org.nd4j.linalg.api.rng.Random#setSeed

Source File: RandomTests.java From nd4j with Apache License 2.0

5 votes

@Test
public void testSetSeed1() throws Exception {
    Random random1 = Nd4j.getRandomFactory().getNewRandomInstance(119);
    Random random2 = Nd4j.getRandomFactory().getNewRandomInstance(119);

    INDArray z01 = Nd4j.create(1000);
    INDArray z11 = Nd4j.create(1000);

    UniformDistribution distribution01 = new UniformDistribution(z01, 1.0, 2.0);
    Nd4j.getExecutioner().exec(distribution01, random1);

    UniformDistribution distribution11 = new UniformDistribution(z11, 1.0, 2.0);
    Nd4j.getExecutioner().exec(distribution11, random2);

    random1.setSeed(1999);
    random2.setSeed(1999);

    INDArray z02 = Nd4j.create(100);
    UniformDistribution distribution02 = new UniformDistribution(z02, 1.0, 2.0);
    Nd4j.getExecutioner().exec(distribution02, random1);

    INDArray z12 = Nd4j.create(100);
    UniformDistribution distribution12 = new UniformDistribution(z12, 1.0, 2.0);
    Nd4j.getExecutioner().exec(distribution12, random2);


    for (int x = 0; x < z01.length(); x++) {
        assertEquals("Failed on element: [" + x + "]", z01.getFloat(x), z11.getFloat(x), 0.01f);
    }

    assertEquals(z01, z11);

    for (int x = 0; x < z02.length(); x++) {
        assertEquals("Failed on element: [" + x + "]", z02.getFloat(x), z12.getFloat(x), 0.01f);
    }

    assertEquals(z02, z12);
}

Source File: Nd4jTest.java From nd4j with Apache License 2.0

5 votes

@Test
public void testGetRandomSetSeed() {
    Random r = Nd4j.getRandom();
    Random t = Nd4j.getRandom();

    assertEquals(r, t);
    r.setSeed(123);
    assertEquals(r, t);
}

Source File: RandomFactory.java From nd4j with Apache License 2.0

5 votes

/**
 * This method returns new onject implementing Random interface, initialized with seed value
 *
 * @param seed seed for this rng object
 * @return object implementing Random interface
 */
public Random getNewRandomInstance(long seed) {
    try {
        Random t = (Random) randomClass.newInstance();
        if (t.getStatePointer() != null) {
            // TODO: attach this thing to deallocator
            // if it's stateless random - we just don't care then
        }
        t.setSeed(seed);
        return t;
    } catch (Exception e) {
        throw new RuntimeException(e);
    }
}

Source File: A3CDiscrete.java From deeplearning4j with Apache License 2.0

5 votes

public A3CDiscrete(MDP<OBSERVATION, Integer, DiscreteSpace> mdp, IActorCritic iActorCritic, A3CLearningConfiguration conf) {
    this.iActorCritic = iActorCritic;
    this.mdp = mdp;
    this.configuration = conf;
    asyncGlobal = new AsyncGlobal<>(iActorCritic, conf);

    Long seed = conf.getSeed();
    Random rnd = Nd4j.getRandom();
    if (seed != null) {
        rnd.setSeed(seed);
    }

    policy = new ACPolicy<>(iActorCritic, rnd);
}

Source File: RandomTests.java From deeplearning4j with Apache License 2.0

5 votes

@Test
public void testSetSeed1() {
    Random random1 = Nd4j.getRandomFactory().getNewRandomInstance(119);
    Random random2 = Nd4j.getRandomFactory().getNewRandomInstance(119);

    INDArray z01 = Nd4j.create(1000);
    INDArray z11 = Nd4j.create(1000);

    UniformDistribution distribution01 = new UniformDistribution(z01, 1.0, 2.0);
    Nd4j.getExecutioner().exec(distribution01, random1);

    UniformDistribution distribution11 = new UniformDistribution(z11, 1.0, 2.0);
    Nd4j.getExecutioner().exec(distribution11, random2);

    random1.setSeed(1999);
    random2.setSeed(1999);

    INDArray z02 = Nd4j.create(100);
    UniformDistribution distribution02 = new UniformDistribution(z02, 1.0, 2.0);
    Nd4j.getExecutioner().exec(distribution02, random1);

    INDArray z12 = Nd4j.create(100);
    UniformDistribution distribution12 = new UniformDistribution(z12, 1.0, 2.0);
    Nd4j.getExecutioner().exec(distribution12, random2);


    for (int x = 0; x < z01.length(); x++) {
        assertEquals("Failed on element: [" + x + "]", z01.getFloat(x), z11.getFloat(x), 0.01f);
    }

    assertEquals(z01, z11);

    for (int x = 0; x < z02.length(); x++) {
        assertEquals("Failed on element: [" + x + "]", z02.getFloat(x), z12.getFloat(x), 0.01f);
    }

    assertEquals(z02, z12);
}

Source File: Nd4jTest.java From deeplearning4j with Apache License 2.0

5 votes

@Test
public void testGetRandomSetSeed() {
    Random r = Nd4j.getRandom();
    Random t = Nd4j.getRandom();

    assertEquals(r, t);
    r.setSeed(123);
    assertEquals(r, t);
}

Source File: SortCooTests.java From deeplearning4j with Apache License 2.0

5 votes

@Test
public void sortSparseCooIndicesSort3() {
    // FIXME: we don't want this test running on cuda for now
    if (Nd4j.getExecutioner().getClass().getCanonicalName().toLowerCase().contains("cuda"))
        return;

    Random rng = Nd4j.getRandom();
    rng.setSeed(12040483421383L);
    long shape[] = {50,50,50};
    int nnz = 100;
    val indices = Nd4j.rand(new int[]{nnz, shape.length}, rng).muli(50).ravel().toLongVector();
    val values = Nd4j.rand(new long[]{nnz}).ravel().toDoubleVector();


    DataBuffer indiceBuffer = Nd4j.getDataBufferFactory().createLong(indices);
    DataBuffer valueBuffer = Nd4j.createBuffer(values);
    INDArray indMatrix = Nd4j.create(indiceBuffer).reshape(new long[]{nnz, shape.length});

    NativeOpsHolder.getInstance().getDeviceNativeOps().sortCooIndices(null, (LongPointer) indiceBuffer.addressPointer(),
            valueBuffer.addressPointer(), nnz, 3);

    for (long i = 1; i < nnz; ++i){
        for(long j = 0; j < shape.length; ++j){
            long prev = indiceBuffer.getLong(((i - 1) * shape.length + j));
            long current = indiceBuffer.getLong((i * shape.length + j));
            if (prev < current){
                break;
            } else if(prev > current){
                long[] prevRow = getLongsAt(indiceBuffer, (i - 1) * shape.length, shape.length);
                long[] currentRow = getLongsAt(indiceBuffer, i * shape.length, shape.length);
                throw new AssertionError(String.format("indices are not correctly sorted between element %d and %d. %s > %s",
                        i - 1, i, Arrays.toString(prevRow), Arrays.toString(currentRow)));
            }
        }
    }
}

Source File: RandomFactory.java From deeplearning4j with Apache License 2.0

5 votes

/**
 * This method returns new onject implementing Random interface, initialized with seed value
 *
 * @param seed seed for this rng object
 * @return object implementing Random interface
 */
public Random getNewRandomInstance(long seed) {
    try {
        Random t = (Random) randomClass.newInstance();
        if (t.getStatePointer() != null) {
            // TODO: attach this thing to deallocator
            // if it's stateless random - we just don't care then
        }
        t.setSeed(seed);
        return t;
    } catch (Exception e) {
        throw new RuntimeException(e);
    }
}

Source File: RandomTests.java From deeplearning4j with Apache License 2.0

4 votes

@Test
public void testStepOver3() {
    Random random = Nd4j.getRandomFactory().getNewRandomInstance(119);
    if (random instanceof NativeRandom) {
        NativeRandom rng = (NativeRandom) random;

        int someInt = rng.nextInt();
        for (int e = 0; e < 10000; e++)
            rng.nextInt();

        random.setSeed(119);

        int sameInt = rng.nextInt();

        assertEquals(someInt, sameInt);

        random.setSeed(120);

        int otherInt = rng.nextInt();

        assertNotEquals(someInt, otherInt);

    } else
        log.warn("Not a NativeRandom object received, skipping test");
}

Source File: RandomTests.java From nd4j with Apache License 2.0

3 votes

@Test
public void testStepOver3() throws Exception {
    Random random = Nd4j.getRandomFactory().getNewRandomInstance(119);
    if (random instanceof NativeRandom) {
        NativeRandom rng = (NativeRandom) random;
        assertTrue(rng.getBufferSize() > 1000000L);

        int someInt = rng.nextInt();
        for (int e = 0; e < 10000; e++)
            rng.nextInt();

        random.setSeed(119);

        int sameInt = rng.nextInt();

        assertEquals(someInt, sameInt);

        random.setSeed(120);

        int otherInt = rng.nextInt();

        assertNotEquals(someInt, otherInt);


    } else
        log.warn("Not a NativeRandom object received, skipping test");
}

Source File: RandomTests.java From nd4j with Apache License 2.0

2 votes

@Test
public void testStepOver1() throws Exception {
    Random random1 = Nd4j.getRandomFactory().getNewRandomInstance(119);


    log.info("1: ----------------");

    INDArray z0 = Nd4j.getExecutioner().exec(new GaussianDistribution(Nd4j.createUninitialized(1000000), 0.0, 1.0));

    assertEquals(0.0, z0.meanNumber().doubleValue(), 0.01);
    assertEquals(1.0, z0.stdNumber().doubleValue(), 0.01);

    random1.setSeed(119);

    log.info("2: ----------------");

    INDArray z2 = Nd4j.zeros(55000000);
    INDArray z1 = Nd4j.zeros(55000000);

    GaussianDistribution op1 = new GaussianDistribution(z1, 0.0, 1.0);
    Nd4j.getExecutioner().exec(op1, random1);


    log.info("2: ----------------");

    //log.info("End: [{}, {}, {}, {}]", z1.getFloat(29000000), z1.getFloat(29000001), z1.getFloat(29000002), z1.getFloat(29000003));

    log.info("Sum: {}", z1.sumNumber().doubleValue());
    log.info("Sum2: {}", z2.sumNumber().doubleValue());


    INDArray match = Nd4j.getExecutioner().exec(new MatchCondition(z1, Conditions.isNan()), Integer.MAX_VALUE);
    log.info("NaNs: {}", match);
    assertEquals(0.0f, match.getFloat(0), 0.01f);

    /*
    for (int i = 0; i < z1.length(); i++) {
        if (Double.isNaN(z1.getDouble(i)))
            throw new IllegalStateException("NaN value found at " + i);
    
        if (Double.isInfinite(z1.getDouble(i)))
            throw new IllegalStateException("Infinite value found at " + i);
    }
    */

    assertEquals(1.0, z1.stdNumber().doubleValue(), 0.01);
    assertEquals(0.0, z1.meanNumber().doubleValue(), 0.01);
}

Source File: RandomTests.java From deeplearning4j with Apache License 2.0

2 votes

@Test
    public void testStepOver1() {
        Random random1 = Nd4j.getRandomFactory().getNewRandomInstance(119);


//        log.info("1: ----------------");

        INDArray z0 = Nd4j.getExecutioner().exec(new GaussianDistribution(Nd4j.createUninitialized(DataType.DOUBLE, 1000000), 0.0, 1.0));

        assertEquals(0.0, z0.meanNumber().doubleValue(), 0.01);
        assertEquals(1.0, z0.stdNumber().doubleValue(), 0.01);

        random1.setSeed(119);

//        log.info("2: ----------------");

        INDArray z1 = Nd4j.zeros(DataType.DOUBLE, 55000000);
        INDArray z2 = Nd4j.zeros(DataType.DOUBLE, 55000000);

        GaussianDistribution op1 = new GaussianDistribution(z1, 0.0, 1.0);
        Nd4j.getExecutioner().exec(op1, random1);

//        log.info("2: ----------------");

        //log.info("End: [{}, {}, {}, {}]", z1.getFloat(29000000), z1.getFloat(29000001), z1.getFloat(29000002), z1.getFloat(29000003));

        //log.info("Sum: {}", z1.sumNumber().doubleValue());
//        log.info("Sum2: {}", z2.sumNumber().doubleValue());


        INDArray match = Nd4j.getExecutioner().exec(new MatchCondition(z1, Conditions.isNan()));
//        log.info("NaNs: {}", match);
        assertEquals(0.0f, match.getFloat(0), 0.01f);

        /*
        for (int i = 0; i < z1.length(); i++) {
            if (Double.isNaN(z1.getDouble(i)))
                throw new IllegalStateException("NaN value found at " + i);
        
            if (Double.isInfinite(z1.getDouble(i)))
                throw new IllegalStateException("Infinite value found at " + i);
        }
        */

        assertEquals(0.0, z1.meanNumber().doubleValue(), 0.01);
        assertEquals(1.0, z1.stdNumber().doubleValue(), 0.01);
    }

Java Code Examples for org.nd4j.linalg.api.rng.Random#setSeed()