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# Jake Vanderplas jakevdp

Last active Aug 29, 2015
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Created Feb 24, 2015
Numba error
View make_error.py
 import mymodule
Created Jun 9, 2015
estimate Bayes factor
View bayes_factor.py
 import numpy as np from astroML import stats def estimate_bayes_factor(traces, logp, r=0.05, return_list=False, old_version=False, normalize_space=True): """Estimate the bayes factor using the local density of points""" D, N = traces.shape if normalize_space: traces = traces.copy()
Created Jun 13, 2011
ARPACK memory error
View AR_crash.py
 import numpy as np from scipy.sparse.linalg import eigs N = 6 k = 2 # with this random seed, I get a memory error on the third iteration below np.random.seed(2301) A = np.random.random((N,N))
Created Sep 29, 2011
test code & dataset for scikit-learn issue #365
 code demonstrating the problem seen in issue #365 to run the example: tar -zxvf data.tgz python test.py
Created Dec 23, 2011
Benchmarks for eigenvalue decomposition
View banded_tools.py
 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:
Created Jan 5, 2012
General Distance Metrics for BallTree

This is the outline of a framework that will allow general distance metrics to be incorporated into scikit-learn BallTree. The idea is that we need a fast way to compute the distance between two points under a given metric. In the basic framework here, this involves creating an object which exposes C-pointers to a function and a parameter structure so that the distance function can be called from either python or directly from cython with no python overhead.

Created Jan 23, 2012
Showing memory error in BallTree
View kneighbors_test.py
 import warnings from sklearn import datasets from sklearn.neighbors import NearestNeighbors import numpy as np n_points = 1000 n_neighbors = 10 out_dim = 2 n_trials = 100
Created Oct 6, 2012
Demo for GIF animations
View basic_animation.py
 import numpy as np from matplotlib import pyplot as plt from matplotlib import animation # First set up the figure, the axis, and the plot element we want to animate fig = plt.figure() ax = fig.add_subplot(111, xlim=(0, 2), ylim=(-2, 2)) line, = ax.plot([], [], lw=2) # initialization function: plot the background of each frame
Created Oct 20, 2015
Fremont Bike Counts 2015
View FremontBridge.ipynb