eitt
elles
honom
hjå
ho
hoe
henne
hennar
hennes
hoss
hossen
ikkje
ingi
inkje
korleis
korso
kva
kvar
kvarhelst
kven
kvi
kvifor
me
medan
mi
mine
mykje
no
nokon
noka
nokor
noko
nokre
si
sia
sidan
so
somt
somme
um
upp
vere
vore
verte
vort
varte

        },
        "norwegian_keywords": {

        },
        "norwegian_keywords": {

  snprintf(buf, kBufSize, "Mean with %2.0f%% trimmed:", trim_ratio * 100);
  std::string label_trimmed(buf);
  snprintf(buf, kBufSize, "Mean of %2.0f%% best:", best_ratio * 100);
  std::string label_best(buf);
  std::vector<std::pair<std::string, double>> groups = {
      {"Best:", sorted_us.front()},
      {"Worst:", sorted_us.back()},
      {"Median:", sorted_us[count_us / 2]},
      {"Mean:", sum_us / count_us},

)

type Data []float64

func (d Data) Min() float64 {
	return d.sorted().min()
}

func (d Data) Max() float64 {
	return d.sorted().max()
}

func (d Data) Median() float64 {
	return d.Quantile(0.5)
}

func (d Data) Mean() float64 {
	total := float64(0)
	for _, v := range d {
		total += v
	}
	return total / float64(len(d))
}

func (d Data) Quantile(phi float64) float64 {

        return myTime && myTime.median < otherTime.median && differenceSignificantCheck.test(myTime, otherTime)
    }

    boolean significantlySlowerThan(MeasuredOperationList other, double minConfidence = MINIMUM_CONFIDENCE) {
        def myTime = results.totalTime
        def otherTime = other.totalTime
        myTime && myTime.median > otherTime.median && differenceIsSignificant(myTime, otherTime, minConfidence)
    }

  // CHECK-DAG: %[[EPS:.+]] = mhlo.constant dense<1.000000e+00> : tensor<256xf32>
  // CHECK-DAG: %[[MEAN:.+]] = "tf.Mean"(%arg0, %[[CST_AXIS]]) <{keep_dims = false}> : (tensor<3x4x256x6xf32>, tensor<3xi32>) -> tensor<256xf32>
  // CHECK-DAG: %[[MEAN_BCAST:.+]] = "mhlo.dynamic_broadcast_in_dim"(%[[MEAN]], %[[X_SHAPE]]) <{broadcast_dimensions = dense<2> : tensor<1xi64>}> : (tensor<256xf32>, tensor<4xindex>) -> tensor<3x4x256x6xf32>

        mean += (value - mean) / (index + 1);
      } else {
        mean = calculateNewMeanNonFinite(mean, value);
      }
    }
    return mean;
  }

  /**
   * Returns the <a href="http://en.wikipedia.org/wiki/Arithmetic_mean">arithmetic mean</a> of the
   * values. The count must be non-zero.
   *
   * <p>The definition of the mean is the same as {@link Stats#mean}.
   *

      }
    } else {
      count++;
      if (isFinite(value) && isFinite(mean)) {
        // Art of Computer Programming vol. 2, Knuth, 4.2.2, (15) and (16)
        double delta = value - mean;
        mean += delta / count;
        sumOfSquaresOfDeltas += delta * (value - mean);
      } else {
        mean = calculateNewMeanNonFinite(mean, value);
        sumOfSquaresOfDeltas = NaN;
      }
      min = Math.min(min, value);

      } else if (values.hasAnyNegativeInfinity()) {
        assertWithMessage("mean of " + values).that(mean).isNegativeInfinity();
      } else {
        assertWithMessage("mean of " + values)
            .that(mean)
            .isWithin(ALLOWED_ERROR)
            .of(MANY_VALUES_MEAN);
      }
    }