vrld

#!/bin/sh

ACTIVE_WINDOW=$(xdotool getactivewindow)

if [ -z "${1}" ]; then
  xdotool search --classname Navigator windowactivate --sync key --clearmodifiers "ctrl+F5"
else
  xdotool search --classname Navigator search --name $1 windowactivate --sync key --clearmodifiers "ctrl+F5"
fi

vrld

#!/bin/sh

usage() {
  echo "Usage: marknote path"
  exit -1
}

FILE=$1
[ -z "${FILE}" ] && usage

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def covar_2d(e, s):
  """Returns a 2D covariance matrix with main diagonal in direction e and second diagonal perpendicular to e with proportional length s.

  Params:
  -------
  e: array-like
    Direction of the main diagonal.
  s: float
    Proportional scale of the second diagonal.

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import requests
from collections import namedtuple

class Dirble(object):
    Category = namedtuple("Category", "id name description")
    Station = namedtuple("Station", "id name streamurl country bitrate status")
    StationDetails = namedtuple("StationDetails", "id name streamurl country bitrate status description added urlid website songhistory")
    Song = namedtuple("Song", "artist title")

    def __init__(self, key, base_url):

vrld

function chol_(A::Matrix, L::Matrix)
    for i = 1:size(A,1)
        s = 0
        @simd for j = 1:i-1
            @inbounds L[i,j] = (A[i,j] - sum([L[i,k]*L[j,k] for k = 1:j-1])) / L[j,j]
            @inbounds s += L[i,j]^2
        end
        L[i,i] = sqrt(A[i,i] - s)
    end
    L

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type Perceptron
	w::Vector{Float64}
	Perceptron(d::Int) = new(zeros(d))
	Perceptron(w::Vector{Float64}) = new(w)
end

type Winnow
	w::Vector{Float64}
	η::Float64
	Perceptron(d::Int, η::Float64) = new(ones(d)./d, η)

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function forward_difference(c::Matrix)
	out = zeros(c)
	@simd for i = 1:size(c,1)-1
		@inbounds out[i,:] = c[i+1,:] - c[i,:]
	end
	@inbounds out[size(c,1),:] = c[1,:] - c[size(c,1),:]
	out
end

function at(c::Matrix, t::Number, Δc::Matrix, cumlen::Vector{Float64})

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module Crossvalidation

export KFold, StratifiedKFold, LOO, RandomSplit

#==
K-Fold cross validation

Example (5-Fold CV):
	KFold(X, y, 5) do X_tr, y_tr, X_te, y_te
		train!(model, X_tr, y_tr)

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-- Euclidean rythm (http://en.wikipedia.org/wiki/Euclidean_Rhythm)
function rythm(k,n)
    local r = {}
    for i = 1,n do
        r[i] = {i <= k}
    end
    
    local function cat(i,k)
        for _,v in ipairs(r[k]) do
            r[i][#r[i]+1] = v

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#include <omp.h>
#include <vector>
#include <complex>
#include <iostream>
#include <fstream>
#include <sstream>
#include <exception>
#include <string>
#include <cstdint>