*
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the
   * actual {@code y} values of the data and the values predicted by the fit for the {@code x}

   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the
   * actual {@code y} values of the data and the values predicted by the fit for the {@code x}

     * existing values in the execution request that are controlled by the toolchains. Hence, it is expected that this
     * method is called on a new/empty execution request before the caller mutates it to fit its needs.
     *
     * @param request The execution request to populate, must not be {@code null}.
     * @param toolchains The toolchains to copy into the execution request, may be {@code null}.

   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the
   * actual {@code y} values of the data and the values predicted by the fit for the {@code x}

   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the
   * actual {@code y} values of the data and the values predicted by the fit for the {@code x}

      LinearTransformation fit = accumulator.leastSquaresFit();
      LinearTransformation fitByAddAllPartitionedPairedStats =
          accumulatorByAddAllPartitionedPairedStats.leastSquaresFit();
      if (values.hasAnyNonFinite()) {
        assertLinearTransformationNaN(fit);
        assertLinearTransformationNaN(fitByAddAllPartitionedPairedStats);
      } else {

  private static BigInteger oldSlowFactorial(int n1, int n2) {
    assert n1 <= n2;
    if (IntMath.log2(n2, CEILING) * (n2 - n1) < Long.SIZE - 1) {
      // the result will definitely fit into a long
      long result = 1;
      for (int i = n1 + 1; i <= n2; i++) {
        result *= i;
      }
      return BigInteger.valueOf(result);
    }

    /*

# Gradle architecture documentation

This directory contains documentation that describes Gradle's architecture and how the various pieces fit together and work.

## Architecture decision records (ADRs)

The Gradle team uses ADRs to record architectural decisions that the team has made.

See [Architecture decisions records](standards) for the list of ADRs.

**Experimental!**

We'd like to capture our architectural decisions about the build tool as [Architectural Decision Records (ADRs)](https://adr.github.io/).
For now we just have this global repository of ADRs.
If we see fit, we can break these out to per-platform ones, or keep a hybrid approach to having global and platform-specific ADSs.

Our aim is to keep the process lightweight and approachable.

      PairedStats stats = createPairedStatsOf(values.asIterable(), OTHER_MANY_VALUES);
      LinearTransformation fit = stats.leastSquaresFit();
      if (values.hasAnyNonFinite()) {
        assertLinearTransformationNaN(fit);
      } else {
        assertDiagonalLinearTransformation(
            fit,
            stats.xStats().mean(),
            stats.yStats().mean(),
            stats.xStats().populationVariance(),