Java Code Examples for org.nd4j.linalg.api.ndarray.INDArray#isMatrix()

private void handleNdArray(INDArray output) {
    if(output.isVector()) {
        for(int i = 0; i < output.length(); i++) {
            statCounters.get(i).add(output.getDouble(i));
        }
    }
    else if(output.isMatrix() && output.length() > 1) {
        for(int i = 0; i < output.rows(); i++) {
            for(int j = 0; j < output.columns(); j++) {
                statCounters.get(i).add(output.getDouble(i,j));
            }
        }
    }
    else if(output.isScalar()) {
        statCounters.get(0).add(output.sumNumber().doubleValue());
    }
    else {
        throw new IllegalArgumentException("Only vectors and matrices supported right now");
    }
}
 

public SparseCOOGemvParameters(INDArray a, INDArray x, INDArray y) {
    this.a = a;
    this.x = x;
    this.y = y;

    if (a.isMatrix() && a.getFormat() == SparseFormat.COO) {
        BaseSparseNDArrayCOO coo = (BaseSparseNDArrayCOO) a;
        val = coo.getIncludedValues();
        nnz = coo.nnz();

        // FIXME: int cast
        m = (int) coo.rows();
        setIndexes(coo, false);
    }
}
 

/**
 * Rotate a matrix 90 degrees
 *
 * @param toRotate the matrix to rotate
 * @return the rotated matrix
 */
@Override
public void rot90(INDArray toRotate) {
    if (!toRotate.isMatrix())
        throw new IllegalArgumentException("Only rotating matrices");

    INDArray start = toRotate.transpose();
    for (int i = 0; i < start.rows(); i++)
        start.putRow(i, reverse(start.getRow(i)));

}
 

/**
 * Rotate a matrix 90 degrees
 *
 * @param toRotate the matrix to rotate
 * @return the rotated matrix
 */
@Override
public void rot90(INDArray toRotate) {
    if (!toRotate.isMatrix())
        throw new IllegalArgumentException("Only rotating matrices");

    INDArray start = toRotate.transpose();
    for (int i = 0; i < start.rows(); i++)
        start.putRow(i, reverse(start.getRow(i)));

}
 

/**
 * Convert an ndarray to a matrix.
 * Note that the matrix will be con
 * @param arr the array
 * @return an mllib vector
 */
public static Matrix toMatrix(INDArray arr) {
    if (!arr.isMatrix()) {
        throw new IllegalArgumentException("passed in array must be a matrix");
    }

    // if arr is a view - we have to dup anyway
    if (arr.isView()) {
        return Matrices.dense(arr.rows(), arr.columns(), arr.dup('f').data().asDouble());
    } else // if not a view - we must ensure data is F ordered
        return Matrices.dense(arr.rows(), arr.columns(),
                        arr.ordering() == 'f' ? arr.data().asDouble() : arr.dup('f').data().asDouble());
}
 

public GemvParameters(INDArray a, INDArray x, INDArray y) {
    a = copyIfNecessary(a);
    x = copyIfNecessaryVector(x);
    this.a = a;
    this.x = x;
    this.y = y;

    if (a.columns() > Integer.MAX_VALUE || a.rows() > Integer.MAX_VALUE)
        throw new ND4JArraySizeException();

    if (x.columns() > Integer.MAX_VALUE || x.rows() > Integer.MAX_VALUE)
        throw new ND4JArraySizeException();


    if (a.ordering() == 'f' && a.isMatrix()) {
        this.m = (int) a.rows();
        this.n = (int) a.columns();
        this.lda = (int) a.rows();
    } else if (a.ordering() == 'c' && a.isMatrix()) {
        this.m = (int) a.columns();
        this.n = (int) a.rows();
        this.lda = (int) a.columns();
        aOrdering = 'T';
    }

    else {
        this.m = (int) a.rows();
        this.n = (int) a.columns();
        this.lda = (int) a.size(0);
    }


    if (x.rank() == 1) {
        incx = 1;
    } else if (x.isColumnVector()) {
        incx = x.stride(0);
    } else {
        incx = x.stride(1);
    }

    this.incy = y.elementWiseStride();

    if (x instanceof IComplexNDArray)
        this.incx /= 2;
    if (y instanceof IComplexNDArray)
        this.incy /= 2;

}
 

public GemvParameters(INDArray a, INDArray x, INDArray y) {
    a = copyIfNecessary(a);
    x = copyIfNecessaryVector(x);
    this.a = a;
    this.x = x;
    this.y = y;

    if (a.columns() > Integer.MAX_VALUE || a.rows() > Integer.MAX_VALUE)
        throw new ND4JArraySizeException();

    if (x.columns() > Integer.MAX_VALUE || x.rows() > Integer.MAX_VALUE)
        throw new ND4JArraySizeException();


    if (a.ordering() == 'f' && a.isMatrix()) {
        this.m = (int) a.rows();
        this.n = (int) a.columns();
        this.lda = (int) a.rows();
    } else if (a.ordering() == 'c' && a.isMatrix()) {
        this.m = (int) a.columns();
        this.n = (int) a.rows();
        this.lda = (int) a.columns();
        aOrdering = 'T';
    }

    else {
        this.m = (int) a.rows();
        this.n = (int) a.columns();
        this.lda = (int) a.size(0);
    }


    if (x.rank() == 1) {
        incx = 1;
    } else if (x.isColumnVector()) {
        incx = x.stride(0);
    } else {
        incx = x.stride(1);
    }

    this.incy = y.elementWiseStride();

}
 

/**
 * This test is not indicative.
 * there's no need in this test within travis, use it manually only for problems detection
 *
 * @throws Exception
 */
@Test
@Ignore
public void testParagraphVectorsReducedLabels1() throws Exception {
    val tempDir = testDir.newFolder();
    ClassPathResource resource = new ClassPathResource("/labeled");
    resource.copyDirectory(tempDir);

    LabelAwareIterator iter = new FileLabelAwareIterator.Builder().addSourceFolder(tempDir).build();

    TokenizerFactory t = new DefaultTokenizerFactory();

    /**
     * Please note: text corpus is REALLY small, and some kind of "results" could be received with HIGH epochs number, like 30.
     * But there's no reason to keep at that high
     */

    ParagraphVectors vec = new ParagraphVectors.Builder().minWordFrequency(1).epochs(3).layerSize(100)
                    .stopWords(new ArrayList<String>()).windowSize(5).iterate(iter).tokenizerFactory(t).build();

    vec.fit();

    //WordVectorSerializer.writeWordVectors(vec, "vectors.txt");

    INDArray w1 = vec.lookupTable().vector("I");
    INDArray w2 = vec.lookupTable().vector("am");
    INDArray w3 = vec.lookupTable().vector("sad.");

    INDArray words = Nd4j.create(3, vec.lookupTable().layerSize());

    words.putRow(0, w1);
    words.putRow(1, w2);
    words.putRow(2, w3);


    INDArray mean = words.isMatrix() ? words.mean(0) : words;

    log.info("Mean" + Arrays.toString(mean.dup().data().asDouble()));
    log.info("Array" + Arrays.toString(vec.lookupTable().vector("negative").dup().data().asDouble()));

    double simN = Transforms.cosineSim(mean, vec.lookupTable().vector("negative"));
    log.info("Similarity negative: " + simN);


    double simP = Transforms.cosineSim(mean, vec.lookupTable().vector("neutral"));
    log.info("Similarity neutral: " + simP);

    double simV = Transforms.cosineSim(mean, vec.lookupTable().vector("positive"));
    log.info("Similarity positive: " + simV);
}
 

/**
 * Create from a matrix. The rows are the indices
 * The columns are the individual element in each ndarrayindex
 *
 * @param index the matrix to getFloat indices from
 * @return the indices to getFloat
 */
public static INDArrayIndex[] create(INDArray index) {

    if (index.isMatrix()) {

        if (index.rows() > Integer.MAX_VALUE)
            throw new ND4JArraySizeException();

        NDArrayIndex[] ret = new NDArrayIndex[(int) index.rows()];
        for (int i = 0; i < index.rows(); i++) {
            INDArray row = index.getRow(i);
            val nums = new long[(int) index.getRow(i).columns()];
            for (int j = 0; j < row.columns(); j++) {
                nums[j] = (int) row.getFloat(j);
            }

            NDArrayIndex idx = new NDArrayIndex(nums);
            ret[i] = idx;

        }


        return ret;

    } else if (index.isVector()) {
        long[] indices = NDArrayUtil.toLongs(index);
        return new NDArrayIndex[] {new NDArrayIndex(indices)};
    }


    throw new IllegalArgumentException("Passed in ndarray must be a matrix or a vector");

}