/** * @see org.apache.commons.math.stat.descriptive.MultivariateSummaryStatistics#getCovariance() */ public synchronized RealMatrix getCovariance() { return super.getCovariance(); }
/** * {@inheritDoc} */ @Override public synchronized RealMatrix getCovariance() { return super.getCovariance(); }
/** * {@inheritDoc} */ @Override public synchronized RealMatrix getCovariance() { return super.getCovariance(); }
@Override public String toString() { return "\nPixelProfileModel[\n" + "\tcount = " + statistics.getN() + "\n" + "\tmean = " + Arrays.toString(statistics.getMean()) + "\n" + "\tcovar = " + statistics.getCovariance() + "\n" + "]"; }
@Override public String toString() { return "\nPixelProfileModel[\n" + "\tcount = " + statistics.getN() + "\n" + "\tmean = " + Arrays.toString(statistics.getMean()) + "\n" + "\tcovar = " + statistics.getCovariance() + "\n" + "]"; }
@Override public String toString() { return "\nPixelProfileModel[\n" + "\tcount = " + statistics.getN() + "\n" + "\tmean = " + Arrays.toString(statistics.getMean()) + "\n" + "\tcovar = " + statistics.getCovariance() + "\n" + "]"; }
@Override public String toString() { return "\nPixelProfileModel[\n" + "\tcount = " + statistics.getN() + "\n" + "\tmean = " + Arrays.toString(statistics.getMean()) + "\n" + "\tcovar = " + statistics.getCovariance() + "\n" + "]"; }
/** * @return the covariance of the model */ public Matrix getCovariance() { if (mean == null) { mean = statistics.getMean(); invCovar = new Matrix(statistics.getCovariance().getData()).inverse(); } return new Matrix(statistics.getCovariance().getData()); }
/** * @return the covariance of the model */ public Matrix getCovariance() { if (mean == null) { mean = statistics.getMean(); invCovar = new Matrix(statistics.getCovariance().getData()).inverse(); } return new Matrix(statistics.getCovariance().getData()); }
/** * @return the covariance of the model */ public Matrix getCovariance() { if (mean == null) { mean = statistics.getMean(); invCovar = new Matrix(statistics.getCovariance().getData()).inverse(); } return new Matrix(statistics.getCovariance().getData()); }
/** * @return the covariance of the model */ public Matrix getCovariance() { if (mean == null) { mean = statistics.getMean(); invCovar = new Matrix(statistics.getCovariance().getData()).inverse(); } return new Matrix(statistics.getCovariance().getData()); }
/** * @return the inverse of the covariance matrix */ public Matrix getInverseCovariance() { if (mean == null) { mean = statistics.getMean(); invCovar = new Matrix(statistics.getCovariance().getData()).inverse(); } return invCovar; }
/** * @return the inverse of the covariance matrix */ public Matrix getInverseCovariance() { if (mean == null) { mean = statistics.getMean(); invCovar = new Matrix(statistics.getCovariance().getData()).inverse(); } return invCovar; }
/** * @return the mean of the model */ public double[] getMean() { if (mean == null) { mean = statistics.getMean(); invCovar = new Matrix(statistics.getCovariance().getData()).inverse(); } return mean; }
/** * @return the inverse of the covariance matrix */ public Matrix getInverseCovariance() { if (mean == null) { mean = statistics.getMean(); invCovar = new Matrix(statistics.getCovariance().getData()).inverse(); } return invCovar; }
/** * @return the mean of the model */ public double[] getMean() { if (mean == null) { mean = statistics.getMean(); invCovar = new Matrix(statistics.getCovariance().getData()).inverse(); } return mean; }
/** * @return the mean of the model */ public double[] getMean() { if (mean == null) { mean = statistics.getMean(); invCovar = new Matrix(statistics.getCovariance().getData()).inverse(); } return mean; }
/** * @return the inverse of the covariance matrix */ public Matrix getInverseCovariance() { if (mean == null) { mean = statistics.getMean(); invCovar = new Matrix(statistics.getCovariance().getData()).inverse(); } return invCovar; }
/** * @return the mean of the model */ public double[] getMean() { if (mean == null) { mean = statistics.getMean(); invCovar = new Matrix(statistics.getCovariance().getData()).inverse(); } return mean; }
/** * Returns hash code based on values of statistics * * @return hash code */ @Override public int hashCode() { int result = 31 + MathUtils.hash(getGeometricMean()); result = result * 31 + MathUtils.hash(getGeometricMean()); result = result * 31 + MathUtils.hash(getMax()); result = result * 31 + MathUtils.hash(getMean()); result = result * 31 + MathUtils.hash(getMin()); result = result * 31 + MathUtils.hash(getN()); result = result * 31 + MathUtils.hash(getSum()); result = result * 31 + MathUtils.hash(getSumSq()); result = result * 31 + MathUtils.hash(getSumLog()); result = result * 31 + getCovariance().hashCode(); return result; }