| /external/libvncserver/webclients/novnc/include/ |
| black.css | 15 background: -moz-linear-gradient(top, #4c4c4c 0%, #2c2c2c 50%, #000000 51%, #131313 100%); /* FF3.6+ */
16 background: -webkit-gradient(linear, left top, left bottom, color-stop(0%,#4c4c4c), color-stop(50%,#2c2c2c), color-stop(51%,#000000), color-stop(100%,#131313)); /* Chrome,Safari4+ */
17 background: -webkit-linear-gradient(top, #4c4c4c 0%,#2c2c2c 50%,#000000 51%,#131313 100%); /* Chrome10+,Safari5.1+ */
18 background: -o-linear-gradient(top, #4c4c4c 0%,#2c2c2c 50%,#000000 51%,#131313 100%); /* Opera11.10+ */
19 background: -ms-linear-gradient(top, #4c4c4c 0%,#2c2c2c 50%,#000000 51%,#131313 100%); /* IE10+ */
20 background: linear-gradient(top, #4c4c4c 0%,#2c2c2c 50%,#000000 51%,#131313 100%); /* W3C */
24 background: -moz-linear-gradient(top, #f04040 0%, #2c2c2c 50%, #000000 51%, #131313 100%); /* FF3.6+ */
25 background: -webkit-gradient(linear, left top, left bottom, color-stop(0%,#f04040), color-stop(50%,#2c2c2c), color-stop(51%,#000000), color-stop(100%,#131313)); /* Chrome,Safari4+ */
26 background: -webkit-linear-gradient(top, #f04040 0%,#2c2c2c 50%,#000000 51%,#131313 100%); /* Chrome10+,Safari5.1+ */
27 background: -o-linear-gradient(top, #f04040 0%,#2c2c2c 50%,#000000 51%,#131313 100%); /* Opera11.10+ *
[all...] |
| blue.css | 11 background-image: -webkit-gradient(
18 background-image: -moz-linear-gradient(
26 background-image: -webkit-gradient(
33 background-image: -moz-linear-gradient(
41 background-image: -webkit-gradient(
48 background-image: -moz-linear-gradient(
|
| base.css | 177 background:#eee; /* default background for browsers without gradient support */
243 background: -moz-linear-gradient(top, #b2bdcd 0%, #899cb3 49%, #7e93af 51%, #6e84a3 100%); /* FF3.6+ */
244 background: -webkit-gradient(linear, left top, left bottom, color-stop(0%,#b2bdcd), color-stop(49%,#899cb3), color-stop(51%,#7e93af), color-stop(100%,#6e84a3)); /* Chrome,Safari4+ */
245 background: -webkit-linear-gradient(top, #b2bdcd 0%,#899cb3 49%,#7e93af 51%,#6e84a3 100%); /* Chrome10+,Safari5.1+ */
246 background: -o-linear-gradient(top, #b2bdcd 0%,#899cb3 49%,#7e93af 51%,#6e84a3 100%); /* Opera11.10+ */
247 background: -ms-linear-gradient(top, #b2bdcd 0%,#899cb3 49%,#7e93af 51%,#6e84a3 100%); /* IE10+ */
248 background: linear-gradient(top, #b2bdcd 0%,#899cb3 49%,#7e93af 51%,#6e84a3 100%); /* W3C */
252 background: -moz-linear-gradient(top, #f04040 0%, #899cb3 49%, #7e93af 51%, #6e84a3 100%); /* FF3.6+ */
253 background: -webkit-gradient(linear, left top, left bottom, color-stop(0%,#f04040), color-stop(49%,#899cb3), color-stop(51%,#7e93af), color-stop(100%,#6e84a3)); /* Chrome,Safari4+ */
254 background: -webkit-linear-gradient(top, #f04040 0%,#899cb3 49%,#7e93af 51%,#6e84a3 100%); /* Chrome10+,Safari5.1+ *
[all...] |
| /external/apache-commons-math/src/main/java/org/apache/commons/math/analysis/ |
| DifferentiableMultivariateRealFunction.java | 33 * needed, it may be more efficient to use the {@link #gradient()} method which will
43 * Returns the gradient function.
47 * @return the gradient function
49 MultivariateVectorialFunction gradient(); method in interface:DifferentiableMultivariateRealFunction
|
| /frameworks/base/tests/HwAccelerationTest/src/com/android/test/hwui/ |
| GradientStopsActivity.java | 47 LinearGradient gradient = new LinearGradient(0.0f, 0.0f, 256.0f, 0.0f, local
51 paint.setShader(gradient);
57 gradient = new LinearGradient(0.0f, 0.0f, 256.0f, 0.0f,
60 paint.setShader(gradient);
67 gradient = new LinearGradient(0.0f, 0.0f, 256.0f, 0.0f,
70 paint.setShader(gradient);
76 gradient = new LinearGradient(0.0f, 0.0f, 256.0f, 0.0f,
79 paint.setShader(gradient);
84 gradient = new LinearGradient(0.0f, 0.0f, 256.0f, 0.0f,
87 paint.setShader(gradient);
[all...] |
| /external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/fitting/ |
| ParametricRealFunction.java | 41 * Compute the gradient of the function with respect to its parameters.
47 double[] gradient(double x, double[] parameters) method in interface:ParametricRealFunction
|
| PolynomialFitter.java | 87 public double[] gradient(double x, double[] parameters) { method in class:PolynomialFitter.ParametricPolynomial
88 final double[] gradient = new double[parameters.length]; local
91 gradient[i] = xn;
94 return gradient;
|
| /external/ImageMagick/MagickCore/ |
| paint.c | 400 % o type: the gradient type: linear or radial.
402 % o spread: the gradient spread meathod: pad, reflect, or repeat.
422 *gradient;
428 Set gradient start-stop end points.
437 gradient=(&draw_info->gradient);
438 gradient->type=type;
439 gradient->bounding_box.width=image->columns;
440 gradient->bounding_box.height=image->rows;
441 artifact=GetImageArtifact(image,"gradient:bounding-box")
419 *gradient; local
[all...] |
| /frameworks/layoutlib/bridge/tests/res/testApp/MyApplication/build/intermediates/classes/debug/com/android/layoutlib/test/myapplication/ |
| R$color.class | |
| /device/google/contexthub/firmware/os/algos/common/math/ |
| levenberg_marquardt.c | 21 float *residual, float *gradient,
24 static bool computeStep(const float *gradient, float *hessian, float *L,
79 // Compute initial cost function gradient and return if already sufficiently
84 data->gradient,
99 if (!computeStep(data->gradient, data->hessian, data->temp, damping_factor,
119 data->step, data->gradient,
129 // Check if cost function gradient is now sufficiently small,
134 data->gradient, data->hessian)) {
156 const float *step, const float *gradient,
163 // predicted gain = 0.5 * step' * (damping_factor * step + gradient)
[all...] |
| levenberg_marquardt.h | 61 // magnitude of the cost function gradient required for solution, i.e.
62 // max(gradient) = max(J'f(x)) < gradient_threshold.
77 float gradient[MAX_LM_MEAS_DIMENSION]; member in struct:LmData
87 GRADIENT_SUFFICIENTLY_SMALL, // cost function gradient is below threshold.
149 * gradient: gradient of the cost function F(x).
155 const float *step, const float *gradient,
|
| /external/tensorflow/tensorflow/compiler/tf2xla/ |
| xla_resource.cc | 43 for (const string& gradient : tensor_array_gradients) {
44 tensor_array_gradients_[gradient].reset(
127 VLOG(2) << "Gradient lookup for resource: " << name_
128 << " gradient: " << source;
130 std::unique_ptr<XlaResource>& gradient = tensor_array_gradients_[source]; local
131 if (!gradient) {
137 gradient.reset(
143 *gradient_out = gradient.get();
155 for (const auto& gradient : tensor_array_gradients_) {
156 elems.push_back(gradient.second->value_)
181 XlaResource* gradient; local
[all...] |
| /external/libxcam/cl_kernel/ |
| kernel_3d_denoise_slm.cl | 69 float4 gradient = (float4)(0.0f, 0.0f, 0.0f, 0.0f);
96 gradient = (float4)(ref_cache[mad24(i, 4 * REF_BLOCK_WIDTH, REF_BLOCK_WIDTH + local_id_x + j)].s2,
100 gradient = (gradient - ref_cache[mad24(i, 4 * REF_BLOCK_WIDTH, local_id_x + j)]) +
101 (gradient - ref_cache[mad24(i, 4 * REF_BLOCK_WIDTH, REF_BLOCK_WIDTH + local_id_x + j)]) +
102 (gradient - ref_cache[mad24(i, 4 * REF_BLOCK_WIDTH, 2 * REF_BLOCK_WIDTH + local_id_x + j)]) +
103 (gradient - ref_cache[mad24(i, 4 * REF_BLOCK_WIDTH, 3 * REF_BLOCK_WIDTH + local_id_x + j)]);
104 gradient.s0 = (gradient.s0 + gradient.s1 + gradient.s2 + gradient.s3) / 15.0f
[all...] |
| /cts/tests/tests/graphics/src/android/graphics/cts/ |
| RadialGradientTest.java | 41 RadialGradient gradient = new RadialGradient(0.5f, 0.5f, 1, local
46 gradient.setLocalMatrix(m);
53 paint.setShader(gradient);
|
| LinearGradientTest.java | 51 // The pixels in same gradient line should be equivalent
67 // The pixels in same gradient line should be equivalent
88 LinearGradient gradient = new LinearGradient(0.5f, 0, 1.5f, 0,
92 gradient.setLocalMatrix(m);
93 Bitmap bitmap = drawLinearGradient(gradient);
|
| /development/samples/ApiDemos/src/com/example/android/apis/animation/ |
| ShapeHolder.java | 33 private RadialGradient gradient; field in class:ShapeHolder
70 gradient = value;
73 return gradient;
|
| /external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/general/ |
| AbstractScalarDifferentiableOptimizer.java | 72 /** Number of gradient evaluations. */
78 /** Objective function gradient. */
79 private MultivariateVectorialFunction gradient; field in class:AbstractScalarDifferentiableOptimizer
148 * Compute the gradient vector.
149 * @param evaluationPoint point at which the gradient must be evaluated
150 * @return gradient at the specified point
151 * @exception FunctionEvaluationException if the function gradient
156 return gradient.value(evaluationPoint);
189 gradient = f.gradient();
[all...] |
| /external/swiftshader/src/Renderer/ |
| SetupProcessor.cpp | 111 state.gradient[interpolant][component].attribute = Unused;
112 state.gradient[interpolant][component].flat = false;
113 state.gradient[interpolant][component].wrap = false;
154 state.gradient[interpolant][component].attribute = input;
155 state.gradient[interpolant][component].flat = flat;
173 state.gradient[interpolant][component].attribute = T0 + semantic.index;
174 state.gradient[interpolant][component].flat = semantic.flat || (point && !sprite);
177 state.gradient[interpolant][component].attribute = C0 + semantic.index;
178 state.gradient[interpolant][component].flat = semantic.flat || flatShading;
|
| /external/tensorflow/tensorflow/contrib/training/python/training/ |
| training.py | 54 # Create the train_op and clip the gradient norms:
76 * Performing additional (non-gradient) updates during training *
80 of non-gradient updates during training. tf.contrib.training.create_train_op
81 allows a user to pass in a list of update_ops to call along with the gradient
275 grads_and_vars: A list of gradient to variable pairs (tuples).
293 logging.info('Var %s has no gradient', var.op.name)
302 gradients_to_variables: A list of gradient to variable pairs (tuples).
306 A list of clipped gradient to variable pairs.
321 """Returns a `transform_grads_fn` function for gradient clipping."""
331 grads_and_vars: A list of gradient to variable pairs (tuples)
[all...] |
| /external/skia/experimental/docs/ |
| canvasBackend.js | 102 if ('gradient' in paint) {
103 var gradient = paint.gradient.split('.');
104 var gradName = gradient[1];
106 var g = window[gradient[0]][gradient[1]];
|
| /external/skqp/experimental/docs/ |
| canvasBackend.js | 102 if ('gradient' in paint) {
103 var gradient = paint.gradient.split('.');
104 var gradName = gradient[1];
106 var g = window[gradient[0]][gradient[1]];
|
| /external/tensorflow/tensorflow/core/kernels/ |
| fake_quant_ops.cc | 118 void Operate(OpKernelContext* context, const Tensor& gradient,
120 OperateNoTemplate(context, gradient, input, output);
123 void OperateNoTemplate(OpKernelContext* context, const Tensor& gradient,
125 OP_REQUIRES(context, input.IsSameSize(gradient),
126 InvalidArgument("gradient and input must be the same size"));
128 functor(context->eigen_device<Device>(), gradient.flat<float>(),
230 const Tensor& gradient = context->input(0); variable
232 OP_REQUIRES(context, input.IsSameSize(gradient),
233 InvalidArgument("gradient and input must be the same size"));
251 functor(context->eigen_device<Device>(), gradient.flat<float>()
367 const Tensor& gradient = context->input(0); variable
[all...] |
| avgpooling_op_gpu.cu.cc | 61 dtype gradient(0);
74 gradient += top_diff_slice[(ph * pooled_width + pw) * channels] /
78 bottom_diff[index] = gradient;
|
| /external/tensorflow/tensorflow/contrib/layers/python/layers/ |
| optimizers.py | 115 clipping is applied to prevent the norm of the gradient to exceed this
243 # Optionally add gradient noise.
253 "Empty list of (gradient, var) pairs encountered. This is most "
273 # Add histograms for variables, gradients and gradient norms.
274 for gradient, variable in gradients:
275 if isinstance(gradient, ops.IndexedSlices):
276 grad_values = gradient.values
278 grad_values = gradient
293 # Create gradient updates.
351 Implement adaptive gradient as presented in section 3.2.1 o
[all...] |
| /external/tensorflow/tensorflow/python/eager/ |
| pywrap_tfe_test.py | 69 dz_dy = tape.gradient(z, [a_2_by_2])[0]
102 dz1_dy = tape.gradient(z1, [a_2_by_2])[0]
103 dz2_dy = tape.gradient(z2, [a_2_by_2])[0]
135 dz1_dy = tape.gradient(z1[0], [a_2_by_2])[0]
136 dz2_dy = tape.gradient(z2[0], [a_2_by_2])[0]
|
