| /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.
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| /cts/apps/CameraITS/tests/scene1/ |
| test_raw_burst_sensitivity.py | 63 variances = []
72 variances.append(var)
78 pylab.plot(range(len(variances)), variances)
82 for i in range(len(variances) - 1):
83 assert(variances[i] < variances[i+1] / VAR_THRESH)
|
| test_raw_sensitivity.py | 51 variances = []
65 variances.append(var)
71 pylab.plot(range(len(variances)), variances)
75 for i in range(len(variances) - 1):
76 assert(variances[i] < variances[i+1] / VAR_THRESH)
|
| test_reprocess_noise_reduction.py | 63 # List of variances for R, G, B.
111 # Get the variances for R, G, and B channels
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| test_dng_noise_model.py | 31 # Pass if the difference between expected and computed variances is small,
|
| /external/libvpx/libvpx/vpx_dsp/ |
| variance.c | 232 #define VARIANCES(W, H) \
237 VARIANCES(64, 64)
238 VARIANCES(64, 32)
239 VARIANCES(32, 64)
240 VARIANCES(32, 32)
241 VARIANCES(32, 16)
242 VARIANCES(16, 32)
243 VARIANCES(16, 16)
244 VARIANCES(16, 8)
245 VARIANCES(8, 16
[all...] |
| /cts/tests/tests/uirendering/src/android/uirendering/cts/bitmapcomparers/ |
| MSSIMComparer.java | 65 double[] variances = getVariances(ideal, given, meanX, meanY, start, stride); local
66 double varX = variances[0];
67 double varY = variances[1];
68 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.
|
| /external/autotest/client/site_tests/hardware_PerfCounterVerification/ |
| stats_utils.py | 28 # which can be calculated from the variances and covariances.
|
| /external/chromium-trace/catapult/telemetry/telemetry/util/ |
| statistics.py | 231 variances = [float(x) - mean for x in data]
232 variances = [x * x for x in variances]
233 std_dev = math.sqrt(ArithmeticMean(variances))
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setools/policyrep/ |
| exception.py | 197 for classes with strong similarities, but with slight variances in
|
| /external/opencv3/doc/py_tutorials/py_imgproc/py_thresholding/ |
| py_thresholding.markdown | 188 It actually finds a value of t which lies in between two peaks such that variances to both classes
209 # finding means and variances
|
| /frameworks/base/packages/SystemUI/src/com/android/systemui/classifier/ |
| AnglesClassifier.java | 39 * final result is the minimum of angle variance of the whole stroke and the sum of angle variances
|
| /toolchain/binutils/binutils-2.25/gas/testsuite/gas/rx/ |
| explode | 136 # returns (before, after, list of variances)
|
| /cts/apps/CameraITS/pymodules/its/ |
| image.py | 641 variances = []
644 variances.append(numpy.var(img[:,:,i], dtype=numpy.float64))
645 return variances
657 variances = compute_image_variances(img)
658 std_devs = [math.sqrt(v) for v in variances]
|
| /external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/ |
| LeastSquaresConverter.java | 98 * odd elements (i.e. reciprocals of variances).
|
| /external/chromium-trace/catapult/dashboard/dashboard/ |
| find_change_points.py | 87 a split that minimizes the sum of the variances on either side. Then the
|
| ttest.py | 41 two samples may have unequal variances. It is also an independent two-sample
|
| /external/libmpeg2/common/ |
| ideint.c | 175 /* Initialize variances */
|
| /cts/apps/CameraITS/tests/dng_noise_model/ |
| dng_noise_model.py | 213 # To avoid overfitting to high ISOs (high variances), divide the system
|
| /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
|
