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Mindaugas Zickus mindis

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mindis / infiniteBacon-finished.html
Created October 8, 2015 11:12 — forked from SimplGy/infiniteBacon-finished.html
An infinite scroller built with Bacon using FRP techniques
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<title>FRP Infinite Scroll using Bacon.js</title>
<script src="//cdnjs.cloudflare.com/ajax/libs/bacon.js/0.7.53/Bacon.min.js"></script>
<script src="//cdnjs.cloudflare.com/ajax/libs/lodash.js/3.7.0/lodash.min.js"></script>
<style type="text/css">
body {
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mindis / spark_parallel_boost.py
Created October 1, 2015 13:32 — forked from wpm/spark_parallel_boost.py
A simple example of how to integrate the Spark parallel computing framework and the scikit-learn machine learning toolkit. This script randomly generates test and train data sets, trains an ensemble of decision trees using boosting, and applies the ensemble to the test set. The ensemble training is done in parallel.
from pyspark import SparkContext
import numpy as np
from sklearn.cross_validation import train_test_split, Bootstrap
from sklearn.datasets import make_classification
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
def run(sc):
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mindis / gist:01b68eeefb7a6f0d66a4
Last active August 29, 2015 14:26 — forked from invkrh/gist:05a83be081c1f713e15b
MLLIB ALS evaluation using Mean Average Precision (MAP) and Expected Percentile Rank (EPR)
def meanAveragedPrecision(k: Int, model: MatrixFactorizationModel, ratings: RDD[Rating]): Double = {
def avgPrecisionK(actual: Seq[Int], predicted: Seq[Int], k: Int): Double = {
val predK = predicted.take(k)
var score = 0.0
var numHits = 0.0
for ((p, i) <- predK.zipWithIndex) {
if (actual.contains(p) && !predK.take(i).contains(p)) {
numHits += 1.0
score += numHits / (i.toDouble + 1.0)
}
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mindis / Program.cs
Last active August 29, 2015 14:23 — forked from ovrmrw/Program.cs
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Web.Http;
using System.Web.Http.Tracing;
using System.Reflection;
using Owin;
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mindis / gist:fdda179e5916b9e7d11e
Last active August 29, 2015 14:22 — forked from debasishg/gist:8172796
  1. General Background and Overview
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mindis / gist:ee8f371a0d5a4182a68e
Last active August 29, 2015 14:22 — forked from debasishg/gist:8172796
  1. General Background and Overview
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mindis / README.md
Last active August 29, 2015 14:17 — forked from jakevdp/README.md

Numba Ball Tree

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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mindis / sql.export.gbm.R
Last active August 29, 2015 14:14 — forked from shanebutler/sql.export.gbm.R
# sql.export.gbm(): save a GBM model as SQL
# v0.11
# Copyright (c) 2013-2014 Shane Butler <shane dot butler at gmail dot com>
#
# sql.export.gbm is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# sql.export.gbm is distributed in the hope that it will be useful, but
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mindis / sql.export.randomForest.R
Last active August 29, 2015 14:14 — forked from shanebutler/sql.export.randomForest.R
# sql.export.rf(): save a randomForest model as SQL
# v0.03
# Copyright (c) 2013-2014 Shane Butler <shane dot butler at gmail dot com>
#
# sql.export.rf is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# sql.export.rf is distributed in the hope that it will be useful, but
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mindis / stream_twitter_tutorial.md
Last active August 29, 2015 14:12 — forked from tbarbugli/stream_twitter_tutorial.md

Build a scalable Twitter clone with Django and GetStream.io

In this tutorial we are going to build a Twitter clone using Django and GetStream.io, a hosted API for newsfeed development. We will show you how easy is to power your newsfeeds with GetStream.io. At the end of this tutorial we will have a Django app with a profile feed, a timeline feed, support for following users, hashtags and mentions.

I assume that you are familiar with Django. If you're new to Django the [official tutorial] (https://docs.Djangoproject.com/en/1.7/intro/install/) explains it very well.

Bootstrap the Django application