| /external/eigen/unsupported/test/ |
| D | autodiff.cpp | 214 // TODO also check actual derivatives!
227 // TODO also check actual derivatives!
235 ap.x().derivatives() = Vector2f::UnitX(); in test_autodiff_vector()
236 ap.y().derivatives() = Vector2f::UnitY(); in test_autodiff_vector()
268 //set unit vectors for the derivative directions (partial derivatives of the input vector) in test_autodiff_hessian()
269 x(0).derivatives().resize(2); in test_autodiff_hessian()
270 x(0).derivatives().setZero(); in test_autodiff_hessian()
271 x(0).derivatives()(0)= 1; in test_autodiff_hessian()
272 x(1).derivatives().resize(2); in test_autodiff_hessian()
273 x(1).derivatives().setZero(); in test_autodiff_hessian()
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| D | autodiff_scalar.cpp | 32 VERIFY_IS_APPROX(res.derivatives(), x.derivatives()); in check_atan2()
36 VERIFY_IS_APPROX(res.derivatives(), x.derivatives()); in check_atan2()
52 VERIFY_IS_APPROX(res1.derivatives().x(), Scalar(1.0) / (cosh_px * cosh_px)); in check_hyperbolic_functions()
56 VERIFY_IS_APPROX(res2.derivatives().x(), cosh_px); in check_hyperbolic_functions()
60 VERIFY_IS_APPROX(res3.derivatives().x(), std::sinh(p.x())); in check_hyperbolic_functions()
66 VERIFY_IS_APPROX(res1.derivatives().x(), Scalar(0.896629559604914)); in check_hyperbolic_functions()
69 VERIFY_IS_APPROX(res2.derivatives().x(), Scalar(1.056071867829939)); in check_hyperbolic_functions()
72 VERIFY_IS_APPROX(res3.derivatives().x(), Scalar(0.339540557256150)); in check_hyperbolic_functions()
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| D | splines.cpp | 251 ArrayXXd derivatives = ArrayXXd::Random(dimension, numPoints); in check_global_interpolation_with_derivatives2d() local
258 points, derivatives, derivativeIndices, degree); in check_global_interpolation_with_derivatives2d()
265 PointType derivative = spline.derivatives(knots(i), 1).col(1); in check_global_interpolation_with_derivatives2d()
266 PointType referenceDerivative = derivatives.col(i); in check_global_interpolation_with_derivatives2d()
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| /external/eigen/unsupported/Eigen/src/AutoDiff/ |
| D | AutoDiffScalar.h | 43 …* \param DerivativeType the vector type used to store/represent the derivatives. The base scalar t…
44 * as well as the number of derivatives to compute are determined from this type.
45 * Typical choices include, e.g., \c Vector4f for 4 derivatives, or \c VectorXf
46 * if the number of derivatives is not known at compile time, and/or, the number
47 * of derivatives is large.
52 …* This class represents a scalar value while tracking its respective derivatives using Eigen's exp…
62 * while derivatives are computed right away.
87 …and initializes the \a nbDer derivatives such that it corresponds to the \a derNumber -th variable…
95 * The derivatives are set to zero. */
103 /** Constructs an active scalar from its \a value and derivatives \a der */
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| D | AutoDiffJacobian.h | 87 av[j].derivatives().resize(x.rows()); in operator()
90 ax[i].derivatives() = DerivativeType::Unit(x.rows(),i); in operator()
101 jac.row(i) = av[i].derivatives(); in operator()
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| /external/eigen/unsupported/Eigen/src/Splines/ |
| D | SplineFitting.h | 243 * derivatives.
246 * \param derivatives The desired derivatives of the interpolating spline at interpolation
249 * must be the same size as @a derivatives.
252 * \returns A spline interpolating @a points with @a derivatives at those points.
260 const PointArrayType& derivatives,
265 * \brief Fits an interpolating spline to the given data points and derivatives.
268 … * \param derivatives The desired derivatives of the interpolating spline at interpolation points.
270 * must be the same size as @a derivatives.
274 * \returns A spline interpolating @a points with @a derivatives at those points.
282 const PointArrayType& derivatives,
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| D | Spline.h | 54 /** \brief The data type used to store the values of the basis function derivatives. */
115 * \brief Evaluation of spline derivatives of up-to given order.
124 * \param order The order up to which the derivatives are computed.
127 derivatives(Scalar u, DenseIndex order) const;
130 * \copydoc Spline::derivatives
136 derivatives(Scalar u, DenseIndex order = DerivativeOrder) const;
158 * \brief Computes the non-zero spline basis function derivatives up to given order.
167 * derivatives are computed.
168 * \param order The order up to which the basis function derivatives are computes.
328 // Retrieve the basis function derivatives up to the desired order... in derivativesImpl()
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| D | SplineFwd.h | 33 …enum { NumOfDerivativesAtCompileTime = OrderAtCompileTime /*!< The number of derivatives defined f…
40 /** \brief The data type used to store the values of the basis function derivatives. */
69 …vativeOrder==Dynamic ? Dynamic : _DerivativeOrder+1 /*!< The number of derivatives defined for the…
73 /** \brief The data type used to store the values of the basis function derivatives. */
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| /external/tensorflow/tensorflow/python/ops/ |
| D | gradients_impl.py | 49 """Constructs symbolic derivatives of sum of `ys` w.r.t. x in `xs`.
55 `gradients()` adds ops to the graph to output the derivatives of `ys` with
63 derivatives using a different initial gradient for each y (e.g., if
70 other things, this allows computation of partial derivatives as opposed to
71 total derivatives. For example:
79 Here the partial derivatives `g` evaluate to `[1.0, 1.0]`, compared to the
80 total derivatives `tf.gradients(a + b, [a, b])`, which take into account the
116 phase. This function is used to evaluate the derivatives of the cost function
181 """Constructs symbolic derivatives of sum of `ys` w.r.t. x in `xs`.
191 `gradients()` adds ops to the graph to output the derivatives of `ys` with
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| D | image_grad.py | 26 """The derivatives for nearest neighbor resizing.
51 """The derivatives for bilinear resizing.
70 """The derivatives for ScaleAndTranslate transformation op.
92 """The derivatives for bicubic resizing.
114 """The derivatives for crop_and_resize.
172 We perform the multivariate derivative and compute all partial derivatives
208 # Derivatives of R, G, B wrt Value slice
213 # Derivatives of R, G, B wrt Saturation slice
240 # Derivatives of R, G, B wrt Hue slice
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/ode/nonstiff/ |
| D | AdamsNordsieckTransformer.java | 36 * classical representation with several previous first derivatives and Nordsieck
37 * representation with higher order scaled derivatives.</p>
39 * <p>We define scaled derivatives s<sub>i</sub>(n) at step n as:
49 * uses first derivatives only, i.e. it handles y<sub>n</sub>, s<sub>1</sub>(n) and
57 * higher degrees scaled derivatives all taken at the same step, i.e it handles y<sub>n</sub>,
139 /** Initialization matrix for the higher order derivatives wrt y'', y''' ... */
142 … /** Update matrix for the higher order derivatives h<sup>2</sup>/2y'', h<sup>3</sup>/6 y''' ... */
145 /** Update coefficients of the higher order derivatives wrt y'. */
254 /** Initialize the high order scaled derivatives at step start.
256 * @param multistep scaled derivatives after step start (hy'1, ..., hy'k-1)
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| D | AdamsIntegrator.java | 97 /** Update the high order scaled derivatives for Adams integrators (phase 1).
98 * <p>The complete update of high order derivatives has a form similar to:
103 * @param highOrder high order scaled derivatives
105 * @return updated high order derivatives
112 /** Update the high order scaled derivatives Adams integrators (phase 2).
113 * <p>The complete update of high order derivatives has a form similar to:
119 * @param start first order scaled derivatives at step start
120 * @param end first order scaled derivatives at step end
121 * @param highOrder high order scaled derivatives, will be modified
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| /external/mesa3d/src/panfrost/midgard/ |
| D | midgard_derivatives.c | 30 /* Derivatives in Midgard are implemented on the texture pipe, rather than the
32 * instructions require (implicit) derivatives to be calculated anyway, so it
34 * texturing ops that calculate derivatives, there are two explicit texture ops
38 * One major caveat is that derivatives can only be calculated on up to a vec2
40 * derivatives will be vec2 (autocalculating mip levels of 2D texture
45 * generation), we generate texture ops 1:1 to the incoming NIR derivatives.
47 * scan for vec3/vec4 derivatives and lower (split) to multiple instructions.
72 /* Returns true if a texturing op computes derivatives either explicitly or
78 /* Only fragment shaders may compute derivatives, but the sense of in mir_op_computes_derivatives()
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/ode/jacobians/ |
| D | StepInterpolatorWithJacobians.java | 99 * Get the partial derivatives of the state vector with respect to
104 * @return partial derivatives of the state vector with respect to
113 * Get the partial derivatives of the state vector with respect to
118 * @return partial derivatives of the state vector with respect to
127 * Get the time derivatives of the state vector of the interpolated point.
131 * @return derivatives of the state vector at time {@link #getInterpolatedTime}
139 * Get the time derivatives of the jacobian of the state vector
144 * @return time derivatives of the jacobian of the state vector
153 * Get the time derivatives of the jacobian of the state vector
158 * @return time derivatives of the jacobian of the state vector
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| D | EventHandlerWithJacobians.java | 28 * precise date is unknown a priori, or when the derivatives have
93 /** Reset derivatives indicator.
96 * go on after the event ending the current step, with a new derivatives
139 * differential equations} to switch the derivatives computation in
141 * or continue integration, possibly with a reset state or derivatives.</p>
152 * derivatives,</li>
154 * will recompute the derivatives,
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| /external/tensorflow/tensorflow/java/src/main/java/org/tensorflow/op/core/ |
| D | Gradients.java | 31 * Adds operations to compute the partial derivatives of sum of {@code y}s w.r.t {@code x}s,
34 …* If {@code Options.dx()} values are set, they are as the initial symbolic partial derivatives of …
40 * The partial derivatives are returned in output {@code dy}, with the size of {@code x}.
60 * @param dx partial derivatives of some loss function {@code L} w.r.t. {@code y}
79 * @param x inputs of the function for which partial derivatives are computed
116 * @param x inputs of the function for which partial derivatives are computed
128 * @param dx partial derivatives of some loss function {@code L} w.r.t. {@code y}
142 * Partial derivatives of {@code y}s w.r.t. {@code x}s, with the size of {@code x}
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| /external/eigen/unsupported/Eigen/src/MatrixFunctions/ |
| D | StemFunction.h | 17 /** \brief The exponential function (and its derivatives). */
25 /** \brief Cosine (and its derivatives). */
50 /** \brief Sine (and its derivatives). */
75 /** \brief Hyperbolic cosine (and its derivatives). */
94 /** \brief Hyperbolic sine (and its derivatives). */
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/ode/ |
| D | MultistepIntegrator.java | 34 * <p>We define scaled derivatives s<sub>i</sub>(n) at step n as:
43 * the Nordsieck vector with higher degrees scaled derivatives all taken at the same
67 /** Nordsieck matrix of the higher scaled derivatives.
240 /** Initialize the high order scaled derivatives at step start.
242 * @param multistep scaled derivatives after step start (hy'1, ..., hy'k-1)
244 * @return high order scaled derivatives at step start
301 /** Initialize the high order scaled derivatives at step start.
303 * @param multistep scaled derivatives after step start (hy'1, ..., hy'k-1)
305 * @return high order derivatives at step start
339 // compute the high order scaled derivatives in handleStep()
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/ode/events/ |
| D | EventHandler.java | 26 * precise date is unknown a priori, or when the derivatives have
68 /** Reset derivatives indicator.
71 * go on after the event ending the current step, with a new derivatives
109 * differential equations} to switch the derivatives computation in
111 * or continue integration, possibly with a reset state or derivatives.</p>
121 * derivatives,</li>
123 * will recompute the derivatives,
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/analysis/interpolation/ |
| D | BicubicSplineInterpolatingFunction.java | 38 * and function derivatives values
66 * Partial derivatives
67 * The value of the first index determines the kind of derivatives:
68 * 0 = first partial derivatives wrt x
69 * 1 = first partial derivatives wrt y
70 * 2 = second partial derivatives wrt x
71 * 3 = second partial derivatives wrt y
72 * 4 = cross partial derivatives
259 * Compute all partial derivatives.
302 * function partial derivatives values at the four corners of a grid
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| D | TricubicSplineInterpolator.java | 101 // Partial derivatives wrt x and wrt y in interpolate()
118 // Partial derivatives wrt y and wrt z in interpolate()
133 // Partial derivatives wrt x and wrt z in interpolate()
146 // Third partial cross-derivatives in interpolate()
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| /external/tensorflow/tensorflow/go/op/ |
| D | gradients.go | 29 // x: inputs of the function for which partial derivatives are computed
30 // dx: if not null, the partial derivatives of some loss function L w.r.t. y
32 // return the partial derivatives
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| /external/webrtc/modules/congestion_controller/goog_cc/ |
| D | loss_based_bwe_v2.cc | 807 LossBasedBweV2::Derivatives LossBasedBweV2::GetDerivatives( in GetDerivatives()
809 Derivatives derivatives; in GetDerivatives() local
823 derivatives.first += in GetDerivatives()
827 derivatives.second -= in GetDerivatives()
834 if (derivatives.second >= 0.0) { in GetDerivatives()
837 << derivatives.second << "."; in GetDerivatives()
838 derivatives.second = -1.0e-6; in GetDerivatives()
841 return derivatives; in GetDerivatives()
979 const Derivatives derivatives = GetDerivatives(channel_parameters); in NewtonsMethodUpdate() local
981 config_->newton_step_size * derivatives.first / derivatives.second; in NewtonsMethodUpdate()
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| /external/tensorflow/tensorflow/java/src/main/java/org/tensorflow/ |
| D | Graph.java | 148 * Adds operations to compute the partial derivatives of sum of {@code y}s w.r.t {@code x}s, i.e.,
151 * <p>{@code dx} are used as initial gradients (which represent the symbolic partial derivatives
166 * @param x inputs of the function for which partial derivatives are computed
167 * @param dx if not null, the partial derivatives of some loss function {@code L} w.r.t. {@code y}
168 * @return the partial derivatives {@code dy} with the size of {@code x}
226 * Adds operations to compute the partial derivatives of sum of {@code y}s w.r.t {@code x}s,
233 * @param x inputs of the function for which partial derivatives are computed
234 * @return the partial derivatives {@code dy} with the size of {@code x}
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| /external/replicaisland/src/com/replica/replicaisland/ |
| D | AllocationGuard.java | 22 * allocation of AllocationGuard or its derivatives will cause an error log entry. Note
23 * that AllocationGuard requires all of its derivatives to call super() in their constructor.
|