Class Util

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 Details

    • 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.

    • randpermInPlace

      public static void randpermInPlace(double[] 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.

    • standardize

      public static double[] standardize(double[] input, double mean, double variance)
      Standardizes the input so it has zero mean and unit variance, i.e., subtracts the mean and divides by the variance.
      Parameters:
      input - The input to standardize.
      mean - The mean.
      variance - The variance.
      Returns:
      The standardized input.

    • standardizeInPlace

      public static void standardizeInPlace(double[] input, double mean, double variance)
      Standardizes the input so it has zero mean and unit variance, i.e., subtracts the mean and divides by the variance. Operates in place on the input array.
      Parameters:
      input - The input to standardize.
      mean - The mean.
      variance - The variance.