melvincabatuan

import itertools 

def jaccard(labels1, labels2):
    """
    Computes the Jaccard similarity between two sets of clustering labels.

    The value returned is between 0 and 1, inclusively. A value of 1 indicates
    perfect agreement between two clustering algorithms, whereas a value of 0
    indicates no agreement. For details on the Jaccard index, see:
    http://en.wikipedia.org/wiki/Jaccard_index

melvincabatuan

0 input_1
1 stem_conv1
2 stem_bn1
3 activation_1
4 reduction_conv_1_stem_1
5 reduction_bn_1_stem_1
6 activation_2
7 activation_4
8 separable_conv_1_reduction_left1_stem_1
9 separable_conv_1_reduction_1_stem_1

melvincabatuan

Studying for a Tech Interview Sucks, so Here's a Cheat Sheet to Help

This list is meant to be a both a quick guide and reference for further research into these topics. It's basically a summary of that comp sci course you never took or forgot about, so there's no way it can cover everything in depth. It also will be available as a gist on Github for everyone to edit and add to.

Data Structure Basics

###Array ####Definition:

• Stores data elements based on an sequential, most commonly 0 based, index.
• Based on tuples from set theory.

melvincabatuan

Below are the Big O performance of common functions of different Java Collections.


List                 | Add  | Remove | Get  | Contains | Next | Data Structure
---------------------|------|--------|------|----------|------|---------------
ArrayList            | O(1) |  O(n)  | O(1) |   O(n)   | O(1) | Array
LinkedList           | O(1) |  O(1)  | O(n) |   O(n)   | O(1) | Linked List
CopyOnWriteArrayList | O(n) |  O(n)  | O(1) |   O(n)   | O(1) | Array

melvincabatuan

import org.opencv.core.*;
import org.opencv.highgui.Highgui;
import org.opencv.highgui.VideoCapture;
import processing.core.PApplet;
import processing.core.PConstants;
import processing.core.PImage;
import javax.imageio.ImageIO;
import java.awt.image.BufferedImage;
import java.io.ByteArrayInputStream;
import java.io.InputStream;

melvincabatuan

/*
 * Build command:
 * g++ -o opencvni opencvni.cpp `pkg-config --cflags --libs opencv`
*/

#include "opencv2/videoio/videoio.hpp"
#include "opencv2/highgui/highgui.hpp"

#include <iostream>

melvincabatuan

I've been interested in computer vision for a long time, but I haven't had any free time to make any progress until this holiday season. Over Christmas and the New Years I experimented with various methodologies in OpenCV to detect road signs and other objects of interest to OpenStreetMap. After some failed experiments with thresholding and feature detection, the excellent /r/computervision suggested using the dlib C++ module because it has more consistently-good documentation and the pre-built tools are faster.

After a day or two figuring out how to compile the examples, I finally made some progress:

Compiling dlib C++ on a Mac with Homebrew

1. Clone dlib from Github to your local machine:

melvincabatuan

#!/bin/bash

# Bash script to install latest version of ffmpeg and its dependencies on Ubuntu 12.04 or 14.04
# Inspired from https://gist.github.com/faleev/3435377

# Remove any existing packages:
sudo apt-get -y remove ffmpeg x264 libav-tools libvpx-dev libx264-dev

# Get the dependencies (Ubuntu Server or headless users):
sudo apt-get update