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

public Construct4dDataSet(int nExamples, int nChannels, int height, int width) {

            INDArray allImages = Nd4j.rand(new int[] {nExamples, nChannels, height, width});
            allImages.get(NDArrayIndex.all(), NDArrayIndex.point(1), NDArrayIndex.all(), NDArrayIndex.all()).muli(100)
                            .addi(200);
            allImages.get(NDArrayIndex.all(), NDArrayIndex.point(2), NDArrayIndex.all(), NDArrayIndex.all()).muli(0.001)
                            .subi(10);

            INDArray labels = Nd4j.linspace(1, nChannels, nChannels).reshape(nChannels, 1);
            sampleDataSet = new DataSet(allImages, labels);

            expectedMean = allImages.mean(0, 2, 3);
            expectedStd = allImages.std(0, 2, 3);

            expectedLabelMean = labels.mean(0);
            expectedLabelStd = labels.std(0);

            expectedMin = allImages.min(0, 2, 3);
            expectedMax = allImages.max(0, 2, 3);
        }
 

public SpTree(INDArray data, Collection<INDArray> indices, String similarityFunction) {
    this.indices = indices;
    this.N = data.rows();
    this.D = data.columns();
    this.similarityFunction = similarityFunction;
    data = data.dup();
    INDArray meanY = data.mean(0);
    INDArray minY = data.min(0);
    INDArray maxY = data.max(0);
    INDArray width = Nd4j.create(data.dataType(), meanY.shape());
    for (int i = 0; i < width.length(); i++) {
        width.putScalar(i, Math.max(maxY.getDouble(i) - meanY.getDouble(i),
                meanY.getDouble(i) - minY.getDouble(i)) + Nd4j.EPS_THRESHOLD);
    }

    try(MemoryWorkspace ws = Nd4j.getMemoryManager().scopeOutOfWorkspaces()) {
        init(null, data, meanY, width, indices, similarityFunction);
        fill(N);
    }
}
 

/**
 * @param mean row vector of means
 * @param std  row vector of standard deviations
 */
public DistributionStats(@NonNull INDArray mean, @NonNull INDArray std) {
    Transforms.max(std, Nd4j.EPS_THRESHOLD, false);
    if (std.min(1) == Nd4j.scalar(Nd4j.EPS_THRESHOLD)) {
        logger.info("API_INFO: Std deviation found to be zero. Transform will round up to epsilon to avoid nans.");
    }

    this.mean = mean;
    this.std = std;
}
 

/**
 * Add rows of data to the statistics
 *
 * @param data the matrix containing multiple rows of data to include
 * @param mask (optionally) the mask of the data, useful for e.g. time series
 */
public MinMaxStats.Builder add(@NonNull INDArray data, INDArray mask) {
    data = DataSetUtil.tailor2d(data, mask);
    if (data == null) {
        // Nothing to add. Either data is empty or completely masked. Just skip it, otherwise we will get
        // null pointer exceptions.
        return this;
    }

    INDArray tad = data.javaTensorAlongDimension(0, 0);
    INDArray batchMin = data.min(0);
    INDArray batchMax = data.max(0);
    if (!Arrays.equals(batchMin.shape(), batchMax.shape()))
        throw new IllegalStateException(
                        "Data min and max must be same shape. Likely a bug in the operation changing the input?");
    if (runningLower == null) {
        // First batch
        // Create copies because min and max are views to the same data set, which will cause problems with the
        // side effects of Transforms.min and Transforms.max
        runningLower = batchMin.dup();
        runningUpper = batchMax.dup();
    } else {
        // Update running bounds
        Transforms.min(runningLower, batchMin, false);
        Transforms.max(runningUpper, batchMax, false);
    }

    return this;
}
 

/**
 * Scales the ndarray columns
 * to the given min/max values
 *
 * @param min the minimum number
 * @param max the max number
 */
public static void scaleMinMax(double min, double max, INDArray toScale) {
    //X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0)) X_scaled = X_std * (max - min) + min

    INDArray min2 = toScale.min(0);
    INDArray max2 = toScale.max(0);

    INDArray std = toScale.subRowVector(min2).diviRowVector(max2.sub(min2));

    INDArray scaled = std.mul(max - min).addi(min);
    toScale.assign(scaled);
}
 

/**
 * Scales the ndarray columns
 * to the given min/max values
 *
 * @param min the minimum number
 * @param max the max number
 */
public static void scaleMinMax(double min, double max, INDArray toScale) {
    //X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0)) X_scaled = X_std * (max - min) + min

    INDArray min2 = toScale.min(0);
    INDArray max2 = toScale.max(0);

    INDArray std = toScale.subRowVector(min2).diviRowVector(max2.sub(min2));

    INDArray scaled = std.mul(max - min).addi(min);
    toScale.assign(scaled);
}
 

/**
 * Pass in a matrix
 * @param data
 */
public QuadTree(INDArray data) {
    INDArray meanY = data.mean(0);
    INDArray minY = data.min(0);
    INDArray maxY = data.max(0);
    init(data, meanY.getDouble(0), meanY.getDouble(1),
                    max(maxY.getDouble(0) - meanY.getDouble(0), meanY.getDouble(0) - minY.getDouble(0))
                                    + Nd4j.EPS_THRESHOLD,
                    max(maxY.getDouble(1) - meanY.getDouble(1), meanY.getDouble(1) - minY.getDouble(1))
                                    + Nd4j.EPS_THRESHOLD);
    fill();
}