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

/**
 * Create an EvaulationBinary instance with an optional decision threshold array.
 *
 * @param decisionThreshold Decision threshold for each output; may be null. Should be a row vector with length
 *                          equal to the number of outputs, with values in range 0 to 1. An array of 0.5 values is
 *                          equivalent to the default (no manually specified decision threshold).
 */
public EvaluationBinary(INDArray decisionThreshold) {
    if (decisionThreshold != null) {
        if (!decisionThreshold.isRowVectorOrScalar()) {
            throw new IllegalArgumentException(
                            "Decision threshold array must be a row vector; got array with shape "
                                            + Arrays.toString(decisionThreshold.shape()));
        }
        if (decisionThreshold.minNumber().doubleValue() < 0.0) {
            throw new IllegalArgumentException("Invalid decision threshold array: minimum value is less than 0");
        }
        if (decisionThreshold.maxNumber().doubleValue() > 1.0) {
            throw new IllegalArgumentException(
                            "invalid decision threshold array: maximum value is greater than 1.0");
        }

        this.decisionThreshold = decisionThreshold;
    }
}
 

private int countLength(List<Writable> list, int from, int to) {
    int length = 0;
    for (int i = from; i <= to; i++) {
        Writable w = list.get(i);
        if (w instanceof NDArrayWritable) {
            INDArray a = ((NDArrayWritable) w).get();
            if (!a.isRowVectorOrScalar()) {
                throw new UnsupportedOperationException("Multiple writables present but NDArrayWritable is "
                                + "not a row vector. Can only concat row vectors with other writables. Shape: "
                                + Arrays.toString(a.shape()));
            }
            length += a.length();
        } else {
            //Assume all others are single value
            length++;
        }
    }

    return length;
}
 

/**
 *  Created evaluation instance with the specified cost array. A cost array can be used to bias the multi class
 *  predictions towards or away from certain classes. The predicted class is determined using argMax(cost * probability)
 *  instead of argMax(probability) when no cost array is present.
 *
 * @param labels Labels for the output classes. May be null
 * @param costArray Row vector cost array. May be null
 */
public Evaluation(List<String> labels, INDArray costArray) {
    if (costArray != null && !costArray.isRowVectorOrScalar()) {
        throw new IllegalArgumentException("Invalid cost array: must be a row vector (got shape: "
                        + Arrays.toString(costArray.shape()) + ")");
    }
    if (costArray != null && costArray.minNumber().doubleValue() < 0.0) {
        throw new IllegalArgumentException("Invalid cost array: Cost array values must be positive");
    }
    this.labelsList = labels;
    this.costArray = costArray == null ? null : costArray.castTo(DataType.FLOAT);
    this.topN = 1;
}
 

@Override
public void setStateViewArray(INDArray viewArray, long[] gradientShape, char gradientOrder, boolean initialize) {
    if (!viewArray.isRowVectorOrScalar())
        throw new IllegalArgumentException("Invalid input: expect row vector input");
    if (initialize)
        viewArray.assign(epsilon);
    this.historicalGradient = viewArray;
    //Reshape to match the expected shape of the input gradient arrays
    this.historicalGradient = Shape.newShapeNoCopy(this.historicalGradient, gradientShape, gradientOrder == 'f');
    if (historicalGradient == null)
        throw new IllegalStateException("Could not correctly reshape gradient view array");

    this.gradientReshapeOrder = gradientOrder;
}
 

@Override
public void setStateViewArray(INDArray viewArray, long[] gradientShape, char gradientOrder, boolean initialize) {
    if (!viewArray.isRowVectorOrScalar())
        throw new IllegalArgumentException("Invalid input: expect row vector input");
    if (initialize)
        viewArray.assign(config.getEpsilon());
    this.lastGradient = viewArray;

    //Reshape to match the expected shape of the input gradient arrays
    this.lastGradient = Shape.newShapeNoCopy(this.lastGradient, gradientShape, gradientOrder == 'f');
    if (lastGradient == null)
        throw new IllegalStateException("Could not correctly reshape gradient view array");

    gradientReshapeOrder = gradientOrder;
}
 

@Override
public void setStateViewArray(INDArray viewArray, long[] gradientShape, char gradientOrder, boolean initialize) {
    if (!viewArray.isRowVectorOrScalar())
        throw new IllegalArgumentException("Invalid input: expect row vector input");
    if (initialize)
        viewArray.assign(0);

    this.v = viewArray;

    //Reshape to match the expected shape of the input gradient arrays
    this.v = Shape.newShapeNoCopy(this.v, gradientShape, gradientOrder == 'f');
    if (v == null)
        throw new IllegalStateException("Could not correctly reshape gradient view array");
    this.gradientReshapeOrder = gradientOrder;
}