learn.js

const t = tf.tensor([1, 2, 3])

var trainingSet = []

function addToTrainingSet(set){
    trainingSet.push(set);
}


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function trainWithPuck(rink){
    // Generate some synthetic data for training.
    var input = [];
    var output = [];
    var cnt = 0;
    for(i in ts1){
        input = input.concat([ts1[i]['puck.x']/rink.width, ts1[i]['puck.y']/rink.heigth]);
        output = output.concat([ts1[i]['own c.x']/rink.width, ts1[i]['own c.y']/rink.heigth]);
        cnt++;
    }
    // Define a model for linear regression.
    const activation = 'relu';
    const model = tf.sequential({
        layers: [
            tf.layers.dense({units: 2, inputShape:[2], activation: activation}),
            tf.layers.dense({units: 2, activation: activation}),
        //   tf.layers.dense({units: 2, activation: activation})
        ]
    });
    model.compile({loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
    console.log(model.summary());
    const xs = tf.tensor2d(input, [cnt, 2]);
    const ys = tf.tensor2d(output, [cnt, 2]);
    // Train the model using the data.
    model.fit(xs, ys, {epochs: 500}).then(info => {
        console.log(info.history)
        var pred = model.predict(tf.tensor2d([124,279],[1,2]));
        pred[0] *= rink.width;
        pred[1] *= rink.heigth;
        pred.print();
        console.log(pred);
    });
    return model;
}

function trainWithPuckAll(){
    // Generate some synthetic data for training.
    var input = [];
    var output = [];
    var cnt = 0;
    for(i in ts1){
        input = input.concat([ts1[i]['puck.x'], ts1[i]['puck.y']]);
        output = output.concat([ts1[i]['own g.x'], ts1[i]['own g.y'],ts1[i]['own c.x'], ts1[i]['own c.y'],ts1[i]['own lf.x'], ts1[i]['own lf.y'],ts1[i]['own rf.x'], ts1[i]['own g.y'],ts1[i]['own g.x'], ts1[i]['own g.y'],ts1[i]['own g.x'], ts1[i]['own g.y'],
                                ts1[i]['opp g.x'], ts1[i]['opp g.y'],ts1[i]['opp c.x'], ts1[i]['opp c.y'],ts1[i]['opp lf.x'], ts1[i]['opp lf.y'],ts1[i]['opp rf.x'], ts1[i]['opp g.y'],ts1[i]['opp g.x'], ts1[i]['opp g.y'],ts1[i]['opp g.x'], ts1[i]['opp g.y']]);
        cnt++;
    }
    // Define a model for linear regression.
    const activation = 'linear';
    const model = tf.sequential({
        layers: [
            tf.layers.dense({units: 24, inputShape:[2], activation: activation})//,
//            tf.layers.dense({units: 48, activation: activation}),
 //           tf.layers.dense({units: 24, activation: activation})
        ]
    });
    model.compile({loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy']});
    console.log(model.summary());
    const xs = tf.tensor2d(input, [cnt, 2]);
    const ys = tf.tensor2d(output, [cnt, 24]);
    // Train the model using the data.
    model.fit(xs, ys, {epochs: 100}).then(info => {
        console.log(info.history)
        var pred = model.predict(tf.tensor2d([175,120],[1,2]));
        pred.print();
        console.log(pred);
    });
    return model;
}