"barAlignment": 0,
            "drawStyle": "line",
            "fillOpacity": 10,
            "gradientMode": "none",
            "hideFrom": {
              "legend": false,
              "tooltip": false,
              "viz": false
            },
            "insertNulls": false,
            "lineInterpolation": "linear",
            "lineWidth": 1,
            "pointSize": 5,

            "barAlignment": 0,
            "drawStyle": "line",
            "fillOpacity": 10,
            "gradientMode": "none",
            "hideFrom": {
              "legend": false,
              "tooltip": false,
              "viz": false
            },
            "insertNulls": false,
            "lineInterpolation": "linear",
            "lineWidth": 1,
            "pointSize": 5,

            "barAlignment": 0,
            "drawStyle": "line",
            "fillOpacity": 10,
            "gradientMode": "none",
            "hideFrom": {
              "legend": false,
              "tooltip": false,
              "viz": false
            },
            "insertNulls": false,
            "lineInterpolation": "linear",
            "lineWidth": 1,
            "pointSize": 5,

tegoryAxes\":[{\"id\":\"CategoryAxis-1\",\"type\":\"category\",\"position\":\"bottom\",\"show\":true,\"style\":{},\"scale\":{\"type\":\"linear\"},\"labels\":{\"show\":true,\"truncate\":100},\"title\":{}}],\"valueAxes\":[{\"id\":\"ValueAxis-1\",\"name\":\"LeftAxis-1\",\"type\":\"value\",\"position\":\"left\",\"show\":true,\"style\":{},\"scale\":{\"type\":\"linear\",\"mode\":\"normal\"},\"labels\":{\"show\":true,\"rotate\":0,\"filter\":false,\"truncate\":100},\"title\":{\"text\":\"Query Count\"}}]...

    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

    }
    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 matches the same characters as linear-white-space from RFC 822, but we make no effort to
   * enforce any particular rules with regards to line folding as stated in the class docs.
   */
  private static final CharMatcher LINEAR_WHITE_SPACE = CharMatcher.anyOf(" \t\r\n");

  // TODO(gak): make these public?

    checkNotNull(presentBehavior);
    checkNotNull(absentBehavior);
    if (!(list instanceof RandomAccess)) {
      list = Lists.newArrayList(list);
    }
    // TODO(lowasser): benchmark when it's best to do a linear search

    int lower = 0;
    int upper = list.size() - 1;

    while (lower <= upper) {
      int middle = (lower + upper) >>> 1;
      int c = comparator.compare(key, list.get(middle));

  private static final int CUTOFF = (int) (MAX_TABLE_SIZE * DESIRED_LOAD_FACTOR);

  /**
   * Returns an array size suitable for the backing array of a hash table that uses open addressing
   * with linear probing in its implementation. The returned size is the smallest power of two that
   * can hold setSize elements with the desired load factor. Always returns at least setSize + 2.
   */