代码搜索：Gradient

找到约 2,951 项符合「Gradient」的源代码

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cs linear.cs

using System; using System.Collections.Generic; using System.Text; namespace AnotherPDFLib.XFA { /// /// A fill type element that describes a linear gradient fill. ///
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www.eeworm.com/read/445571/1720347

cs radial.cs

using System; using System.Collections.Generic; using System.Text; namespace AnotherPDFLib.XFA { /// /// A fill type element that describes a radial gradient fill. ///
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www.eeworm.com/read/438778/1822491

c crf1m_learn_sgd.c

/* * Training linear-chain CRFs with Stochastic Gradient Descent (SGD). * * Copyright (c) 2007-2009, Naoaki Okazaki * All rights reserved. * * Redistribution and use in source and binary fo
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www.eeworm.com/read/403190/2314794

svn-base compute_grad.m.svn-base

function grad = compute_grad(M,options) % compute_grad - compute the gradient of an image using central differences % % grad = compute_grad(M,options); % % 'options' is a structure: % - op
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m graddesc.m

function [x, options, flog, pointlog] = graddesc(f, x, options, gradf, ... varargin) %GRADDESC Gradient descent optimization. % % Description % [X, OPTIONS, FLOG, POINTLOG] = GRADDESC(F, X, OPTIONS
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cs linear.cs

using System; using System.Collections.Generic; using System.Text; namespace AnotherPDFLib.XFA { /// /// A fill type element that describes a linear gradient fill. ///
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www.eeworm.com/read/363037/2923469

cs radial.cs

using System; using System.Collections.Generic; using System.Text; namespace AnotherPDFLib.XFA { /// /// A fill type element that describes a radial gradient fill. ///
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www.eeworm.com/read/359369/2978392

m demolgd1.m

%DEMOLGD1 Demonstrate simple MLP optimisation with on-line gradient descent % % Description % The problem consists of one input variable X and one target variable % T with data generated by sampli
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www.eeworm.com/read/359369/2978463

m graddesc.m

function [x, options, flog, pointlog] = graddesc(f, x, options, gradf, ... varargin) %GRADDESC Gradient descent optimization. % % Description % [X, OPTIONS, FLOG, POINTLOG] = GRADDESC(F, X, OP
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www.eeworm.com/read/395537/8168695

m uvw.m

function f = uvw(xy); % % uvw % Objective function to minimize in Conjugate Gradient Homework. % Vectorized to handle a 2 x n input matrix xy, % whose columns give the evaluation points in the
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