| /ndk/sources/cxx-stl/llvm-libc++/libcxx/test/numerics/rand/rand.dis/rand.dist.pois/rand.dist.pois.poisson/ |
| ctor_double.pass.cpp | 25 assert(d.mean() == 1);
30 assert(d.mean() == 3.5);
|
| param_ctor.pass.cpp | 27 assert(p.mean() == 1);
33 assert(p.mean() == 10);
|
| ctor_param.pass.cpp | 27 assert(d.mean() == 0.25);
|
| param_assign.pass.cpp | 29 assert(p.mean() == .7);
|
| param_copy.pass.cpp | 28 assert(p.mean() == .125);
|
| eval.pass.cpp | 45 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); local
51 double d = (u[i] - mean);
62 double x_mean = d.mean();
63 double x_var = d.mean();
66 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
84 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); local
90 double d = (u[i] - mean);
101 double x_mean = d.mean();
102 double x_var = d.mean();
105 assert(std::abs((mean - x_mean) / x_mean) < 0.01)
123 double mean = std::accumulate(u.begin(), u.end(), 0.0) \/ u.size(); local
[all...] |
| eval_param.pass.cpp | 47 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); local
53 double d = (u[i] - mean);
64 double x_mean = p.mean();
65 double x_var = p.mean();
68 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
88 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); local
94 double d = (u[i] - mean);
105 double x_mean = p.mean();
106 double x_var = p.mean();
109 assert(std::abs((mean - x_mean) / x_mean) < 0.01)
129 double mean = std::accumulate(u.begin(), u.end(), 0.0) \/ u.size(); local
[all...] |
| /ndk/sources/cxx-stl/llvm-libc++/libcxx/test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.normal/ |
| ctor_double_double.pass.cpp | 15 // explicit normal_distribution(result_type mean = 0, result_type stddev = 1);
25 assert(d.mean() == 0);
31 assert(d.mean() == 14.5);
37 assert(d.mean() == 14.5);
|
| param_ctor.pass.cpp | 27 assert(p.mean() == 0);
34 assert(p.mean() == 10);
41 assert(p.mean() == 10);
|
| ctor_param.pass.cpp | 27 assert(d.mean() == 0.25);
|
| eval.pass.cpp | 42 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); local
48 double d = (u[i] - mean);
59 double x_mean = d.mean();
63 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
|
| eval_param.pass.cpp | 43 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); local
49 double d = (u[i] - mean);
60 double x_mean = p.mean();
64 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
|
| param_assign.pass.cpp | 29 assert(p.mean() == .75);
|
| param_copy.pass.cpp | 28 assert(p.mean() == 10);
|
| /external/webrtc/src/common_audio/vad/ |
| vad_gmm.h | 19 // normal distribution with mean and standard deviation (|mean|, |std|).
23 // - mean : mean input in the statistical model, Q7.
29 // |delta| = (|input| - |mean|) / |std|^2.
33 // 1 / |std| * exp(-(|input| - |mean|)^2 / (2 * |std|^2));
35 int16_t mean,
|
| vad_gmm.c | 25 // m = |mean| (Q7)
31 int16_t mean,
52 tmp16 = tmp16 - mean; // Q7 - Q7 = Q7
|
| /external/chromium_org/net/quic/congestion_control/ |
| inter_arrival_overuse_detector_test.cc | 24 QuicTime::Delta GaussianRandom(QuicTime::Delta mean,
28 QuicTime::Delta mean,
32 QuicTime::Delta mean,
38 QuicTime::Delta mean,
43 QuicTime::Delta mean,
48 QuicTime::Delta mean,
54 QuicTime::Delta mean,
73 QuicTime::Delta mean,
83 Add(mean).Subtract(drift_from_mean_);
86 drift_from_mean_ = drift_from_mean_.Subtract(mean);
267 QuicTime::Delta mean = QuicTime::Delta::FromMilliseconds(30); local
407 QuicTime::Delta mean = QuicTime::Delta::FromMilliseconds(1000); local
446 QuicTime::Delta mean = QuicTime::Delta::FromMilliseconds(1000); local
485 QuicTime::Delta mean = QuicTime::Delta::FromMilliseconds(100); local
532 QuicTime::Delta mean = QuicTime::Delta::FromMilliseconds(100); local
579 QuicTime::Delta mean = QuicTime::Delta::FromMilliseconds(10); local
626 QuicTime::Delta mean = QuicTime::Delta::FromMilliseconds(10); local
673 QuicTime::Delta mean = QuicTime::Delta::FromMilliseconds(1); local
720 QuicTime::Delta mean = QuicTime::Delta::FromMilliseconds(1); local
767 QuicTime::Delta mean = QuicTime::Delta::FromMilliseconds(1); local
822 QuicTime::Delta mean = QuicTime::Delta::FromMilliseconds(10); local
865 QuicTime::Delta mean = QuicTime::Delta::FromMilliseconds(1); local
912 QuicTime::Delta mean = QuicTime::Delta::FromMilliseconds(10); local
959 QuicTime::Delta mean = QuicTime::Delta::FromMilliseconds(10); local
1011 QuicTime::Delta mean = QuicTime::Delta::FromMilliseconds(10); local
1040 QuicTime::Delta mean = QuicTime::Delta::FromMilliseconds(10); local
1078 QuicTime::Delta mean = QuicTime::Delta::FromMilliseconds(10); local
[all...] |
| /external/valgrind/main/VEX/test/ |
| rounderr.c | 43 /* Compute the arithmetic mean of a dataset using the recurrence relation
44 mean_(n) = mean(n-1) + (data[n] - mean(n-1))/(n+1) */
45 long double mean = 0; local
50 mean += (data[i * stride] - mean) / (i + 1);
52 return mean;
58 /* Compute the arithmetic mean of a dataset using the
|
| /external/chromium_org/third_party/smhasher/src/ |
| SpeedTest.cpp | 15 // of the mean.
19 double mean = 0; local
23 mean += v[i];
26 mean /= double(v.size());
28 return mean;
33 double mean = 0; local
37 mean += v[i];
40 mean /= (b-a+1);
42 return mean;
47 double mean = CalcMean(v,a,b) local
68 double mean = 0; local
128 double mean = CalcMean(v,a,b); local
[all...] |
| /external/opencv/cv/src/ |
| cvadapthresh.cpp | 48 CvMat* mean = 0; local
70 mean = dst;
72 CV_CALL( mean = cvCreateMat( rows, cols, CV_8UC1 ));
74 CV_CALL( cvSmooth( src, mean, method == CV_ADAPTIVE_THRESH_MEAN_C ?
89 const uchar* m = mean->data.ptr + i*mean->step;
98 if( mean != dst )
99 cvReleaseMat( &mean );
|
| /external/chromium_org/third_party/WebKit/Tools/Scripts/ |
| sampstat | 32 usage = "usage: %prog [options] [FILES]\n Compute the mean and 95% confidence interval of a sample set.\n Standard input or files must contain two or more decimal numbers, one per line."
47 def standardDeviation(mean, items):
48 deltaSquares = [(item - mean) ** 2 for item in items]
65 mean = arithmeticMean(items)
66 stdDev = standardDeviation(mean, items)
83 mean = arithmeticMean(results) variable
85 confidencePercent = 100 * confidenceInterval / mean
96 prefix = "Mean: " if options.verbose else ""
97 print "%s%.2f %s +/- %.2f %s (%.1f%%)" % (prefix, mean, options.unit, confidenceInterval, options.unit, confidencePercent)
|
| /external/chromium_org/third_party/libjingle/source/talk/base/ |
| profiler_unittest.cc | 56 EXPECT_NEAR(kWaitSec, event->mean(), kTolerance);
61 EXPECT_NEAR(kWaitSec, event->mean(), kTolerance);
63 EXPECT_DOUBLE_EQ(event->mean(), event->total_time() / event->event_count());
86 EXPECT_NEAR(kWaitSec, event1->mean(), kTolerance);
98 EXPECT_NEAR(kEvent2WaitSec, event2->mean(), kTolerance);
110 EXPECT_NEAR(kWaitSec, event1->mean(), kTolerance);
112 EXPECT_DOUBLE_EQ(event1->mean(),
|
| /external/eigen/Eigen/src/Eigen2Support/ |
| LeastSquares.h | 121 * 1 - center the data to the mean
142 // compute the mean of the data
143 VectorType mean = VectorType::Zero(size); local
145 mean += *(points[i]);
146 mean /= numPoints;
153 VectorType diff = (*(points[i]) - mean).conjugate();
164 // plane pass trough the mean point:
165 result->offset() = - (result->normal().cwise()* mean).sum();
|
| /external/opencv/cxcore/src/ |
| cxmean.cpp | 46 * Mean value over the region *
165 mean[0] = scale*(double)tmp##0
171 mean[0] = t0; \
172 mean[1] = t1
179 mean[0] = t0; \
180 mean[1] = t1; \
181 mean[2] = t2
187 mean[0] = t0; \
188 mean[1] = t1; \
191 mean[2] = t0;
384 CvScalar mean = {{0,0,0,0}}; local
[all...] |
| /external/chromium_org/tools/telemetry/telemetry/page/ |
| page_measurement_value.py | 27 (mean, _) = perf_tests_helper.GeomMeanAndStdDevFromHistogram(self.value)
28 return mean
|
