averaged-perceptron
v3.0.0
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A linear classifier with the averaged perceptron algorithm
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averaged-perceptron
A linear classifier with the averaged perceptron algorithm
Installing
npm install averaged-perceptron
Using
A simple (and unrealistic) example:
import averagedPerceptron from "averaged-perceptron";
const { predict, update } = averagedPerceptron();
const trainingDataset = [
[{ height: 4, width: 2 }, "slim"],
[{ height: 2, width: 4 }, "fat"],
[{ height: 1, width: 4 }, "fat"],
[{ height: 2, width: 2.1 }, "fat"],
[{ height: 2.1, width: 2 }, "slim"],
[{ height: 2, width: 1 }, "slim"],
[{ height: 1, width: 2 }, "fat"],
[{ height: 1, width: 1.1 }, "fat"],
[{ height: 1.1, width: 1 }, "slim"],
[{ height: 4, width: 1 }, "slim"],
];
const epochs = 1000;
for (let epoch = 0; epoch < epochs; epoch += 1) {
const shuffledDataset = shuffle(trainingDataset); // Any Fisher–Yates shuffle
shuffledDataset.forEach(([features, label]) => update(features, label));
}
predict({ height: 8, width: 2 }); // => "slim"
predict({ height: 2.1, width: 2 }); // => "slim"
predict({ height: 2, width: 2.1 }); // => "fat"
predict({ height: 2, width: 8 }); // => "fat"
A slightly more realistic example using the Iris dataset can be found in the tests.
API
averagedPerceptron([weights [, iterations]])
Returns a perceptron object. It may be initialized with weights
, an object of objects with the weight of each feature-label pair. When initialized with weights
, the number of iterations used to obtain them are iterations
, or 0
by default.
import averagedPerceptron from "averaged-perceptron";
// Create a new perceptron
const { predict, update, weights } = averagedPerceptron();
If you want to train the model in multiple sessions, you may resume training by specifying the iterations
, which is the number of times update()
was called to obtain the weights. That way new update()
calls are properly averaged against the pretrained weights
.
import averagedPerceptron from "averaged-perceptron";
// Create a perceptron from pretrained weights to do further training
const weightsJSON = '{"x":{"a":0.4,"b":0.6},"y":{"a":0.8,"b":-0.4}}';
const weights = JSON.parse(weightsJSON);
const iterations = 1000; // weights obtained with 1000 update() calls
const { predict, update, weights } = averagedPerceptron(weights, iterations);
// Keep training by calling update()
predict(features)
Returns the label predicted from the values in features
, or ""
if none exists.
import averagedPerceptron from "averaged-perceptron";
const { predict } = averagedPerceptron({
x: { a: 0.4, b: 0.6 },
y: { a: 0.8, b: -0.4 },
});
predict({ x: 1, y: 1 }); // => "a"
update(features, label [, guess])
Returns the perceptron, updating its weights with the respective values in features
if label
does not equal guess
. If guess
is not given, it defaults to the output of predict(features)
.
import averagedPerceptron from "averaged-perceptron";
const { update } = averagedPerceptron();
update({ x: 1, y: 1 }, "a");
Note that update()
may be given feature-label pairs whose weights have not been preinitialized, so the model may be used for online learning when the features or labels are unknown a priori.
weights()
Returns an object of objects with the weight of each feature-label pair.
import averagedPerceptron from "averaged-perceptron";
const { weights } = averagedPerceptron({
x: { a: 0.4, b: 0.6 },
y: { a: 0.8, b: -0.4 },
});
weights(); // => { x: { a: 0.4, b: 0.6 }, y: { a: 0.8, b: -0.4 } }
Note that the weights are stored as an object of objects, because this perceptron is optimized for sparse features.