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
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from __future__ import division, print_function | |
import numpy as np | |
from scipy import signal | |
import matplotlib.pyplot as plt | |
def plot_response(fs, w, h, title): | |
plt.figure() | |
plt.plot(0.5*fs*w/np.pi, 20*np.log10(np.abs(h))) |
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#!/usr/bin/env python | |
# -*- coding: utf-8 -*- | |
"""Implements Partial Directed Coherence and Direct Transfer Function | |
using MVAR processes. | |
Reference | |
--------- | |
Luiz A. Baccala and Koichi Sameshima. Partial directed coherence: | |
a new concept in neural structure determination. |
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#!/usr/bin/env python | |
# -*- coding: utf-8 -*- | |
# extract stay points from a GPS log file | |
# implementation of algorithm in | |
# [1] Q. Li, Y. Zheng, X. Xie, Y. Chen, W. Liu, and W.-Y. Ma, "Mining user similarity based on location history", in Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems, New York, NY, USA, 2008, pp. 34:1--34:10. | |
import time | |
import os | |
import sys |
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#!/bin/bash | |
tmup () | |
{ | |
echo -n "Updating to latest tmux environment..."; | |
export IFS=","; | |
for line in $(tmux showenv -t $(tmux display -p "#S") | tr "\n" ","); | |
do | |
if [[ $line == -* ]]; then | |
unset $(echo $line | cut -c2-); |
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// It is important to declare your variables. | |
(function() { | |
var foo = 'Hello, world!'; | |
print(foo); //=> Hello, world! | |
})(); | |
// Because if you don't, the become global variables. | |
(function() { |
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