/external/apache-commons-math/src/main/java/org/apache/commons/math/analysis/ |
H A D | 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
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/external/ImageMagick/Magick++/demo/ |
H A D | demo.cpp | 229 cout << " gradient ..." << endl; 230 Image gradient; local 231 gradient.size( "130x194" ); 232 gradient.read( "gradient:#20a0ff-#ffff00" ); 233 gradient.label( "Gradient" ); 234 images.push_back( gradient );
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/external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/fitting/ |
H A D | ParametricGaussianFunction.java | 94 * Computes the gradient vector for a four variable version of the function 97 * computing the gradient vector for the function <tt>f(x)</tt> (which would 99 * it's a one-dimensional function), computes the gradient vector for the 103 * The components of the computed gradient vector are the partial 114 * <tt>d</tt> for computation of gradient vector of <tt>f(a, b, c, 117 * @return gradient vector of <tt>f(a, b, c, d)</tt> 124 public double[] gradient(double x, double[] parameters) throws ZeroException { method in class:ParametricGaussianFunction 142 * the <code>value</code> and <code>gradient</code> methods.
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H A D | 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
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H A D | HarmonicFitter.java | 121 public double[] gradient(double x, double[] parameters) { method in class:HarmonicFitter.ParametricHarmonicFunction
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H A D | PolynomialFitter.java | 87 public double[] gradient(double x, double[] parameters) { method in class:PolynomialFitter.ParametricPolynomial 88 final double[] gradient = new double[parameters.length]; 91 gradient[i] = xn; 94 return gradient;
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/external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/general/ |
H A D | 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/ |
H A D | Color.hpp | 52 static Color<T> gradient(const Color<T> &c1, const Color<T> &c2, float d); 448 inline Color<T> Color<T>::gradient(const Color<T> &c1, const Color<T> &c2, float d) function in class:sw::Color
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H A D | SetupProcessor.hpp | 73 Gradient gradient[MAX_FRAGMENT_INPUTS][4]; member in union:sw::SetupProcessor::States::__anon22367
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/external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
H A D | while_op.cc | 66 for (const auto& gradient : resource->tensor_array_gradients()) { 67 arg.tensor_array_gradients.insert(gradient.first); 129 // gradient TensorArrays will be created by the TensorArrayGradV3 operator. 134 // 1) once with uninitialized resource inputs and no TensorArray gradient 168 VLOG(4) << "TensorArray " << resource->name() << " accessed gradient " 170 XlaResource* gradient; local 172 grad_source, builder, &gradient)); 177 for (const auto& gradient : resource->tensor_array_gradients()) { 178 arg.tensor_array_gradients.insert(gradient.first);
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H A D | fake_quantize_ops.cc | 162 xla::ComputationDataHandle gradient = ctx->Input(0); variable 178 b->Select(between_nudged_min_max, gradient, zeroes); 246 xla::ComputationDataHandle gradient = ctx->Input(0); variable 264 b->Select(between_nudged_min_max, gradient, zeroes); 269 b->ReduceAll(b->Select(below_min, gradient, zeroes), zero, 275 b->ReduceAll(b->Select(above_max, gradient, zeroes), zero,
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H A D | tensor_array_ops.cc | 582 // Finds or looks up the corresponding gradient TensorArray, which stores 584 XlaResource* gradient; variable 586 ctx, resource->GetOrCreateTensorArrayGradient(source_, b, &gradient)); 588 ctx->SetResourceOutput(0, gradient);
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/external/tensorflow/tensorflow/compiler/tf2xla/ |
H A D | xla_resource.cc | 43 for (const string& gradient : tensor_array_gradients) { 44 tensor_array_gradients_[gradient].reset( 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/tensorflow/tensorflow/core/kernels/ |
H A D | ctc_loss_op.cc | 140 Tensor* gradient; variable 142 ctx->allocate_output("gradient", inputs_shape, &gradient)); 143 auto gradient_t = gradient->tensor<float, 3>();
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H A D | fake_quant_ops.cc | 118 void Operate(OpKernelContext* context, const Tensor& gradient, argument 120 OperateNoTemplate(context, gradient, input, output); 123 void OperateNoTemplate(OpKernelContext* context, const Tensor& gradient, argument 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 367 const Tensor& gradient = context->input(0); variable [all...] |
H A D | pooling_ops_3d_sycl.h | 400 // maximum value in the input tensor. This is then the index of the gradient to 627 // input value at this index. Then for each gradient in this window, compute 629 // this size to scale the gradient accordingly. Add this scaled gradient to the 679 T gradient = T(0); local 704 gradient += input_backprop_n[idx] / static_cast<T>(window_size); 708 output_backprop[index] = gradient;
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/external/ImageMagick/MagickCore/ |
H A D | 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 419 *gradient; local [all...] |
H A D | draw.h | 190 gradient; member in struct:_ElementReference 226 gradient; member in struct:_DrawInfo
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H A D | draw.c | 329 clone_info->gradient=draw_info->gradient; 330 if (draw_info->gradient.stops != (StopInfo *) NULL) 335 number_stops=clone_info->gradient.number_stops; 336 clone_info->gradient.stops=(StopInfo *) AcquireQuantumMemory((size_t) 337 number_stops,sizeof(*clone_info->gradient.stops)); 338 if (clone_info->gradient.stops == (StopInfo *) NULL) 341 (void) CopyMagickMemory(clone_info->gradient.stops, 342 draw_info->gradient.stops,(size_t) number_stops* 343 sizeof(*clone_info->gradient 3303 GetStopColorOffset(const GradientInfo *gradient, const ssize_t x,const ssize_t y) argument 3381 *gradient; local [all...] |
/external/opencv/cv/src/ |
H A D | cvsnakes.cpp | 66 // if _CV_SNAKE_GRAD - magnitude of gradient is energy 100 float *gradient = NULL; local 149 gradient = (float *) cvAlloc( roi.height * roi.width * sizeof( float )); 151 if( !gradient ) 156 cvFree( &gradient ); 159 /* clear map - no gradient computed */ 315 gradient[(y*WTILE_SIZE + l) * roi.width + x*WTILE_SIZE + m] = 325 gradient[(pt[i].y + j) * roi.width + pt[i].x + k]; 401 cvFree( &gradient );
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/external/tensorflow/tensorflow/python/eager/ |
H A D | backprop.py | 94 """Pretends to be a tf.Operation for the gradient functions.""" 112 """Calls the gradient function of the op. 141 # gradient function registration site, to be less error-prone 247 # gradient functions. 248 # Some gradient functions can accept None arguments for gradients. The following 249 # maps the operation name to the indices at which the corresponding gradient 251 # e.g. FusedBatchNorm outputs 5 values and hence receives 5 gradient values 252 # during backprop. However the gradient function uses only the first of those 254 # indicates that only the gradient corresponding to index 0 is used, and the 255 # gradient value 889 def gradient(self, target, sources, output_gradients=None): member in class:GradientTape [all...] |
/external/tensorflow/tensorflow/core/framework/ |
H A D | function.h | 309 // Adds gradient definition 'grad' to this function library. 320 // Remove gradient of function `func` from the library. Returns non-OK Status 321 // unless `func` has a gradient. 334 // If the gradient function for 'func' is specified explicitly in 335 // the library, returns the gradient function name. Otherwise, 599 // To register a gradient function for a builtin op, one should use 603 // converted into ::tensorflow::gradient::Creator, which is 606 // A ::tensorflow::gradient::Creator should populate in FunctionDef* with a 607 // definition of a brain function which compute the gradient for the 638 // gradient functio 657 namespace gradient { namespace in namespace:tensorflow [all...] |
H A D | function.cc | 115 // Right now the C++ function gradient code assumes it can pass 116 // all the attrs of the function to the gradient, and any attrs that 117 // the gradient doesn't care about will be ignored. 931 for (const auto& grad : def_lib.gradient()) { 990 "Cannot assign gradient function '", grad.gradient_func(), "' to '", 991 grad.function_name(), "' because it already has gradient function ", 1054 for (const GradientDef& grad : lib_def.gradient()) { 1080 return errors::InvalidArgument("Tried to remove non-existent gradient ", 1122 // If ndef is SymbolicGradient[f=Foo], we use Foo's gradient or 1130 // If 'func' has a user-defined gradient functio 1318 namespace gradient { namespace in namespace:tensorflow [all...] |
/external/skia/tests/ |
H A D | ImageFilterTest.cpp | 588 SkBitmap gradient = make_gradient_circle(width, height); local 590 gradient));
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/external/skqp/tests/ |
H A D | ImageFilterTest.cpp | 588 SkBitmap gradient = make_gradient_circle(width, height); local 590 gradient));
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