package com.github.lwhite1.tablesaw.reducing;
import com.github.lwhite1.tablesaw.api.DoubleColumn;
import com.github.lwhite1.tablesaw.api.FloatColumn;
import org.apache.commons.math3.stat.StatUtils;
import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics;
import org.apache.commons.math3.stat.descriptive.moment.Kurtosis;
import org.apache.commons.math3.stat.descriptive.moment.Skewness;
import org.apache.commons.math3.util.FastMath;
/**
* Contains common utilities for double and long types
*/
public class NumericReduceUtils {
// TODO(lwhite): Re-implement these methods to work natively with float[], instead of converting to double[]
/**
* A function that calculates the mean of the values in the column param
*/
public static NumericReduceFunction mean = new NumericReduceFunction() {
@Override
public String functionName() {
return "Mean";
}
@Override
public double reduce(double[] data) {
return StatUtils.mean(data);
}
};
/**
* A function that calculates the sum of the values in the column param
*/
public static NumericReduceFunction sum = new NumericReduceFunction() {
@Override
public String functionName() {
return "Sum";
}
@Override
public double reduce(double[] data) {
return StatUtils.sum(data);
}
@Override
public double reduce(FloatColumn floatColumn) {
float sum;
sum = 0.0f;
for (float value : floatColumn) {
if (value != Float.NaN) {
sum += value;
}
}
return sum;
}
@Override
public double reduce(DoubleColumn floatColumn) {
float sum;
sum = 0.0f;
for (double value : floatColumn) {
if (value != Float.NaN) {
sum += value;
}
}
return sum;
}
};
public static NumericReduceFunction median = new NumericReduceFunction() {
@Override
public String functionName() {
return "Median";
}
@Override
public double reduce(double[] data) {
return percentile(data, 50.0);
}
};
public static NumericReduceFunction n = new NumericReduceFunction() {
@Override
public String functionName() {
return "N";
}
//TODO: Consider whether we should provide a count without missing values
@Override
public double reduce(double[] data) {
return data.length;
}
};
public static NumericReduceFunction quartile1 = new NumericReduceFunction() {
@Override
public String functionName() {
return "First Quartile";
}
@Override
public double reduce(double[] data) {
return percentile(data, 25.0);
}
};
public static NumericReduceFunction quartile3 = new NumericReduceFunction() {
@Override
public String functionName() {
return "Third Quartile";
}
@Override
public double reduce(double[] data) {
return percentile(data, 75.0);
}
};
public static NumericReduceFunction percentile90 = new NumericReduceFunction() {
@Override
public String functionName() {
return "90th Percentile";
}
@Override
public double reduce(double[] data) {
return percentile(data, 90.0);
}
};
public static NumericReduceFunction percentile95 = new NumericReduceFunction() {
@Override
public String functionName() {
return "95th Percentile";
}
@Override
public double reduce(double[] data) {
return percentile(data, 95.0);
}
};
public static NumericReduceFunction percentile99 = new NumericReduceFunction() {
@Override
public String functionName() {
return "99th Percentile";
}
@Override
public double reduce(double[] data) {
return percentile(data, 99.0);
}
};
public static NumericReduceFunction range = new NumericReduceFunction() {
@Override
public String functionName() {
return "Range";
}
@Override
public double reduce(double[] data) {
return StatUtils.max(data) - StatUtils.min(data);
}
};
public static NumericReduceFunction min = new NumericReduceFunction() {
@Override
public String functionName() {
return "Min";
}
@Override
public double reduce(double[] data) {
return StatUtils.min(data);
}
@Override
public double reduce(FloatColumn data) {
if (data.size() == 0) {
return Float.NaN;
}
float min = data.firstElement();
for (float value : data) {
if (!Float.isNaN(value)) {
min = (min < value) ? min : value;
}
}
return min;
}
};
public static NumericReduceFunction max = new NumericReduceFunction() {
@Override
public String functionName() {
return "Max";
}
@Override
public double reduce(double[] data) {
return StatUtils.max(data);
}
};
public static NumericReduceFunction product = new NumericReduceFunction() {
@Override
public String functionName() {
return "Product";
}
@Override
public double reduce(double[] data) {
return StatUtils.product(data);
}
@Override
public double reduce(FloatColumn data) {
float product = 1.0f;
boolean empty = true;
for (float value : data) {
if (value != Float.NaN) {
empty = false;
product *= value;
}
}
if (empty) {
return Float.NaN;
}
return product;
}
};
public static NumericReduceFunction geometricMean = new NumericReduceFunction() {
@Override
public String functionName() {
return "Geometric Mean";
}
@Override
public double reduce(double[] data) {
return StatUtils.geometricMean(data);
}
};
public static NumericReduceFunction populationVariance = new NumericReduceFunction() {
@Override
public String functionName() {
return "Population Variance";
}
@Override
public double reduce(double[] data) {
return StatUtils.populationVariance(data);
}
};
/**
* Returns the quadratic mean, aka, the root-mean-square
*/
public static NumericReduceFunction quadraticMean = new NumericReduceFunction() {
@Override
public String functionName() {
return "Quadratic Mean";
}
@Override
public double reduce(double[] data) {
return new DescriptiveStatistics(data).getQuadraticMean();
}
};
public static NumericReduceFunction kurtosis = new NumericReduceFunction() {
@Override
public String functionName() {
return "Kurtosis";
}
@Override
public double reduce(double[] data) {
return new Kurtosis().evaluate(data, 0, data.length);
}
};
public static NumericReduceFunction skewness = new NumericReduceFunction() {
@Override
public String functionName() {
return "Skewness";
}
@Override
public double reduce(double[] data) {
return new Skewness().evaluate(data, 0, data.length);
}
};
public static NumericReduceFunction sumOfSquares = new NumericReduceFunction() {
@Override
public String functionName() {
return "Sum of Squares";
}
@Override
public double reduce(double[] data) {
return StatUtils.sumSq(data);
}
};
public static NumericReduceFunction sumOfLogs = new NumericReduceFunction() {
@Override
public String functionName() {
return "Sum of Logs";
}
@Override
public double reduce(double[] data) {
return StatUtils.sumLog(data);
}
};
public static NumericReduceFunction variance = new NumericReduceFunction() {
@Override
public String functionName() {
return "Variance";
}
@Override
public double reduce(double[] data) {
return StatUtils.variance(data);
}
/**
* Returns the (sample) variance of the available values.
* <p>
* <p>This method returns the bias-corrected sample variance (using {@code n - 1} in
* the denominator).
*
* @return The variance, Double.NaN if no values have been added
* or 0.0 for a single value set.
*/
@Override
public double reduce(FloatColumn column) {
double avg = mean.reduce(column);
double sumSquaredDiffs = 0.0f;
for (float value : column) {
double diff = value - avg;
double sqrdDiff = diff * diff;
sumSquaredDiffs += sqrdDiff;
}
return sumSquaredDiffs / (column.size() - 1);
}
};
public static NumericReduceFunction stdDev = new NumericReduceFunction() {
@Override
public String functionName() {
return "Std. Deviation";
}
@Override
public double reduce(double[] data) {
return Math.sqrt(StatUtils.variance(data));
}
/**
* Returns the standard deviation of the available values.
*
* @return The standard deviation, Double.NaN if no values have been added
* or 0.0 for a single value set.
*/
public double stdDev(FloatColumn values) {
float stdDev = Float.NaN;
int N = values.size();
if (N > 0) {
if (N > 1) {
stdDev = (float) FastMath.sqrt(variance.reduce(values));
} else {
stdDev = 0.0f;
}
}
return stdDev;
}
};
public static double percentile(double[] data, double percentile) {
return StatUtils.percentile(data, percentile);
}
// TODO(lwhite): These are two column reductions. We need a class for that
public static double meanDifference(FloatColumn column1, FloatColumn column2) {
return StatUtils.meanDifference(column1.toDoubleArray(), column2.toDoubleArray());
}
public static double sumDifference(FloatColumn column1, FloatColumn column2) {
return StatUtils.sumDifference(column1.toDoubleArray(), column2.toDoubleArray());
}
}