| /external/webrtc/webrtc/modules/audio_processing/intelligibility/ |
| intelligibility_utils.h | 46 // Internal helper for computing the variances of a stream of arrays.
47 // The result is an array of variances per position: the i-th variance
51 // * kStepInfinite computes variances from the beginning onwards
54 // * kStepWindowed computes variances within a moving window
57 // one block and the history then consists of the variances of these blocks
77 // Add a new data point to the series and compute the new variances.
84 // Reset variances to zero and forget all history.
86 // Scale the input data by |scale|. Effectively multiply variances
90 // The current set of variances.
93 // The mean value of the current set of variances
[all...] |
| intelligibility_utils.cc | 157 // Windowed variance computation. On each step, the variances for the
188 // Variance with a window of blocks. Within each block, the variances are
191 // history window and a new block is started. The variances for the window
225 // Recomputes variances for each window from scratch based on previous window.
|
| /cts/apps/CameraITS/tests/scene1/ |
| test_raw_sensitivity.py | 52 variances = []
85 variances.append(var)
88 x = range(len(variances))
89 pylab.plot(x, variances, "-ro")
96 for i in range(len(variances) - 1):
97 assert variances[i] < variances[i+1] / VAR_THRESH
|
| test_raw_burst_sensitivity.py | 76 variances = []
96 variances.append(var)
99 x = range(len(variances))
100 pylab.plot(x, variances, "-ro")
109 assert variances[i] < variances[i+1] / VAR_THRESH
|
| test_reprocess_noise_reduction.py | 65 # List of variances for R, G, B.
113 # Get the variances for R, G, and B channels
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| test_dng_noise_model.py | 34 # Pass if the difference between expected and computed variances is small,
|
| /external/libvpx/libvpx/vpx_dsp/ |
| variance.c | 197 #define VARIANCES(W, H) \
202 VARIANCES(64, 64)
203 VARIANCES(64, 32)
204 VARIANCES(32, 64)
205 VARIANCES(32, 32)
206 VARIANCES(32, 16)
207 VARIANCES(16, 32)
208 VARIANCES(16, 16)
209 VARIANCES(16, 8)
210 VARIANCES(8, 16
[all...] |
| /cts/tests/tests/uirendering/src/android/uirendering/cts/bitmapcomparers/ |
| MSSIMComparer.java | 59 double[] variances = getVariances(ideal, given, meanX, meanY, start, stride); local
60 double varX = variances[0];
61 double varY = variances[1];
62 double stdBoth = variances[2];
|
| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/inference/ |
| TTestImpl.java | 220 * subpopulation variances. To compute a t-statistic without the
221 * equal variances hypothesis, use {@link #t(double[], double[])}.
262 * subpopulation variances. To compute a t-statistic assuming equal
263 * variances, use {@link #homoscedasticT(double[], double[])}.
300 * assumption of equal subpopulation variances. Use
302 * compute a t-statistic under the equal variances assumption.
341 * assumption of equal subpopulation variances. To compute a t-statistic
342 * without the equal variances assumption, use
545 * The test does not assume that the underlying popuation variances are
553 * variances, use {@link #homoscedasticTTest(double[], double[])}.</p
[all...] |
| TTest.java | 179 * subpopulation variances. To compute a t-statistic without the
180 * equal variances hypothesis, use {@link #t(double[], double[])}.
214 * subpopulation variances. To compute a t-statistic assuming equal
215 * variances, use {@link #homoscedasticT(double[], double[])}.
245 * assumption of equal subpopulation variances. Use
247 * compute a t-statistic under the equal variances assumption.
280 * assumption of equal subpopulation variances. To compute a t-statistic
281 * without the equal variances assumption, use
462 * The test does not assume that the underlying popuation variances are
470 * variances, use {@link #homoscedasticTTest(double[], double[])}.</p
[all...] |
| /external/webrtc/webrtc/modules/audio_processing/transient/ |
| moving_moments.h | 24 // buffer of second moments; and calculates the variances. When needed.
|
| /cts/tests/tests/transition/src/android/transition/cts/ |
| FadeTest.java | 238 double[] variances = getVariances(expected, real, meanX, meanY, x, y); local
239 double varX = variances[0];
240 double varY = variances[1];
241 double stdBoth = variances[2];
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setools/policyrep/ |
| exception.py | 202 for classes with strong similarities, but with slight variances in
|
| /external/tensorflow/tensorflow/python/layers/ |
| normalization_test.py | [all...] |
| /packages/apps/Dialer/java/com/android/incallui/answer/impl/classifier/ |
| AnglesClassifier.java | 40 * variances of the two parts split up. The classifier tries the tick option only if the first part
|
| /toolchain/binutils/binutils-2.27/gas/testsuite/gas/rx/ |
| explode | 136 # returns (before, after, list of variances)
|
| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/ |
| ar_model_test.py | 170 variances = predictions["covariance"][:, 0]
171 standard_deviations = np.sqrt(variances)
|
| model.py | 132 self._stats_means, variances = (
134 self._stats_sigmas = math_ops.sqrt(variances)
144 """Scale variances according to stats (input scale -> model scale)."""
158 """Scale back variances according to stats (model scale -> input scale)."""
[all...] |
| /external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/ |
| LeastSquaresConverter.java | 98 * odd elements (i.e. reciprocals of variances).
|
| /external/libmpeg2/common/ |
| ideint.c | 175 /* Initialize variances */
|
| /external/libxcam/cl_kernel/ |
| kernel_3d_denoise.cl | 5 * threshold: Noise variances of observed image
|
| /frameworks/base/packages/SystemUI/src/com/android/systemui/classifier/ |
| AnglesClassifier.java | 42 * final result is the minimum of angle variance of the whole stroke and the sum of angle variances
|
| /cts/apps/CameraITS/pymodules/its/ |
| image.py | 680 variances = []
683 variances.append(numpy.var(img[:,:,i], dtype=numpy.float64))
684 return variances
697 variances = compute_image_variances(img)
698 std_devs = [math.sqrt(v) for v in variances]
[all...] |
| /external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/general/ |
| AbstractLeastSquaresOptimizer.java | 254 * distinct normal distributions centered on 0 and whose variances are
|
| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/moment/ |
| Mean.java | 47 * Sample Means and Variances," Robert F. Ling, Journal of the American
|
