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/* eslint no-shadow: 0 */
const PerceptronModel = require("../").PerceptronModel;const test = require("tap").test;
test("perceptron", function (t) {    t.test("initializes to zeros if label is zero", function (t) {        const p = new PerceptronModel();        p.train([1, 2, 3], 0);        t.same(p.weights, [0, 0, 0]);        t.equal(p.bias, 0);        t.end();    });
    t.test("initializes to values if label is one", function (t) {        const p = new PerceptronModel();        p.train([1, 2, 3], 1);        t.same(p.weights, [1, 2, 3]);        t.equal(p.bias, 1);        t.end();    });
    t.test("base case of zero prediction features", function (t) {        const p = new PerceptronModel();        p.train([1, 2, 3], 1);        t.same(p.predict([]), null);        t.end();    });
    t.test("train with invalid label", function (t) {        const p = new PerceptronModel();        t.same(p.train([1, 2, 3], 0.5), null);        t.end();    });
    t.test("learns to separate one from two", function (t) {        const p = new PerceptronModel();        for (let i = 0; i < 4; i++) {            p.train([1], 0);            p.train([2], 1);        }        t.equal(p.predict([1]), 0);        t.equal(p.predict([2]), 1);        t.end();    });
    t.test("learns a diagonal boundary", function (t) {        const p = new PerceptronModel();        for (let i = 0; i < 5; i++) {            p.train([1, 1], 1);            p.train([0, 1], 0);            p.train([1, 0], 0);            p.train([0, 0], 0);        }        t.equal(p.predict([0, 0]), 0);        t.equal(p.predict([0, 1]), 0);        t.equal(p.predict([1, 0]), 0);        t.equal(p.predict([1, 1]), 1);        t.end();    });    t.end();});