return ensureInUnitRange(sumOfProductsOfDeltas / Math.sqrt(productOfSumsOfSquaresOfDeltas));
  }

  /**
   * Returns a linear transformation giving the best fit to the data according to <a
   * href="http://mathworld.wolfram.com/LeastSquaresFitting.html">Ordinary Least Squares linear
   * regression</a> of {@code y} as a function of {@code x}. The count must be greater than one, and

    return ensureInUnitRange(sumOfProductsOfDeltas / Math.sqrt(productOfSumsOfSquaresOfDeltas));
  }

  /**
   * Returns a linear transformation giving the best fit to the data according to <a
   * href="http://mathworld.wolfram.com/LeastSquaresFitting.html">Ordinary Least Squares linear
   * regression</a> of {@code y} as a function of {@code x}. The count must be greater than one, and

// a bit vector with (length of input) * (length of prog) bits,
// to make sure it never explores the same (character position, instruction)
// state multiple times. This limits the search to run in time linear in
// the length of the test.
//
// backtrack is a fast replacement for the NFA code on small
// regexps when onepass cannot be used.

package regexp

import (
	"regexp/syntax"
	"sync"
)

    }
    return this;
  }

  /**
   * Adds each member of {@code elements} as a candidate for the top {@code k} elements. This
   * operation takes amortized linear time in the length of {@code elements}.
   *
   * <p>If all input data to this {@code TopKSelector} is in a single {@code Iterable}, prefer

	// This is a bit complicated but the obvious alternative,
	// namely a hand-written search like in most shell glob matchers,
	// is too easy to make accidentally exponential.
	// Using package regexp guarantees linear-time matching.

	const vendorChar = "\x00"

	if vendorExclude && strings.Contains(pattern, vendorChar) {
		return func(name string) bool { return false }
	}

	re := regexp.QuoteMeta(pattern)
	wild := `.*`

    }
    return this;
  }

  /**
   * Adds each member of {@code elements} as a candidate for the top {@code k} elements. This
   * operation takes amortized linear time in the length of {@code elements}.
   *
   * <p>If all input data to this {@code TopKSelector} is in a single {@code Iterable}, prefer

		}
		s.retryAfter.Store(retryAfter)
		s.retryAfterUpdateUnix = unixTime
		return
	}

	if droppedRequests < retryAfter && retryAfter > 1 {
		// We try to mimc the TCP algorithm and thus are linearly
		// scaling down the retryAfter here.
		retryAfter--
		s.retryAfter.Store(retryAfter)
		return
	}
}

// droppedRequestsTracker implement DroppedRequestsTracker interface

		t1 := time.Now()
		f(n)
		return time.Since(t1)
	}

	t0 := time.Now()

	n := tries
	fails := 0
	for {
		t1 := timeF(n)
		t2 := timeF(2 * n)

		// should be 2x (linear); allow up to 3x
		if t2 < 3*t1 {
			if false {
				fmt.Println(typ, "\t", time.Since(t0))
			}
			return
		}
		// If n ops run in under a second and the ratio

    return ensureInUnitRange(sumOfProductsOfDeltas / Math.sqrt(productOfSumsOfSquaresOfDeltas));
  }

  /**
   * Returns a linear transformation giving the best fit to the data according to <a
   * href="http://mathworld.wolfram.com/LeastSquaresFitting.html">Ordinary Least Squares linear
   * regression</a> of {@code y} as a function of {@code x}. The count must be greater than one, and

    return ensureInUnitRange(sumOfProductsOfDeltas / Math.sqrt(productOfSumsOfSquaresOfDeltas));
  }

  /**
   * Returns a linear transformation giving the best fit to the data according to <a
   * href="http://mathworld.wolfram.com/LeastSquaresFitting.html">Ordinary Least Squares linear
   * regression</a> of {@code y} as a function of {@code x}. The count must be greater than one, and