Java INDArray.mmul方法代碼示例

import org.nd4j.linalg.api.ndarray.INDArray; //導入方法依賴的package包/類
public void nd4JExample() {
    double[] A = {
        0.1950, 0.0311,
        0.3588, 0.2203,
        0.1716, 0.5931,
        0.2105, 0.3242};

    double[] B = {
        0.0502, 0.9823, 0.9472,
        0.5732, 0.2694, 0.916};

    
    INDArray aINDArray = Nd4j.create(A,new int[]{4,2},'c');
    INDArray bINDArray = Nd4j.create(B,new int[]{2,3},'c');
    
    INDArray cINDArray;
    cINDArray = aINDArray.mmul(bINDArray);
    for(int i=0; i<cINDArray.rows(); i++) {
        System.out.println(cINDArray.getRow(i));
    }
}
 

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
private boolean isTransitive(double[] ilpSolution, int n) {

		double[][] adjacencyMatrix = new double[n][n];
		int k = 0;
		for (int i = 0; i < n; i++) {
			adjacencyMatrix[i][i] = 1;
			for (int j = i + 1; j < n; j++) {
				adjacencyMatrix[i][j] = ilpSolution[k];
				adjacencyMatrix[j][i] = ilpSolution[k];
				k++;
			}
		}

		INDArray m = new NDArray(adjacencyMatrix);
		INDArray m2 = m.mmul(m);

		System.out.println(m);

		for (int i = 0; i < m.rows(); i++) {
			for (int j = 0; j < m.columns(); j++) {
				if (m2.getDouble(i, j) > 0 && m.getDouble(i, j) == 0) {
					System.out.println(i + " " + j + " " + m2.getDouble(i, j) + " " + m.getDouble(i, j));
					return false;
				}
			}
		}

		return true;
	}