In [24]:
# Pkg.add("RDatasets")
using RDatasets
iris = dataset("datasets", "iris")
Out[24]:
Use Clustering.jl to cluster using the SepalLength, PetalLength, and PetalWidthfeatures via K-means clustering. Make a scatter plot of the resulting clusters.
Hint: You will need to index the dataframe, convert it to an array, and transpose it. In addition, you will need to use the assignments field of the return to get the cluster assignments.
For the the example presented here, we will use a subhset of Word Embedding, trained using Word2Vec.jl. These are 100 dimentional vectors, which encode syntactic and semantic information about words.
In [ ]:
using Embeddings
countries = ["Afghanistan", "Algeria", "Angola", "Arabia", "Argentina", "Australia", "Bangladesh", "Brazil", "Britain", "Canada", "China", "Colombia", "Congo", "Egypt", "England", "Ethiopia", "France", "Germany", "Ghana", "India", "Indonesia", "Iran", "Iraq", "Ireland", "Italy", "Japan", "Kenya", "Korea", "Madagascar", "Malaysia", "Mexico", "Morocco", "Mozambique", "Myanmar", "Nepal", "Nigeria", "Pakistan", "Peru", "Philippines", "Poland", "Russia", "South", "Spain", "Sudan", "Tanzania", "Thailand", "Uganda", "Ukraine", "Usa", "Uzbekistan", "Venezuela", "Vietnam", "Wales", "Yemen"]
usa_cities = ["Albuquerque", "Atlanta", "Austin", "Baltimore", "Boston", "Charlotte", "Chicago", "Columbus", "Dallas", "Denver", "Detroit", "Francisco", "Fresno", "Houston", "Indianapolis", "Jacksonville", "Las", "Louisville", "Memphis", "Mesa", "Milwaukee", "Nashville", "Omaha", "Philadelphia", "Phoenix", "Portland", "Raleigh", "Sacramento", "San", "Seattle", "Tucson", "Vegas", "Washington"]
world_capitals = ["Accra", "Algiers", "Amman", "Ankara", "Antananarivo", "Athens", "Baghdad", "Baku", "Bangkok", "Beijing", "Beirut", "Berlin", "Bogotá", "Brasília", "Bucharest", "Budapest", "Cairo", "Caracas", "Damascus", "Dhaka", "Hanoi", "Havana", "Jakarta", "Kabul", "Kampala", "Khartoum", "Kinshasa", "Kyiv", "Lima", "London", "Luanda", "Madrid", "Manila", "Minsk", "Moscow", "Nairobi", "Paris", "Pretoria", "Pyongyang", "Quito", "Rabat", "Riyadh", "Rome", "Santiago", "Seoul", "Singapore", "Stockholm", "Taipei", "Tashkent", "Tehran", "Tokyo", "Vienna", "Warsaw", "Yaoundé"]
animals = ["alpaca","camel","cattle","dog","dove","duck","ferret","goldfish","goose","rat","llama","mouse","pigeon","yak"]
sports = ["archery","badminton","basketball","boxing","cycling","diving","equestrian","fencing","field","football","golf","gymnastics","handball","hockey","judo","kayak","pentathlon","polo","rowing","rugby","sailing","shooting","soccer","swimming","taekwondo","tennis","triathlon","volleyball","weightlifting","wrestling"]
words_by_class = [countries, usa_cities, world_capitals, animals, sports]
all_words = reduce(vcat, words_by_class)
embedding_table = load_embeddings(Word2Vec; keep_words = all_words)
@assert Set(all_words) == Set(embedding_table.vocab)
embeddings = embedding_table.embeddings
all_words = embedding_table.vocab
classes = map(all_words) do word
findfirst(col -> word ∈ col, [countries, usa_cities, world_capitals, animals, sports])
end;
You can download the datased from here, and load it up with JLD as shown below. (or just load it directly if you have cloned the notebooks)
Done finely and it will seperate ball-sports from other sports, and will seperate locations according to regions, etc
Affinity propagraion requires a similarity matrix, which you can set as a negated distance matrix.