jakevdp

import mymodule

jakevdp

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()

jakevdp

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))

jakevdp

code demonstrating the problem seen in issue #365

to run the example:
  tar -zxvf data.tgz
  python test.py

jakevdp

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

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

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

jakevdp

import warnings
import re
import itertools

from pelican.readers import MarkdownReader, EXTENSIONS
from pelican.utils import pelican_open
from pelican import signals


def split_args(argstring):