Linear SVM with Stochastic Gradient Descent

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Learns a linear SVM trained with stochastic gradient descent, based on the implementation in scikit-learn, for classification of binary-labeled data.   Can be used for training or testing, depending on the arguments passed in: For training: takes in a data collection file path to write a trained model to and two other data collection paths to read data and labels in from, along with hyperparameter settings (in order: regularization coefficient, initial learning rate, learning rate decay parameter, and number of iterations of stochastic gradient descent).  Writes the trained model to the file at the specified data collection path.   For testing: takes in one path to a file in a data collection that contains a trained model to use, another file path to a test data set, and a third file path to which to write predictions.  

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1. Type your input

2. See the result

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3. Use this algorithm

curl -X POST -d '{{input | formatInput:"curl"}}' -H 'Content-Type: application/json' -H 'Authorization: Simple YOUR_API_KEY' https://api.algorithmia.com/v1/algo/mheimann/LinearSVMwithStochasticGradientDescent/0.1.0
View cURL Docs
algo auth
# Enter API Key: YOUR_API_KEY
algo run algo://mheimann/LinearSVMwithStochasticGradientDescent/0.1.0 -d '{{input | formatInput:"cli"}}'
View CLI 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://mheimann/LinearSVMwithStochasticGradientDescent/0.1.0");
AlgoResponse result = algo.pipeJson(input);
System.out.println(result.asJsonString());
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://mheimann/LinearSVMwithStochasticGradientDescent/0.1.0")
val result = algo.pipeJson(input)
System.out.println(result.asJsonString)
View Scala Docs
var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://mheimann/LinearSVMwithStochasticGradientDescent/0.1.0")
           .pipe(input)
           .then(function(output) {
             console.log(output);
           });
View Javascript Docs
var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://mheimann/LinearSVMwithStochasticGradientDescent/0.1.0")
           .pipe(input)
           .then(function(response) {
             console.log(response.get());
           });
View NodeJS Docs
import Algorithmia

input = {{input | formatInput:"python"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('mheimann/LinearSVMwithStochasticGradientDescent/0.1.0')
print algo.pipe(input)
View Python Docs
library(algorithmia)

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("mheimann/LinearSVMwithStochasticGradientDescent/0.1.0")
result <- algo$pipe(input)$result
print(result)
View R Docs
require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('mheimann/LinearSVMwithStochasticGradientDescent/0.1.0')
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('mheimann/LinearSVMwithStochasticGradientDescent/0.1.0');
let response = algo.pipe(input);
View Rust Docs
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