tck, u … Read more
The sparse library now has hstack and vstack for respectively concatenating matrices horizontally and vertically.