| # By Jake VanderPlas | |
| # License: BSD-style | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| def discrete_cmap(N, base_cmap=None): | |
| """Create an N-bin discrete colormap from the specified input map""" |
| from time import time | |
| import numpy as np | |
| from scipy.sparse import spdiags, issparse, dia_matrix | |
| from scipy.sparse.linalg import factorized | |
| from scipy import linalg as splinalg | |
| class BandedMatrix(object): | |
| def __init__(self, data, lu=None): | |
| if issparse(data): | |
| if lu: |
A simple example of two-way communication between IPython notebook and javascript, using a D3 widget.
View this notebook on nbviewer, but be aware that it won't work without being connected to an IPython kernel!
It's better to download the notebook itself and open it with IPython notebook.
| """ | |
| This file has an example function, with a documentation string which should | |
| serve as a template for scikit-learn docstrings. | |
| """ | |
| def sklearn_template(X, y, a=1, flag=True, f=None, **kwargs): | |
| """This is where a short one-line description goes | |
| This is where a longer, multi-line description goes. It's not | |
| required, but might be helpful if more information is needed. |
This is a quick attempt at writing a ball tree for nearest neighbor searches using numba. I've included a pure python version, and a version with numba jit decorators. Because class support in numba is not yet complete, all the code is factored out to stand-alone functions in the numba version. The resulting code produced by numba is about ~10 times slower than the cython ball tree in scikit-learn. My guess is that part of this stems from lack of inlining in numba, while the rest is due to some sort of overhead