| /frameworks/support/graphics/drawable/animated/src/androidTest/java/androidx/vectordrawable/graphics/drawable/tests/ |
| PathInterpolatorValueParameterizedTest.java | 41 private static final float EPSILON = 1e-3f;
75 assertTrue("value " + value + " is different than expected " + mExpected, delta < EPSILON);
|
| /packages/inputmethods/LatinIME/native/jni/tests/suggest/core/layout/ |
| normal_distribution_2d_test.cpp | 51 static const float EPSILON = 0.01f;
62 EXPECT_NEAR(probabilityDensity0, probabilityDensity1, EPSILON);
|
| /packages/inputmethods/LatinIME/tests/src/com/android/inputmethod/keyboard/internal/ |
| MatrixUtilsTests.java | 28 private static final float EPSILON = 0.00001f;
31 assertEqualsFloat(f0, f1, EPSILON);
|
| /external/deqp/external/openglcts/modules/glesext/tessellation_shader/ |
| esextcTessellationShaderPoints.cpp | 556 const float epsilon = (float)1.0f / 255.0f; local
586 if (de::abs(expected_color_r - rendered_color_r) > epsilon ||
587 de::abs(expected_color_g - rendered_color_g) > epsilon ||
588 de::abs(expected_color_b - rendered_color_b) > epsilon ||
589 de::abs(expected_color_a - rendered_color_a) > epsilon)
595 << ", " << rendered_color_a << ") epsilon: " << epsilon << tcu::TestLog::EndMessage;
794 const float epsilon = 1e-5f; local
847 * is treated as though it were originally specified as 1+epsilon, which would
852 if (de::abs(clamped_inner_levels[0] - 1.0f) < epsilon)
1016 const float epsilon = 1e-5f; local
[all...] |
| esextcTessellationShaderVertexOrdering.cpp | 486 const float epsilon = 1e-5f; local
516 epsilon &&
518 epsilon &&
520 epsilon &&
522 epsilon &&
524 epsilon &&
526 epsilon &&
565 const float epsilon = 1e-5f; local
593 DE_UNREF(epsilon);
595 1.0f) < epsilon);
[all...] |
| /external/tensorflow/tensorflow/python/ops/ |
| nn_fused_batchnorm_test.py | 36 def _batch_norm(self, x, mean, var, offset, scale, epsilon):
40 inv = math_ops.rsqrt(var + epsilon) * scale
44 def _inference_ref(self, x, scale, offset, mean, var, epsilon, data_format):
50 y = self._batch_norm(x, mean, var, offset, scale, epsilon)
75 epsilon = 0.001
82 epsilon=epsilon,
86 y_ref = self._inference_ref(x, scale, offset, mean, var, epsilon,
94 def _training_ref(self, x, scale, offset, epsilon, data_format):
102 y = self._batch_norm(x, mean, var, offset, scale, epsilon)
[all...] |
| /external/tensorflow/tensorflow/compiler/xla/tests/ |
| batch_normalization_test.cc | 185 auto epsilon = builder.ConstantR0<float>(kEpsilon); local
197 builder.Gt(standard_deviation, epsilon), ShapeUtil::MakeShape(PRED, {2}));
232 /*epsilon=*/0.001, kFeatureIndex);
256 /*epsilon=*/0.001, kFeatureIndex);
287 /*epsilon=*/1, kFeatureIndex);
301 // Test the correctness of choosing a large epsilon value.
318 // var = 125, mean = 15, epsilon = -100
320 /*epsilon=*/-100, kFeatureIndex);
350 /*epsilon=*/0.0, kFeatureIndex);
441 float epsilon = 0.001 local
541 float epsilon = 0.001; local
649 float epsilon = 0.001; local
[all...] |
| /cts/tests/tests/animation/src/android/animation/cts/ |
| ValueAnimatorTest.java | 61 private static final float EPSILON = 0.0001f;
178 assertEquals(.5f, currentFraction, EPSILON);
179 assertEquals(50, currentValue, EPSILON);
186 assertEquals(.5f, currentFraction, EPSILON);
187 assertEquals(50, currentValue, EPSILON);
196 assertEquals(.5f, currentFraction, EPSILON);
197 assertEquals(50, currentValue, EPSILON);
203 assertEquals(.5f, currentFraction, EPSILON);
204 assertEquals(50, currentValue, EPSILON);
211 assertEquals(.5f, delayedAnim.getAnimatedFraction(), EPSILON);
[all...] |
| /external/eigen/unsupported/Eigen/src/LevenbergMarquardt/ |
| LMonestep.h | 178 if (abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() && Scalar(.5) * ratio <= 1.)
183 if (m_delta <= NumTraits<Scalar>::epsilon() * xnorm)
188 if (m_gnorm <= NumTraits<Scalar>::epsilon())
|
| /external/libcups/cups/ |
| pwg-private.h | 48 extern pwg_media_t *_pwgMediaNearSize(int width, int length, int epsilon);
|
| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| api_def_SparseApplyAdadelta.pbtxt | 28 name: "epsilon"
|
| /external/tensorflow/tensorflow/python/training/ |
| adadelta_test.py | 59 epsilon = 1e-8
60 adadelta_opt = adadelta.AdadeltaOptimizer(lr, rho, epsilon)
106 update[step] = (np.sqrt(accum_update + epsilon) *
107 (1. / np.sqrt(accum + epsilon)) * grad)
|
| /external/tensorflow/tensorflow/tools/api/golden/ |
| tensorflow.keras.callbacks.-reduce-l-r-on-plateau.pbtxt | 8 argspec: "args=[\'self\', \'monitor\', \'factor\', \'patience\', \'verbose\', \'mode\', \'epsilon\', \'cooldown\', \'min_lr\'], varargs=None, keywords=None, defaults=[\'val_loss\', \'0.1\', \'10\', \'0\', \'auto\', \'0.0001\', \'0\', \'0\'], "
|
| tensorflow.keras.optimizers.-adadelta.pbtxt | 8 argspec: "args=[\'self\', \'lr\', \'rho\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'1.0\', \'0.95\', \'None\', \'0.0\'], "
|
| tensorflow.keras.optimizers.-adagrad.pbtxt | 8 argspec: "args=[\'self\', \'lr\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'0.01\', \'None\', \'0.0\'], "
|
| tensorflow.keras.optimizers.-adam.pbtxt | 8 argspec: "args=[\'self\', \'lr\', \'beta_1\', \'beta_2\', \'epsilon\', \'decay\', \'amsgrad\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'0.999\', \'None\', \'0.0\', \'False\'], "
|
| tensorflow.keras.optimizers.-adamax.pbtxt | 8 argspec: "args=[\'self\', \'lr\', \'beta_1\', \'beta_2\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'0.002\', \'0.9\', \'0.999\', \'None\', \'0.0\'], "
|
| tensorflow.keras.optimizers.-nadam.pbtxt | 8 argspec: "args=[\'self\', \'lr\', \'beta_1\', \'beta_2\', \'epsilon\', \'schedule_decay\'], varargs=None, keywords=kwargs, defaults=[\'0.002\', \'0.9\', \'0.999\', \'None\', \'0.004\'], "
|
| tensorflow.keras.optimizers.-r-m-sprop.pbtxt | 8 argspec: "args=[\'self\', \'lr\', \'rho\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'None\', \'0.0\'], "
|
| /packages/apps/Camera2/src/com/android/camera/ui/motion/ |
| UnitBezier.java | 31 private static final float EPSILON = 1e-6f;
82 if (Math.abs(value) < EPSILON) {
86 if (Math.abs(derivative) < EPSILON) {
106 if (Math.abs(value - target) < EPSILON) {
|
| /prebuilts/gcc/linux-x86/host/x86_64-linux-glibc2.15-4.8/x86_64-linux/include/c++/4.8/bits/ |
| regex_nfa.tcc | 75 << " [label=\"epsilon\", tailport=\"s\"];\n"
77 << " [label=\"epsilon\", tailport=\"n\"];\n";
82 << __id << " -> " << _M_next << " [label=\"epsilon\"];\n";
87 << __id << " -> " << _M_next << " [label=\"epsilon\"];\n";
|
| /prebuilts/gcc/linux-x86/host/x86_64-w64-mingw32-4.8/x86_64-w64-mingw32/include/c++/4.8.3/bits/ |
| regex_nfa.tcc | 75 << " [label=\"epsilon\", tailport=\"s\"];\n"
77 << " [label=\"epsilon\", tailport=\"n\"];\n";
82 << __id << " -> " << _M_next << " [label=\"epsilon\"];\n";
87 << __id << " -> " << _M_next << " [label=\"epsilon\"];\n";
|
| /external/apache-commons-math/src/main/java/org/apache/commons/math/special/ |
| Gamma.java | 151 * @param epsilon When the absolute value of the nth item in the
152 * series is less than epsilon the approximation ceases
160 double epsilon,
173 ret = 1.0 - regularizedGammaQ(a, x, epsilon, maxIterations);
179 while (FastMath.abs(an/sum) > epsilon && n < maxIterations && sum < Double.POSITIVE_INFINITY) {
228 * @param epsilon When the absolute value of the nth item in the
229 * series is less than epsilon the approximation ceases
237 double epsilon,
250 ret = 1.0 - regularizedGammaP(a, x, epsilon, maxIterations);
266 ret = 1.0 / cf.evaluate(x, epsilon, maxIterations)
[all...] |
| /external/tensorflow/tensorflow/core/kernels/ |
| training_ops_gpu.cu.cc | 61 typename TTypes<T>::ConstScalar epsilon,
71 (accum_update + epsilon.reshape(single).broadcast(bcast)).sqrt() *
72 (accum + epsilon.reshape(single).broadcast(bcast)).rsqrt() * grad;
111 typename TTypes<T>::ConstScalar epsilon,
133 (epsilon.reshape(single).broadcast(bcast) + v.sqrt());
140 (epsilon.reshape(single).broadcast(bcast) + v.sqrt());
152 typename TTypes<T>::ConstScalar epsilon,
164 ((epsilon.reshape(single).broadcast(bcast) + ms).sqrt());
177 typename TTypes<T>::ConstScalar epsilon,
187 auto denom = (ms - mg.square()) + epsilon.reshape(single).broadcast(bcast)
[all...] |
| /external/eigen/Eigen/src/SparseCore/ |
| AmbiVector.h | 291 * \param epsilon the minimal value used to prune zero coefficients.
292 * In practice, all coefficients having a magnitude smaller than \a epsilon
295 explicit Iterator(const AmbiVector& vec, const RealScalar& epsilon = 0)
299 m_epsilon = epsilon;
367 RealScalar m_epsilon; // epsilon used to prune zero coefficients
|
