Java Code Examples for org.apache.commons.math3.stat.StatUtils#max()

/**
 * Given a set of likelihoods in log10, output 1- the probability of the most largest likelihood [ 1-p].
 * @param allLikelihoods a collection of likelihoods
 * @return 1 - (best like / sum (likes))
 */
public double getOneMinusPvalueFromLog10Likelihood (final double [] allLikelihoods) {
	// we clone the array so we don't change it.
	double [] likes=allLikelihoods.clone();
	Arrays.sort(likes);

	double maxValue = StatUtils.max(likes);

	double totalLikelihood=0;
	double allButBestLikelihood=0;

	for (int i=0; i<likes.length; i++) {
		double d = likes[i];
		d=d-maxValue;
		d=Math.pow(10, d);
		totalLikelihood+=d;
		if (i!=(likes.length-1))
			allButBestLikelihood+=d;
	}

	double result = allButBestLikelihood/totalLikelihood;
	if (result < Double.MIN_VALUE) result = Double.MIN_VALUE;
	return result;
}
 

/**
 * Given a set of likelihoods in log10, output the probability of the most largest likelihood [p].
 * @param allLikelihoods
 * @return
 */
public double getPvalueFromLog10Likelihood (final double [] allLikelihoods) {
	//TODO: is it better to clone the array here, or should the class handing off the array do the cloning?
	double [] likes=allLikelihoods.clone();
	Arrays.sort(likes);

	double maxValue = StatUtils.max(likes);
	double totalLikelihood=0;
	
	for (int i=0; i<likes.length; i++) {
		double d = likes[i];
		d=d-maxValue;
		d=Math.pow(10, d);
		totalLikelihood+=d;
	}

	double result = 1/totalLikelihood;
	if (result < Double.MIN_VALUE) result = Double.MIN_VALUE;
	return result;
}
 

/**
 * Returns a figure containing a QQ Plot describing the differences between the distribution of
 * values in the columns of interest
 *
 * @param title A title for the plot
 * @param measure The measure being compared on the plot (e.g "inches" or "height in inches"
 * @param xData The data to plot on the x Axis
 * @param yData The data to plot on the y Axis
 * @return A quantile plot
 */
public static Figure create(String title, String measure, double[] xData, double[] yData) {

  Preconditions.checkArgument(xData.length != 0, "x Data array is empty");
  Preconditions.checkArgument(yData.length != 0, "x Data array is empty");

  if (xData.length != yData.length) {
    double[] interpolatedData;
    if (xData.length < yData.length) {
      interpolatedData = interpolate(yData, xData.length);
      yData = interpolatedData;
    } else {
      interpolatedData = interpolate(xData, yData.length);
      xData = interpolatedData;
    }
  }

  Arrays.sort(xData);
  Arrays.sort(yData);

  double[] averagePoints = new double[xData.length];
  double[] differencePoints = new double[xData.length];
  for (int i = 0; i < xData.length; i++) {
    averagePoints[i] = (xData[i] + yData[i]) / 2.0;
    differencePoints[i] = (xData[i] - yData[i]);
  }

  double xMin = StatUtils.min(xData);
  double xMax = StatUtils.max(xData);
  double[] zeroLineX = {xMin, xMax};
  double[] zeroLineY = {0, 0};

  // Draw the line indicating equal distributions (this is zero in this plot)
  ScatterTrace trace1 =
      ScatterTrace.builder(zeroLineX, zeroLineY)
          .mode(ScatterTrace.Mode.LINE)
          .name("y = x")
          .build();

  // Draw the actual data points
  ScatterTrace trace2 =
      ScatterTrace.builder(averagePoints, differencePoints).name("mean x difference").build();

  Layout layout =
      Layout.builder()
          .title(title)
          .xAxis(Axis.builder().title("mean (" + measure + ")").build())
          .yAxis(Axis.builder().title("difference (" + measure + ")").build())
          .height(700)
          .width(900)
          .build();
  return new Figure(layout, trace1, trace2);
}
 

@Override
public Double summarize(NumericColumn<?> column) {
  double[] data = removeMissing(column);
  return StatUtils.max(data) - StatUtils.min(data);
}
 

@Override
public Double summarize(NumericColumn<?> column) {
  return StatUtils.max(removeMissing(column));
}
 

@Override
public Double summarize(NumericColumn<?> data) {
  return StatUtils.max(data.asDoubleArray()) + 1000;
}
 

/**
 * Returns a figure containing a QQ Plot describing the differences between the distribution of
 * values in the columns of interest
 *
 * @param title A title for the plot
 * @param measure The measure being compared on the plot (e.g "inches" or "height in inches"
 * @param xData The data to plot on the x Axis
 * @param yData The data to plot on the y Axis
 * @return A quantile plot
 */
public static Figure create(String title, String measure, double[] xData, double[] yData) {

  Preconditions.checkArgument(xData.length != 0, "x Data array is empty");
  Preconditions.checkArgument(yData.length != 0, "x Data array is empty");

  if (xData.length != yData.length) {
    double[] interpolatedData;
    if (xData.length < yData.length) {
      interpolatedData = interpolate(yData, xData.length);
      yData = interpolatedData;
    } else {
      interpolatedData = interpolate(xData, yData.length);
      xData = interpolatedData;
    }
  }

  Arrays.sort(xData);
  Arrays.sort(yData);

  double[] averagePoints = new double[xData.length];
  double[] differencePoints = new double[xData.length];
  for (int i = 0; i < xData.length; i++) {
    averagePoints[i] = (xData[i] + yData[i]) / 2.0;
    differencePoints[i] = (xData[i] - yData[i]);
  }

  double xMin = StatUtils.min(xData);
  double xMax = StatUtils.max(xData);
  double[] zeroLineX = {xMin, xMax};
  double[] zeroLineY = {0, 0};

  // Draw the line indicating equal distributions (this is zero in this plot)
  ScatterTrace trace1 =
      ScatterTrace.builder(zeroLineX, zeroLineY)
          .mode(ScatterTrace.Mode.LINE)
          .name("y = x")
          .build();

  // Draw the actual data points
  ScatterTrace trace2 =
      ScatterTrace.builder(averagePoints, differencePoints).name("mean x difference").build();

  Layout layout =
      Layout.builder()
          .title(title)
          .xAxis(Axis.builder().title("mean (" + measure + ")").build())
          .yAxis(Axis.builder().title("difference (" + measure + ")").build())
          .height(700)
          .width(900)
          .build();
  return new Figure(layout, trace1, trace2);
}
 

@Override
public Double summarize(NumericColumn<?> column) {
  double[] data = removeMissing(column);
  return StatUtils.max(data) - StatUtils.min(data);
}
 

@Override
public Double summarize(NumericColumn<?> column) {
  return StatUtils.max(removeMissing(column));
}
 

@Override
public Double summarize(NumericColumn<?> data) {
  return StatUtils.max(data.asDoubleArray()) + 1000;
}
 

/**
 * Calculates the max of specified column
 * @param colName the column for which the max will be calculated
 * @return max
 */
public double max(String colName) {
  return StatUtils.max(toDoubleArray(colName));
}