| /external/icu/icu4c/source/test/perf/perldriver/ |
| D | Dataset.pm | 23 _mean => 0.0,
32 $self->{_mean} = $stats->mean();
63 return $self->{_mean} * $self->{_scale};
79 my $minratio = ($self->{_mean} - $self->{_error}) /
80 ($rhs->{_mean} + $rhs->{_error});
81 my $maxratio = ($self->{_mean} + $self->{_error}) /
82 ($rhs->{_mean} - $rhs->{_error});
85 $result->{_mean} = ($minratio + $maxratio) / 2;
86 $result->{_error} = $result->{_mean} - $minratio;
98 $result->{_mean} = $self->{_mean} - $rhs->{_mean};
[all …]
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| /external/icu/icu4j/perf-tests/perldriver/ |
| D | Dataset.pm | 21 _mean => 0.0,
30 $self->{_mean} = $stats->mean();
61 return $self->{_mean} * $self->{_scale};
77 my $minratio = ($self->{_mean} - $self->{_error}) /
78 ($rhs->{_mean} + $rhs->{_error});
79 my $maxratio = ($self->{_mean} + $self->{_error}) /
80 ($rhs->{_mean} - $rhs->{_error});
83 $result->{_mean} = ($minratio + $maxratio) / 2;
84 $result->{_error} = $result->{_mean} - $minratio;
96 $result->{_mean} = $self->{_mean} - $rhs->{_mean};
[all …]
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| /external/icu/icu4j/perf-tests/ |
| D | Dataset.pm | 20 _mean => 0.0,
29 $self->{_mean} = $stats->mean();
60 return $self->{_mean} * $self->{_scale};
76 my $minratio = ($self->{_mean} - $self->{_error}) /
77 ($rhs->{_mean} + $rhs->{_error});
78 my $maxratio = ($self->{_mean} + $self->{_error}) /
79 ($rhs->{_mean} - $rhs->{_error});
82 $result->{_mean} = ($minratio + $maxratio) / 2;
83 $result->{_error} = $result->{_mean} - $minratio;
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| /external/perfetto/ui/src/frontend/ |
| D | perf.ts | 48 private _mean = 0; property in RunningStatistics
61 this._mean = (this._mean * this._count + value) / (this._count + 1);
66 return this._mean;
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| /external/tensorflow/tensorflow/python/ops/distributions/ |
| D | laplace.py | 204 def _mean(self): member in Laplace
211 return self._mean()
214 return self._mean()
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| D | dirichlet_multinomial.py | 293 def _mean(self): member in DirichletMultinomial
316 x = self._variance_scale_term() * self._mean()
324 x = scale * self._mean()
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| D | dirichlet.py | 267 def _mean(self): member in Dirichlet
271 x = self._variance_scale_term() * self._mean()
279 x = scale * self._mean()
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| D | beta.py | 299 def _mean(self): member in Beta
303 return self._mean() * (1. - self._mean()) / (1. + self.total_concentration)
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| D | bernoulli.py | 158 def _mean(self): member in Bernoulli
162 return self._mean() * (1. - self.probs)
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| D | normal.py | 226 def _mean(self): member in Normal
236 return self._mean()
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| D | uniform.py | 201 def _mean(self): member in Uniform
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| D | multinomial.py | 291 def _mean(self): member in Multinomial
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| D | gamma.py | 253 def _mean(self): member in Gamma
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| D | distribution.py | 987 def _mean(self): member in Distribution
994 return self._mean()
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| D | student_t.py | 305 def _mean(self): member in StudentT
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| /external/ImageMagick/Magick++/lib/ |
| D | Statistic.cpp | 286 _mean(0.0), in ChannelStatistics()
306 _mean(channelStatistics_._mean), in ChannelStatistics()
359 return(_mean); in mean()
410 _mean(channelStatistics_->mean), in ChannelStatistics()
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| /external/ImageMagick/Magick++/lib/Magick++/ |
| D | Statistic.h | 207 double _mean; variable
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| /external/tensorflow/tensorflow/lite/micro/examples/hello_world/train/ |
| D | train_hello_world_model.ipynb | 1521 … There are several ways to calculate loss, and the method we have used is _mean squared error_. Th…
1581 …model's performance we can plot some more data. This time, we'll plot the _mean absolute error_, w…
1633 …"This graph of _mean absolute error_ tells another story. We can see that training data shows cons…
|