paolorossi

/*
 * Inspired by: http://stackoverflow.com/questions/4360060/video-streaming-with-html-5-via-node-js
 */

var http = require('http'),
    fs = require('fs'),
    util = require('util');

http.createServer(function (req, res) {
  var path = 'video.mp4';

alexalemi

import math
class Welford(object):
    """ Implements Welford's algorithm for computing a running mean
    and standard deviation as described at: 
        http://www.johndcook.com/standard_deviation.html

    can take single values or iterables

    Properties:
        mean    - returns the mean

glamp

import csv

def trycast(x):
    try:
        return float(x)
    except:
        try:
            return int(x)
        except:
            return x

bsweger

Useful Pandas Snippets

A personal diary of DataFrame munging over the years.

Data Types and Conversion

Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)

PetrochukM

"""
We implement additional hyperparameter optimization methods not present in
https://scikit-optimize.github.io/.

Gist: https://gist.github.com/Deepblue129/2c5fae9daf0529ed589018c6353c9f7b
"""

import math
import logging
import random

logandillard

import math
import numpy as np
from sklearn.linear_model import Ridge


class LinearModelTree:
    def __init__(self, min_node_size, node_model_fit_func, min_split_improvement=0):
        self.min_node_size = min_node_size
        self.node_model_fit_func = node_model_fit_func
        self.min_split_improvement = min_split_improvement

jayspeidell

# Info on how to get your api key (kaggle.json) here: https://github.com/Kaggle/kaggle-api#api-credentials
!pip install kaggle
api_token = {"username":"USERNAME","key":"API_KEY"}
import json
import zipfile
import os
with open('/content/.kaggle/kaggle.json', 'w') as file:
    json.dump(api_token, file)
!chmod 600 /content/.kaggle/kaggle.json
!kaggle config path -p /content

gdsaxton

vdalv

Convert PascalVOC Annotations to YOLO

This script reads PascalVOC xml files, and converts them to YOLO txt files.

Note: This script was written and tested on Ubuntu. YMMV on other OS's.

Disclaimer: This code is a modified version of Joseph Redmon's voc_label.py

Instructions:

1. Place the convert_voc_to_yolo.py file into your data folder.

twiecki

import dask
import dask.array as da
import dask.dataframe as dd
import sparse

@dask.delayed(pure=True)
def corr_on_chunked(chunk1, chunk2, corr_thresh=0.9):
    return sparse.COO.from_numpy((np.dot(chunk1, chunk2.T) > corr_thresh))
        
def chunked_corr_sparse_dask(data, chunksize=5000, corr_thresh=0.9):