Java INDArray.add方法代碼示例

import org.nd4j.linalg.api.ndarray.INDArray; //導入方法依賴的package包/類
/**
 * Computes the filtered {@link GaussianDistribution} (mean and covariance) using the supplied
 * state-, measurement-, and control-transition matrices, and the supplied control input, process
 * noise, observation noise, and current state.
 *
 * @param stateTransitionMatrix       The state-transition matrix used in projecting the {@link
 *                                    GaussianDistribution}.
 * @param measurementTransitionMatrix The measurement-transition matrix used in computing the
 *                                    measurement based on the supplied state.
 * @param controlTransitionMatrix     The control-transition matrix used in computing the effect
 *                                    of the control input on the resultant {@link
 *                                    GaussianDistribution}.
 * @param controlInputVector          The control input.
 * @param processCovariance           The process variance matrix. This matrix determines the
 *                                    impact of the state transition matrix on the filter and can
 *                                    be seen as an indication of how trustworthy the state
 *                                    transition model is. Lower values in the process covariance
 *                                    matrix result in the filtered state tending towards the
 *                                    projected state using the state transition model. Higher
 *                                    values result in the filtered state tending towards the
 *                                    measurements.
 * @param measurement                 The {@link GaussianDistribution} of the measurement, where
 *                                    the mean is equal to the measurement vector and the
 *                                    covariance is equal to the measurement variance matrix.
 * @param previousState               The {@link GaussianDistribution} of the latest state.
 * @return The filtered state.
 */
public Distribution apply(
    final INDArray stateTransitionMatrix,
    final INDArray measurementTransitionMatrix,
    final INDArray controlTransitionMatrix,
    final INDArray controlInputVector,
    final INDArray processCovariance,
    final Distribution measurement,
    final Distribution previousState
) {
  final INDArray projectedState = stateTransitionMatrix.mmul(previousState.getMean())
      .add(controlTransitionMatrix.mmul(controlInputVector));

  final INDArray projectedErrorCovariance =
      stateTransitionMatrix
          .mmul(previousState.getCovariance().mmul(stateTransitionMatrix.transpose()))
          .add(processCovariance);

  final INDArray kalmanGain = projectedErrorCovariance
      .mmul(measurementTransitionMatrix.transpose()
          .mmul(InvertMatrix.invert(measurementTransitionMatrix
              .mmul(projectedErrorCovariance
                  .mmul(measurementTransitionMatrix.transpose()))
              .add(measurement.getCovariance()), false)));

  final INDArray estimatedState = projectedState
      .add(kalmanGain
          .mmul(measurement.getMean().sub(measurementTransitionMatrix.mmul(projectedState))));

  final INDArray estimatedErrorCovariance =
      (Nd4j.eye(estimatedState.rows()).sub(kalmanGain.mmul(measurementTransitionMatrix)))
          .mmul(projectedErrorCovariance);

  return new SimpleDistribution(estimatedState, estimatedErrorCovariance);
}
 

import org.nd4j.linalg.api.ndarray.INDArray; //導入方法依賴的package包/類
/**
 * Computes the filtered {@link Distribution} (mean and covariance) using the supplied state- and
 * measurement-transition functions, their respective Jacobians and the supplied control input,
 * process noise, observation noise, and current state.
 *
 * @param stateTransition               A {@link BiFunction} which takes as its first argument the
 *                                      previous state, and the control input as its second
 *                                      argument. The result is the next state.
 * @param measurementTransition         A {@link Function} which takes as its first argument the
 *                                      current state and returns the measurement.
 * @param stateTransitionJacobian       The Jacobian representing the state-transition.
 * @param measurementTransitionJacobian The Jacobian representing the measurement-transition.
 * @param controlInput                  The control input.
 * @param processCovariance             the process covariance
 * @param observationNoise              The {@link Distribution} of observation noise
 * @param state                         The {@link Distribution} of the latest state.
 * @return The filtered state.
 */
public Distribution apply(
    final BiFunction<INDArray, INDArray, INDArray> stateTransition,
    final Function<INDArray, INDArray> measurementTransition,
    final INDArray stateTransitionJacobian,
    final INDArray measurementTransitionJacobian,
    final INDArray controlInput,
    final INDArray processCovariance,
    final Distribution observationNoise,
    final Distribution state
) {
  final INDArray projectedState = stateTransition.apply(state.getMean(), controlInput);

  final INDArray projectedErrorCovariance = stateTransitionJacobian
      .mmul(state.getMean()
          .mmul(stateTransitionJacobian.transpose()))
      .add(processCovariance);

  final INDArray kalmanGain = projectedErrorCovariance
      .mmul(measurementTransitionJacobian.transpose()
          .mmul(InvertMatrix.invert(measurementTransitionJacobian
              .mmul(projectedErrorCovariance
                  .mmul(measurementTransitionJacobian.transpose()))
              .add(observationNoise.getCovariance()), false)));

  final INDArray estimatedState = projectedState
      .add(kalmanGain
          .mmul(measurementTransition.apply(state.getMean())
              .sub(measurementTransition.apply(projectedState))));

  final INDArray estimatedErrorCovariance = Nd4j.eye(estimatedState.rows())
      .sub(kalmanGain.
          mul(measurementTransitionJacobian))
      .mmul(projectedErrorCovariance);

  return new SimpleDistribution(estimatedState, estimatedErrorCovariance);
}
 

import org.nd4j.linalg.api.ndarray.INDArray; //導入方法依賴的package包/類
@Override
public SeldonMessage aggregate(List<SeldonMessage> outputs, PredictiveUnitState state){
	
	if (outputs.size()==0){
		throw new APIException(APIException.ApiExceptionType.ENGINE_INVALID_COMBINER_RESPONSE, String.format("Combiner received no inputs"));
	}
	
	int[] shape = PredictorUtils.getShape(outputs.get(0).getData());
	
	if (shape == null){
		throw new APIException(APIException.ApiExceptionType.ENGINE_INVALID_COMBINER_RESPONSE, String.format("Combiner cannot extract data shape"));
	}
	
	if (shape.length!=2){
		throw new APIException(APIException.ApiExceptionType.ENGINE_INVALID_COMBINER_RESPONSE, String.format("Combiner received data that is not 2 dimensional"));
	}
	
	INDArray currentSum = Nd4j.zeros(shape[0],shape[1]);
	SeldonMessage.Builder respBuilder = SeldonMessage.newBuilder();
	
	for (Iterator<SeldonMessage> i = outputs.iterator(); i.hasNext();)
	{
		DefaultData inputData = i.next().getData();
		int[] inputShape = PredictorUtils.getShape(inputData);
		if (inputShape == null){
			throw new APIException(APIException.ApiExceptionType.ENGINE_INVALID_COMBINER_RESPONSE, String.format("Combiner cannot extract data shape"));
		}
		if (inputShape.length!=2){
			throw new APIException(APIException.ApiExceptionType.ENGINE_INVALID_COMBINER_RESPONSE, String.format("Combiner received data that is not 2 dimensional"));
		}
		if (inputShape[0] != shape[0]){
			throw new APIException(APIException.ApiExceptionType.ENGINE_INVALID_COMBINER_RESPONSE, String.format("Expected batch length %d but found %d",shape[0],inputShape[0]));
		}
		if (inputShape[1] != shape[1]){
			throw new APIException(APIException.ApiExceptionType.ENGINE_INVALID_COMBINER_RESPONSE, String.format("Expected batch length %d but found %d",shape[1],inputShape[1]));
		}
		INDArray inputArr = PredictorUtils.getINDArray(inputData);
		currentSum = currentSum.add(inputArr);
	}
	currentSum = currentSum.div((float)outputs.size());
	
	DefaultData newData = PredictorUtils.updateData(outputs.get(0).getData(), currentSum);
	respBuilder.setData(newData);
	respBuilder.setMeta(outputs.get(0).getMeta());
	respBuilder.setStatus(outputs.get(0).getStatus());
	
	return respBuilder.build();
}