Util (Tribuo 4.0.2 API)

java.lang.Object
- org.tribuo.util.Util

```
public final class Util
extends Object
```
Ye olde util class.
Basically full of vector and RNG operations.

Method Summary

All Methods Static Methods Concrete Methods
Modifier and Type	Method and Description
`static <T extends Comparable<T>> com.oracle.labs.mlrg.olcut.util.Pair<Integer,T>`	`argmax(List<T> values)` Find the index of the maximum value in a list.
`static <T extends Comparable<T>> com.oracle.labs.mlrg.olcut.util.Pair<Integer,T>`	`argmin(List<T> values)` Find the index of the minimum value in a list.
`static double`	`auc(double[] x, double[] y)` Calculates the area under the curve, bounded below by the x axis.
`static <T> int`	`binarySearch(List<? extends Comparable<? super T>> list, T key)` A binary search function.
`static <T> int`	`binarySearch(List<? extends Comparable<? super T>> list, T key, int low, int high)` A binary search function.
`static <T> int`	`binarySearch(List<? extends T> list, int key, ToIntFunction<T> extractionFunc)` A binary search function.
`static int[]`	`cumulativeSum(boolean[] input)` Produces a cumulative sum array.
`static double[]`	`cumulativeSum(double[] input)` Produces a cumulative sum array.
`static int[]`	`differencesIndices(double[] input)` Returns an array containing the indices where values are different.
`static int[]`	`differencesIndices(double[] input, double tolerance)` Returns an array containing the indices where values are different.
`static String`	`formatDuration(long startMillis, long stopMillis)` Formats a duration given two times in milliseconds.
`static int[]`	`generateBootstrapIndices(int size, Random rng)` Draws a bootstrap sample of indices.
`static int[]`	`generateBootstrapIndices(int size, SplittableRandom rng)` Draws a bootstrap sample of indices.
`static double[]`	`generateCDF(double[] pmf)` Generates a cumulative distribution function from the supplied probability mass function.
`static double[]`	`generateCDF(float[] pmf)` Generates a cumulative distribution function from the supplied probability mass function.
`static double[]`	`generateCDF(long[] counts, long countSum)` Generates a cumulative distribution function from the supplied probability mass function.
`static float[]`	`generateUniformFloatVector(int length, float value)`
`static double[]`	`generateUniformVector(int length, double value)`
`static float[]`	`generateUniformVector(int length, float value)`
`static int[]`	`generateWeightedIndicesSample(int size, double[] weights, Random rng)` Generates a sample of indices weighted by the provided weights.
`static int[]`	`generateWeightedIndicesSample(int size, double[] weights, SplittableRandom rng)` Generates a sample of indices weighted by the provided weights.
`static int[]`	`generateWeightedIndicesSample(int size, float[] weights, Random rng)` Generates a sample of indices weighted by the provided weights.
`static int[]`	`generateWeightedIndicesSample(int size, float[] weights, SplittableRandom rng)` Generates a sample of indices weighted by the provided weights.
`static int[]`	`generateWeightedIndicesSampleWithoutReplacement(int size, double[] weights, Random rng)` Generates a sample of indices weighted by the provided weights without replacement.
`static int[]`	`generateWeightedIndicesSampleWithoutReplacement(int size, float[] weights, Random rng)` Generates a sample of indices weighted by the provided weights without replacement.
`static void`	`inPlaceAdd(double[] input, double[] update)`
`static void`	`inPlaceAdd(float[] input, float[] update)`
`static double[]`	`inplaceNormalizeToDistribution(double[] input)`
`static void`	`inplaceNormalizeToDistribution(float[] input)`
`static void`	`inPlaceSubtract(double[] input, double[] update)`
`static void`	`inPlaceSubtract(float[] input, float[] update)`
`static void`	`logVector(Logger otherLogger, Level level, double[] input)`
`static void`	`logVector(Logger otherLogger, Level level, float[] input)`
`static <V extends Number> double`	`mean(Collection<V> values)`
`static double`	`mean(double[] inputs)` Returns the mean of the input array.
`static double`	`mean(double[] array, int length)`
`static com.oracle.labs.mlrg.olcut.util.Pair<Double,Double>`	`meanAndVariance(double[] inputs)` Returns the mean and variance of the input.
`static com.oracle.labs.mlrg.olcut.util.Pair<Double,Double>`	`meanAndVariance(double[] inputs, int length)` Returns the mean and variance of the input's first length elements.
`static double[]`	`normalizeToDistribution(double[] input)`
`static double[]`	`normalizeToDistribution(float[] input)`
`static int[]`	`randperm(int size, Random rng)` Shuffles the indices in the range [0,size).
`static int[]`	`randperm(int size, SplittableRandom rng)` Shuffles the indices in the range [0,size).
`static void`	`randpermInPlace(int[] input, Random rng)` Shuffles the input.
`static void`	`randpermInPlace(int[] input, SplittableRandom rng)` Shuffles the input.
`static int`	`sampleFromCDF(double[] cdf, Random rng)` Samples an index from the supplied cdf.
`static int`	`sampleFromCDF(double[] cdf, SplittableRandom rng)` Samples an index from the supplied cdf.
`static int[]`	`sampleInts(Random rng, int size, int range)`
`static <V extends Number> double`	`sampleStandardDeviation(Collection<V> values)`
`static <V extends Number> double`	`sampleVariance(Collection<V> values)`
`static int[]`	`sortedDifference(int[] first, int[] second)` Expects sorted input arrays.
`static double`	`sum(double[] input)`
`static double`	`sum(double[] array, int length)`
`static float`	`sum(float[] input)`
`static float`	`sum(float[] array, int length)`
`static float`	`sum(int[] indices, float[] input)`
`static float`	`sum(int[] indices, int indicesLength, float[] input)`
`static double[]`	`toDoubleArray(float[] floats)` Convert an array of floats to an array of doubles.
`static float[]`	`toFloatArray(double[] doubles)` Convert an array of doubles to an array of floats.
`static double[]`	`toPrimitiveDouble(List<Double> input)`
`static double[]`	`toPrimitiveDoubleFromInteger(List<Integer> input)`
`static float[]`	`toPrimitiveFloat(List<Float> input)`
`static int[]`	`toPrimitiveInt(List<Integer> input)`
`static long[]`	`toPrimitiveLong(List<Long> input)`
`static double`	`vectorNorm(double[] input)`
`static double`	`weightedMean(double[] inputs, double[] weights)` Returns the weighted mean of the input.
`static double`	`weightedMean(double[] array, float[] weights, int length)`
`static double`	`weightedSum(double[] array, float[] weights, int length)`

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Method Detail
  - argmax
```
public static <T extends Comparable<T>> com.oracle.labs.mlrg.olcut.util.Pair<Integer,T> argmax(List<T> values)
```
    Find the index of the maximum value in a list.
    
    Type Parameters:
    
    T - the type of the values (must implement Comparable)
    
    Parameters:
    
    values - list
    
    Returns:
    
    a pair: (index of the max value, max value)
  - argmin
```
public static <T extends Comparable<T>> com.oracle.labs.mlrg.olcut.util.Pair<Integer,T> argmin(List<T> values)
```
    Find the index of the minimum value in a list.
    
    Type Parameters:
    
    T - the type of the values (must implement Comparable)
    
    Parameters:
    
    values - list
    
    Returns:
    
    a pair: (index of the min value, min value)
  - toFloatArray
```
public static float[] toFloatArray(double[] doubles)
```
    Convert an array of doubles to an array of floats.
    
    Parameters:
    
    doubles - The array of doubles to convert.
    
    Returns:
    
    An array of floats.
  - toDoubleArray
```
public static double[] toDoubleArray(float[] floats)
```
    Convert an array of floats to an array of doubles.
    
    Parameters:
    
    floats - The array of floats to convert.
    
    Returns:
    
    An array of doubles.
  - randperm
```
public static int[] randperm(int size,
                             Random rng)
```
    Shuffles the indices in the range [0,size).
    
    Parameters:
    
    size - The number of elements.
    
    rng - The random number generator to use.
    
    Returns:
    
    A random permutation of the values in the range (0, size-1).
  - randperm
```
public static int[] randperm(int size,
                             SplittableRandom rng)
```
    Shuffles the indices in the range [0,size).
    
    Parameters:
    
    size - The number of elements.
    
    rng - The random number generator to use.
    
    Returns:
    
    A random permutation of the values in the range (0, size-1).
  - randpermInPlace
```
public static void randpermInPlace(int[] input,
                                   Random rng)
```
    Shuffles the input.
    
    Parameters:
    
    input - The array to shuffle.
    
    rng - The random number generator to use.
  - randpermInPlace
```
public static void randpermInPlace(int[] input,
                                   SplittableRandom rng)
```
    Shuffles the input.
    
    Parameters:
    
    input - The array to shuffle.
    
    rng - The random number generator to use.
  - generateBootstrapIndices
```
public static int[] generateBootstrapIndices(int size,
                                             Random rng)
```
    Draws a bootstrap sample of indices.
    
    Parameters:
    
    size - Size of the sample to generate.
    
    rng - The RNG to use.
    
    Returns:
    
    A bootstrap sample.
  - generateBootstrapIndices
```
public static int[] generateBootstrapIndices(int size,
                                             SplittableRandom rng)
```
    Draws a bootstrap sample of indices.
    
    Parameters:
    
    size - Size of the sample to generate.
    
    rng - The RNG to use.
    
    Returns:
    
    A bootstrap sample.
  - generateWeightedIndicesSample
```
public static int[] generateWeightedIndicesSample(int size,
                                                  double[] weights,
                                                  Random rng)
```
    Generates a sample of indices weighted by the provided weights.
    
    Parameters:
    
    size - Size of the sample to generate.
    
    weights - A probability mass function of weights.
    
    rng - The RNG to use.
    
    Returns:
    
    A sample with replacement from weights.
  - generateWeightedIndicesSample
```
public static int[] generateWeightedIndicesSample(int size,
                                                  float[] weights,
                                                  Random rng)
```
    Generates a sample of indices weighted by the provided weights.
    
    Parameters:
    
    size - Size of the sample to generate.
    
    weights - A probability mass function of weights.
    
    rng - The RNG to use.
    
    Returns:
    
    A sample with replacement from weights.
  - generateWeightedIndicesSample
```
public static int[] generateWeightedIndicesSample(int size,
                                                  double[] weights,
                                                  SplittableRandom rng)
```
    Generates a sample of indices weighted by the provided weights.
    
    Parameters:
    
    size - Size of the sample to generate.
    
    weights - A probability mass function of weights.
    
    rng - The RNG to use.
    
    Returns:
    
    A sample with replacement from weights.
  - generateWeightedIndicesSample
```
public static int[] generateWeightedIndicesSample(int size,
                                                  float[] weights,
                                                  SplittableRandom rng)
```
    Generates a sample of indices weighted by the provided weights.
    
    Parameters:
    
    size - Size of the sample to generate.
    
    weights - A probability mass function of weights.
    
    rng - The RNG to use.
    
    Returns:
    
    A sample with replacement from weights.
  - generateWeightedIndicesSampleWithoutReplacement
```
public static int[] generateWeightedIndicesSampleWithoutReplacement(int size,
                                                                    double[] weights,
                                                                    Random rng)
```
    Generates a sample of indices weighted by the provided weights without replacement. Does not recalculate proportions in-between samples. Use judiciously.
    
    Parameters:
    
    size - Size of the sample to generate
    
    weights - A probability mass function of weights
    
    rng - The RNG to use
    
    Returns:
    
    A sample without replacement from weights
  - generateWeightedIndicesSampleWithoutReplacement
```
public static int[] generateWeightedIndicesSampleWithoutReplacement(int size,
                                                                    float[] weights,
                                                                    Random rng)
```
    Generates a sample of indices weighted by the provided weights without replacement. Does not recalculate proportions in-between samples. Use judiciously.
    
    Parameters:
    
    size - Size of the sample to generate
    
    weights - A probability mass function of weights
    
    rng - The RNG to use
    
    Returns:
    
    A sample without replacement from weights
  - generateCDF
```
public static double[] generateCDF(double[] pmf)
```
    Generates a cumulative distribution function from the supplied probability mass function.
    
    Parameters:
    
    pmf - The probability mass function (i.e., the probability distribution).
    
    Returns:
    
    The CDF.
  - cumulativeSum
```
public static double[] cumulativeSum(double[] input)
```
    Produces a cumulative sum array.
    
    Parameters:
    
    input - The input to sum.
    
    Returns:
    
    The cumulative sum.
  - cumulativeSum
```
public static int[] cumulativeSum(boolean[] input)
```
    Produces a cumulative sum array.
    
    Parameters:
    
    input - The input to sum.
    
    Returns:
    
    The cumulative sum.
  - generateCDF
```
public static double[] generateCDF(float[] pmf)
```
    Generates a cumulative distribution function from the supplied probability mass function.
    
    Parameters:
    
    pmf - The probability mass function (i.e., the probability distribution).
    
    Returns:
    
    The CDF.
  - generateCDF
```
public static double[] generateCDF(long[] counts,
                                   long countSum)
```
    Generates a cumulative distribution function from the supplied probability mass function.
    
    Parameters:
    
    counts - The frequency counts.
    
    countSum - The sum of the counts.
    
    Returns:
    
    The CDF.
  - sampleFromCDF
```
public static int sampleFromCDF(double[] cdf,
                                Random rng)
```
    Samples an index from the supplied cdf.
    
    Parameters:
    
    cdf - The cdf to sample from.
    
    rng - The rng to use.
    
    Returns:
    
    A sample.
  - sampleFromCDF
```
public static int sampleFromCDF(double[] cdf,
                                SplittableRandom rng)
```
    Samples an index from the supplied cdf.
    
    Parameters:
    
    cdf - The cdf to sample from.
    
    rng - The rng to use.
    
    Returns:
    
    A sample.
  - generateUniformVector
```
public static double[] generateUniformVector(int length,
                                             double value)
```
  - generateUniformVector
```
public static float[] generateUniformVector(int length,
                                            float value)
```
  - normalizeToDistribution
```
public static double[] normalizeToDistribution(double[] input)
```
  - normalizeToDistribution
```
public static double[] normalizeToDistribution(float[] input)
```
  - inplaceNormalizeToDistribution
```
public static double[] inplaceNormalizeToDistribution(double[] input)
```
  - inplaceNormalizeToDistribution
```
public static void inplaceNormalizeToDistribution(float[] input)
```
  - logVector
```
public static void logVector(Logger otherLogger,
                             Level level,
                             double[] input)
```
  - logVector
```
public static void logVector(Logger otherLogger,
                             Level level,
                             float[] input)
```
  - toPrimitiveDoubleFromInteger
```
public static double[] toPrimitiveDoubleFromInteger(List<Integer> input)
```
  - toPrimitiveDouble
```
public static double[] toPrimitiveDouble(List<Double> input)
```
  - toPrimitiveFloat
```
public static float[] toPrimitiveFloat(List<Float> input)
```
  - toPrimitiveInt
```
public static int[] toPrimitiveInt(List<Integer> input)
```
  - toPrimitiveLong
```
public static long[] toPrimitiveLong(List<Long> input)
```
  - sampleInts
```
public static int[] sampleInts(Random rng,
                               int size,
                               int range)
```
  - inPlaceAdd
```
public static void inPlaceAdd(double[] input,
                              double[] update)
```
  - inPlaceSubtract
```
public static void inPlaceSubtract(double[] input,
                                   double[] update)
```
  - inPlaceAdd
```
public static void inPlaceAdd(float[] input,
                              float[] update)
```
  - inPlaceSubtract
```
public static void inPlaceSubtract(float[] input,
                                   float[] update)
```
  - vectorNorm
```
public static double vectorNorm(double[] input)
```
  - sum
```
public static double sum(double[] input)
```
  - sum
```
public static float sum(float[] input)
```
  - sum
```
public static double sum(double[] array,
                         int length)
```
  - sum
```
public static float sum(float[] array,
                        int length)
```
  - sum
```
public static float sum(int[] indices,
                        int indicesLength,
                        float[] input)
```
  - sum
```
public static float sum(int[] indices,
                        float[] input)
```
  - generateUniformFloatVector
```
public static float[] generateUniformFloatVector(int length,
                                                 float value)
```
  - binarySearch
```
public static <T> int binarySearch(List<? extends Comparable<? super T>> list,
                                   T key)
```
    A binary search function.
    
    Type Parameters:
    
    T - Type of the list, must implement Comparable.
    
    Parameters:
    
    list - Input list, must be ordered.
    
    key - Key to search for.
    
    Returns:
    
    the index of the search key, if it is contained in the list; otherwise, (-(insertion point) - 1). The insertion point is defined as the point at which the key would be inserted into the list: the index of the first element greater than the key, or list.size() if all elements in the list are less than the specified key. Note that this guarantees that the return value will be >= 0 if and only if the key is found.
  - binarySearch
```
public static <T> int binarySearch(List<? extends Comparable<? super T>> list,
                                   T key,
                                   int low,
                                   int high)
```
    A binary search function.
    
    Type Parameters:
    
    T - Type of the list, must implement Comparable.
    
    Parameters:
    
    list - Input list, must be ordered.
    
    key - Key to search for.
    
    low - Starting index.
    
    high - End index (will be searched).
    
    Returns:
    
    the index of the search key, if it is contained in the list; otherwise, (-(insertion point) - 1). The insertion point is defined as the point at which the key would be inserted into the list: the index of the first element greater than the key, or high if all elements in the list are less than the specified key. Note that this guarantees that the return value will be >= 0 if and only if the key is found.
  - binarySearch
```
public static <T> int binarySearch(List<? extends T> list,
                                   int key,
                                   ToIntFunction<T> extractionFunc)
```
    A binary search function.
    
    Type Parameters:
    
    T - Type of the list, must implement Comparable.
    
    Parameters:
    
    list - Input list, must be ordered.
    
    key - Key to search for.
    
    extractionFunc - Takes a T and generates an int which can be used for comparison using int's natural ordering.
    
    Returns:
    
    the index of the search key, if it is contained in the list; otherwise, (-(insertion point) - 1). The insertion point is defined as the point at which the key would be inserted into the list: the index of the first element greater than the key, or high if all elements in the list are less than the specified key. Note that this guarantees that the return value will be >= 0 if and only if the key is found.
  - auc
```
public static double auc(double[] x,
                         double[] y)
```
    Calculates the area under the curve, bounded below by the x axis.
    Uses linear interpolation between the points on the x axis, i.e., trapezoidal integration.
    The x axis must be increasing.
    
    Parameters:
    
    x - The x points to evaluate.
    
    y - The corresponding heights.
    
    Returns:
    
    The AUC.
  - meanAndVariance
```
public static com.oracle.labs.mlrg.olcut.util.Pair<Double,Double> meanAndVariance(double[] inputs)
```
    Returns the mean and variance of the input.
    
    Parameters:
    
    inputs - The input array.
    
    Returns:
    
    The mean and variance of the inputs. The mean is the first element, the variance is the second.
  - meanAndVariance
```
public static com.oracle.labs.mlrg.olcut.util.Pair<Double,Double> meanAndVariance(double[] inputs,
                                                                                  int length)
```
    Returns the mean and variance of the input's first length elements.
    
    Parameters:
    
    inputs - The input array.
    
    length - The number of elements to use.
    
    Returns:
    
    The mean and variance of the inputs. The mean is the first element, the variance is the second.
  - weightedMean
```
public static double weightedMean(double[] inputs,
                                  double[] weights)
```
    Returns the weighted mean of the input.
    Throws IllegalArgumentException if the two arrays are not the same length.
    
    Parameters:
    
    inputs - The input array.
    
    weights - The weights to use.
    
    Returns:
    
    The weighted mean.
  - mean
```
public static double mean(double[] inputs)
```
    Returns the mean of the input array.
    
    Parameters:
    
    inputs - The input array.
    
    Returns:
    
    The mean of inputs.
  - mean
```
public static double mean(double[] array,
                          int length)
```
  - mean
```
public static <V extends Number> double mean(Collection<V> values)
```
  - sampleVariance
```
public static <V extends Number> double sampleVariance(Collection<V> values)
```
  - sampleStandardDeviation
```
public static <V extends Number> double sampleStandardDeviation(Collection<V> values)
```
  - weightedMean
```
public static double weightedMean(double[] array,
                                  float[] weights,
                                  int length)
```
  - weightedSum
```
public static double weightedSum(double[] array,
                                 float[] weights,
                                 int length)
```
  - differencesIndices
```
public static int[] differencesIndices(double[] input)
```
    Returns an array containing the indices where values are different. Basically a combination of np.where and np.diff.
    Stores an index if the value after it is different. Always stores the final index.
    Uses a default tolerance of 1e-12.
    
    Parameters:
    
    input - Input array.
    
    Returns:
    
    An array containing the indices where the input changes.
  - differencesIndices
```
public static int[] differencesIndices(double[] input,
                                       double tolerance)
```
    Returns an array containing the indices where values are different. Basically a combination of np.where and np.diff.
    Stores an index if the value after it is different. Always stores the final index.
    
    Parameters:
    
    input - Input array.
    
    tolerance - Tolerance to determine a difference.
    
    Returns:
    
    An array containing the indices where the input changes.
  - formatDuration
```
public static String formatDuration(long startMillis,
                                    long stopMillis)
```
    Formats a duration given two times in milliseconds.
    Format string is - (%02d:%02d:%02d:%03d) or (%d days, %02d:%02d:%02d:%03d)
    
    Parameters:
    
    startMillis - Start time in ms.
    
    stopMillis - End time in ms.
    
    Returns:
    
    A formatted string measuring time in hours, minutes, second and milliseconds.
  - sortedDifference
```
public static int[] sortedDifference(int[] first,
                                     int[] second)
```
    Expects sorted input arrays. Returns an array containing all the elements in first that are not in second.
    
    Parameters:
    
    first - The first sorted array.
    
    second - The second sorted array.
    
    Returns:
    
    An array containing all the elements of first that aren't in second.

Class Util

Method Summary

Methods inherited from class java.lang.Object

Method Detail

argmax

argmin

toFloatArray

toDoubleArray

randperm

randperm

randpermInPlace

randpermInPlace

generateBootstrapIndices

generateBootstrapIndices

generateWeightedIndicesSample

generateWeightedIndicesSample

generateWeightedIndicesSample

generateWeightedIndicesSample

generateWeightedIndicesSampleWithoutReplacement

generateWeightedIndicesSampleWithoutReplacement

generateCDF

cumulativeSum

cumulativeSum

generateCDF

generateCDF

sampleFromCDF

sampleFromCDF

generateUniformVector

generateUniformVector

normalizeToDistribution

normalizeToDistribution

inplaceNormalizeToDistribution

inplaceNormalizeToDistribution

logVector

logVector

toPrimitiveDoubleFromInteger

toPrimitiveDouble

toPrimitiveFloat

toPrimitiveInt

toPrimitiveLong

sampleInts

inPlaceAdd

inPlaceSubtract

inPlaceAdd

inPlaceSubtract

vectorNorm

sum

sum

sum

sum

sum

sum

generateUniformFloatVector

binarySearch

binarySearch

binarySearch

auc

meanAndVariance

meanAndVariance

weightedMean

mean

mean

mean

sampleVariance

sampleStandardDeviation

weightedMean

weightedSum

differencesIndices

differencesIndices

formatDuration

sortedDifference