Examples with Matrix org.apache.ignite.ml.math.primitives.matrix.Matrix used on opensource projects

Example 21 with Matrix

use of org.apache.ignite.ml.math.primitives.matrix.Matrix in project ignite by apache.

the class MultilayerPerceptron method differentiateNonlinearity.

/**
 * Differentiate non-linearity.
 *
 * @param linearOut Linear output of current layer.
 * @param nonlinearity Non-linearity of current layer.
 * @return Gradients matrix.
 */
private Matrix differentiateNonlinearity(Matrix linearOut, IgniteDifferentiableDoubleToDoubleFunction nonlinearity) {
    Matrix diff = linearOut.copy();
    diff.map(nonlinearity::differential);
    return diff;
}

Example 22 with Matrix

use of org.apache.ignite.ml.math.primitives.matrix.Matrix in project ignite by apache.

the class ReplicatedVectorMatrix method assignRow.

/**
 * {@inheritDoc}
 */
@Override
public Matrix assignRow(int row, Vector vec) {
    int rows = asCol ? vector.size() : replicationCnt;
    int cols = asCol ? replicationCnt : vector.size();
    int times = asCol ? cols : rows;
    Matrix res = new DenseMatrix(rows, cols);
    IgniteBiConsumer<Integer, Vector> replicantAssigner = asCol ? res::assignColumn : res::assignRow;
    IgniteBiConsumer<Integer, Vector> assigner = res::assignRow;
    assign(replicantAssigner, assigner, vector, vec, times, row);
    return res;
}

Example 23 with Matrix

use of org.apache.ignite.ml.math.primitives.matrix.Matrix in project ignite by apache.

the class MultilayerPerceptron method forwardPass.

/**
 * Perform forward pass and if needed write state of outputs of each layer.
 *
 * @param val Value to perform computation on.
 * @param state State object to write state into.
 * @param writeState Flag indicating need to write state.
 */
public Matrix forwardPass(Matrix val, MLPState state, boolean writeState) {
    Matrix res = val;
    if (below != null)
        res = below.forwardPass(val, state, writeState);
    for (int i = 1; i < architecture.layersCount(); i++) {
        MLPLayer curLayer = layers.get(i - 1);
        res = curLayer.weights.times(res);
        TransformationLayerArchitecture layerCfg = this.architecture.transformationLayerArchitecture(i);
        if (layerCfg.hasBias()) {
            ReplicatedVectorMatrix biasesMatrix = new ReplicatedVectorMatrix(biases(i), res.columnSize(), true);
            res = res.plus(biasesMatrix);
        }
        state.linearOutput.add(res);
        // If we do not write state, we can overwrite result.
        if (writeState)
            res = res.copy();
        res = res.map(layerCfg.activationFunction());
        state.activatorsOutput.add(res);
    }
    return res;
}

Example 24 with Matrix

use of org.apache.ignite.ml.math.primitives.matrix.Matrix in project ignite by apache.

the class MLPTest method testXOR.

/**
 * Test that MLP with parameters that should produce function close to 'XOR' is close to 'XOR' on 'XOR' domain.
 */
@Test
public void testXOR() {
    MLPArchitecture conf = new MLPArchitecture(2).withAddedLayer(2, true, Activators.SIGMOID).withAddedLayer(1, true, Activators.SIGMOID);
    MultilayerPerceptron mlp1 = new MultilayerPerceptron(conf, new MLPConstInitializer(1, 2));
    mlp1.setWeights(1, new DenseMatrix(new double[][] { { 20.0, 20.0 }, { -20.0, -20.0 } }));
    mlp1.setBiases(1, new DenseVector(new double[] { -10.0, 30.0 }));
    MultilayerPerceptron mlp2 = mlp1.setWeights(2, new DenseMatrix(new double[][] { { 20.0, 20.0 } }));
    MultilayerPerceptron mlp = mlp2.setBiases(2, new DenseVector(new double[] { -30.0 }));
    Matrix input = new DenseMatrix(new double[][] { { 0.0, 0.0 }, { 0.0, 1.0 }, { 1.0, 0.0 }, { 1.0, 1.0 } });
    Matrix predict = mlp.predict(input);
    Matrix truth = new DenseMatrix(new double[][] { { 0.0 }, { 1.0 }, { 1.0 }, { 0.0 } });
    TestUtils.checkIsInEpsilonNeighbourhood(predict.getRow(0), truth.getRow(0), 1E-4);
}

Example 25 with Matrix

use of org.apache.ignite.ml.math.primitives.matrix.Matrix in project ignite by apache.

the class MLPTest method testStackedMLP.

/**
 * Test that two layer MLP is equivalent to it's subparts stacked on each other.
 */
@Test
public void testStackedMLP() {
    int firstLayerNeuronsCnt = 3;
    int secondLayerNeuronsCnt = 2;
    MLPConstInitializer initer = new MLPConstInitializer(1, 2);
    MLPArchitecture conf = new MLPArchitecture(4).withAddedLayer(firstLayerNeuronsCnt, false, Activators.SIGMOID).withAddedLayer(secondLayerNeuronsCnt, false, Activators.SIGMOID);
    MultilayerPerceptron mlp = new MultilayerPerceptron(conf, initer);
    MLPArchitecture mlpLayer1Conf = new MLPArchitecture(4).withAddedLayer(firstLayerNeuronsCnt, false, Activators.SIGMOID);
    MLPArchitecture mlpLayer2Conf = new MLPArchitecture(firstLayerNeuronsCnt).withAddedLayer(secondLayerNeuronsCnt, false, Activators.SIGMOID);
    MultilayerPerceptron mlp1 = new MultilayerPerceptron(mlpLayer1Conf, initer);
    MultilayerPerceptron mlp2 = new MultilayerPerceptron(mlpLayer2Conf, initer);
    MultilayerPerceptron stackedMLP = mlp1.add(mlp2);
    Matrix predict = mlp.predict(new DenseMatrix(new double[][] { { 1 }, { 2 }, { 3 }, { 4 } }).transpose());
    Matrix stackedPredict = stackedMLP.predict(new DenseMatrix(new double[][] { { 1 }, { 2 }, { 3 }, { 4 } }).transpose());
    Assert.assertEquals(predict, stackedPredict);
}