npm -v and node -vIf you want to develop some Javascript the needs to run in a browser and want to make use of the NodeJS development and package ecosystem, much of which involves code that is incompatible with a browser, then you'll need a way to transform browser-incompatible code into compatible code.
| # | |
| # Solves the 'Jug Problem' from RMIT AI Sem 1 2018 Tutorial 1 | |
| # | |
| # The state space graph for this problem contains multiple cycles, so we | |
| # can't rely on a naive search fully exploring the graph without getting stuck in a cycle | |
| # | |
| # In order to combat the cycles, I introduce some randomness into operation choices and | |
| # set a maximum search depth that well exceeds the known depth of the most efficient solution | |
| # NOTE: This is rough code. It's hashed out and it's ugly. |
| [user] | |
| email = [email protected] | |
| name = Jonathon Belotti | |
| [core] | |
| editor = vim | |
| whitespace = fix,-indent-with-non-tab,trailing-space,cr-at-eol | |
| excludesfile = ~/.gitignore | |
| [web] | |
| browser = google-chrome |
It's quite hard to look across some code you've written, and pick something out to show; Like most programmers, I really only found in past my work, things to fix and change. Had to pick something though, so I'll show simplegraphdb, which I something I wrote relatively recently (mid November 2017) in preparation for being a Golang programmer at Atlassian.
It's quite small, and self-contained. I had been interested for quite a while in graph databases, as I like the kinds of questions a graph database allows you to ask efficiently about certain kinds of data. They are also not something explored really at university, the relation-model dominating my education thus far.
I approached this project as a thing that could serve as an my introduction to graph database thinking. I wanted to keep things nice, simple, and elegant and found the "Hexastore" indexing model to be exactly this. It can be implemented by building and linking m
| def coinProblem(coins, total): | |
| dp = [0] * (total + 1) | |
| coins.sort() | |
| for subtotal in range(1, total + 1): | |
| best = float('inf') | |
| for c in coins: | |
| if c <= subtotal: | |
| option = 1 + dp[subtotal - c] |
| def unbounded_knapsack(items, C): | |
| """ | |
| items List[item], where 'item' is a named tuple -> (val=4, cost=10) | |
| C int, where 'C' stands for capacity | |
| Note: This implementation also stores the optimal group of items at each capacity value | |
| """ | |
| dp = [(0, [])] * (C + 1) | |
| used = [] |
| class Heap(): | |
| def __init__(self): | |
| self.data = [None] | |
| self.size = 0 | |
| def insert(self, n): | |
| self.size += 1 | |
| self.data.append(n) | |
| self._swim(self.size) | |
| class Trie(object): | |
| class Node(object): | |
| def __init__(self): | |
| self.val = 0 | |
| self.children = {} | |
| def __init__(self): | |
| """ | |
| Initialize your data structure here. | |
| """ |