endolith

function [maxtab, mintab]=peakdet(v, delta, x)
%PEAKDET Detect peaks in a vector
%        [MAXTAB, MINTAB] = PEAKDET(V, DELTA) finds the local
%        maxima and minima ("peaks") in the vector V.
%        MAXTAB and MINTAB consists of two columns. Column 1
%        contains indices in V, and column 2 the found values.
%      
%        With [MAXTAB, MINTAB] = PEAKDET(V, DELTA, X) the indices
%        in MAXTAB and MINTAB are replaced with the corresponding
%        X-values.

endolith

from __future__ import division
from numpy.fft import rfft
from numpy import argmax, mean, diff, log, nonzero
from scipy.signal import blackmanharris, correlate
from time import time
import sys
try:
    import soundfile as sf
except ImportError:
    from scikits.audiolab import flacread

sixtenbe

#!/usr/bin/python2


# Copyright (C) 2016 Sixten Bergman
# License WTFPL
#
# This program is free software. It comes without any warranty, to the extent
# permitted by applicable law. 
# You can redistribute it and/or modify it under the terms of the Do What The
# Fuck You Want To Public License, Version 2, as published by Sam Hocevar. See

fabianp

import numpy as np
from scipy import linalg, optimize

MAX_ITER = 100


def group_lasso(X, y, alpha, groups, max_iter=MAX_ITER, rtol=1e-6,
             verbose=False):
    """
    Linear least-squares with l2/l1 regularization solver.

kevinhughes27

#include "FlyCapture2.h"

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>

#include <iostream>

using namespace FlyCapture2;

int main()

st4lk

"""
Settings for root logger.
Log messages will be printed to console and also to log file (rotated, with
specified size). All log messages from used libraries will be also handled.

Three approaches for defining logging settings are used:
1. using logging classes directly (py25+, py30+)
2. using fileConfig (py26+, py30+)
3. using dictConfig (py27+, py32+)
Choose any variant as you like, but keep in mind python versions, that

erogol

import numpy as np
import numpy
import theano
import theano.tensor as T
from theano import function, config, shared, sandbox
from theano import ProfileMode
from sklearn import cluster, datasets
import matplotlib.pyplot as plt

def rsom(data, cluster_num, alpha, epochs = -1, batch = 1, verbose = False):   

simono

#!/bin/zsh
#
# Highlight a given file and copy it as RTF.
#
# Simon Olofsson <simon@olofsson.de>
#

set -o errexit
set -o nounset

lmullen

SLIDES := $(patsubst %.md,%.md.slides.pdf,$(wildcard *.md))
HANDOUTS := $(patsubst %.md,%.md.handout.pdf,$(wildcard *.md))

all : $(SLIDES) $(HANDOUTS)

%.md.slides.pdf : %.md
	pandoc $^ -t beamer --slide-level 2 -o $@

%.md.handout.pdf : %.md
	pandoc $^ -t beamer --slide-level 2 -V handout -o $@ 

GaelVaroquaux

'''
Non-parametric computation of entropy and mutual-information

Adapted by G Varoquaux for code created by R Brette, itself
from several papers (see in the code).

This code is maintained at https://github.com/mutualinfo/mutual_info 
Please download the latest code there, to have improvements and 
bug fixes.