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jakevdp / google_magic.ipynb
Last active Aug 29, 2015
Google magic for IPython
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@jakevdp
jakevdp / bayes_factor.py
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()
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jakevdp / AR_crash.py
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))
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jakevdp / README
Created Sep 29, 2011
test code & dataset for scikit-learn issue #365
View README
code demonstrating the problem seen in issue #365
to run the example:
tar -zxvf data.tgz
python test.py
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jakevdp / banded_tools.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:
@jakevdp
jakevdp / README.rst
Created Jan 5, 2012
General Distance Metrics for BallTree
View README.rst

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.

@jakevdp
jakevdp / kneighbors_test.py
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
@jakevdp
jakevdp / basic_animation.py
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
@jakevdp
jakevdp / FremontBridge.ipynb
Created Oct 20, 2015
Fremont Bike Counts 2015
View FremontBridge.ipynb
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