Kulbear

Open API Specification (fka Swagger Specification) tutorial

All these Gist files are explained on my Open API Specification (fka Swagger Specification) tutorial on API Handyman blog.

The tutorial

This tutorial is composed of several posts, here are the posts links and files used for each one:

• Part 1: Introduction
  • A JSON specification
  • A YAML specification
• Part 2: The basics

Kulbear

function goTop (acc, t) {
  const acceleration = acc || 0.1
  const time = t || 16

  const document = window.document

  let x1 = 0
  let y1 = 0
  let x2 = 0
  let y2 = 0

Kulbear

#!/usr/bin/python
# -*- coding: utf-8 -*-

# Authors: Ji Yang <jyang7@ualberta.ca>
# License: MIT
# Version: 1.0.0
# Last Updated: May 14, 2017

import random
import sys

Kulbear

function [output]=simple_nlm(input,t,f,h1,h2,selfsim)
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %
 %  input   : image to be filtered
 %  t       : radius of search window
 %  f       : radius of similarity window
 %  h1,h2   : w(i,j) = exp(-||GaussFilter(h1) .* (p(i) - p(j))||_2^2/h2^2)
 %  selfsim : w(i,i) = selfsim, for all i
 %

Kulbear

#/usr/bin/env python
# coding=utf-8

import random
import re


def color(messages):
    color = '\x1B[%d;%dm' % (1,random.randint(30,37))
    return '%s %s\x1B[0m' % (color,messages)

Kulbear

# Uses python3
import sys
from collections import namedtuple

Segment = namedtuple('Segment', 'start end')


def optimal_points(segments):
    points = []
    segments_by_end = sorted(segments, key=lambda x: x.end)

Kulbear

% Author: Ji Yang
%% Description
% A simple implementation of histogram equalization.
%

%% Note
% Histogram equalization is a technique for adjusting image intensities 
% to enhance contrast.
%

Kulbear

from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets
import numpy as np
import timeit
import tensorflow as tf
from pprint import pformat

mnist = read_data_sets("data", one_hot=False)

NUM_CLASS = 10
STEP = 200

Kulbear

import tensorflow as tf
import numpy as np
import os

import zconfig
import utils

class DenoisingAutoencoder(object):

    """ Implementation of Denoising Autoencoders using TensorFlow.

Kulbear

class Node:
    def __init__(self, val):
        self.val = val
        self.next = None


class MyLinkedList:
    def __init__(self):
        """
        Initialize your data structure here.