Geographic Spectral Clustering

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Spectral clustering for geographic (lat/long) data. Input is json for a python dictionary containing keys "data" - whose value is a list of lat/long pairs "numClusters" - whose value is an integer denoting the number of clusters that the data will be partitioned into. The output is an ordered list containing the cluster label of each point. We use inverse distance (in km, as calculated by the Haversine formula) for similarity, so close points are more similar. Any points within about a meter of each other are counted as the same point. We cannot guarantee the accuracy of Haversine distances on very nearby points, so be careful. The advantage of spectral clustering is that is does not depend on cluster centers, like K-means, and so can resolve clusters that are naturally non-convex. This is based on scikit-learn's spectral clustering implementation . Read more about spectral clustering here

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curl -X POST -d '{{input | formatInput:"curl"}}' -H 'Content-Type: application/json' -H 'Authorization: Simple YOUR_API_KEY'
View cURL Docs
algo auth
algo run algo://sklearn/GeographicSpectralClustering/0.1.4 -d '{{input | formatInput:"cli"}}'
View CLI Docs
import (
  algorithmia ""

input := {{input | formatInput:"go"}}

var client = algorithmia.NewClient("YOUR_API_KEY", "")
algo, _ := client.Algo("algo://sklearn/GeographicSpectralClustering/0.1.4")
resp, _ := algo.Pipe(input)
response := resp.(*algorithmia.AlgoResponse)
View Go Docs
import com.algorithmia.*;
import com.algorithmia.algo.*;

String input = "{{input | formatInput:"java"}}";
AlgorithmiaClient client = Algorithmia.client("YOUR_API_KEY");
Algorithm algo = client.algo("algo://sklearn/GeographicSpectralClustering/0.1.4");
AlgoResponse result = algo.pipeJson(input);
View Java Docs
import com.algorithmia._
import com.algorithmia.algo._

val input = {{input | formatInput:"scala"}}
val client = Algorithmia.client("YOUR_API_KEY")
val algo = client.algo("algo://sklearn/GeographicSpectralClustering/0.1.4")
val result = algo.pipeJson(input)
View Scala Docs
var input = {{input | formatInput:"javascript"}};
           .then(function(output) {
View Javascript Docs
var input = {{input | formatInput:"javascript"}};
           .then(function(response) {
View NodeJS Docs
import Algorithmia

input = {{input | formatInput:"python"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('sklearn/GeographicSpectralClustering/0.1.4')
print algo.pipe(input)
View Python Docs

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("sklearn/GeographicSpectralClustering/0.1.4")
result <- algo$pipe(input)$result
View R Docs
require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('sklearn/GeographicSpectralClustering/0.1.4')
puts algo.pipe(input).result
View Ruby Docs
use algorithmia::*;

let input = {{input | formatInput:"rust"}};
let client = Algorithmia::client("YOUR_API_KEY");
let algo = client.algo("sklearn/GeographicSpectralClustering/0.1.4");
let response = algo.pipe(input);
View Rust Docs
import Algorithmia

let input = "{{input | formatInput:"swift"}}";
let client = Algorithmia.client(simpleKey: "YOUR_API_KEY")
let algo = client.algo(algoUri: "sklearn/GeographicSpectralClustering/0.1.4") { resp, error in
View Swift Docs
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