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/external/apache-commons-math/src/main/java/org/apache/commons/math/estimation/
DLevenbergMarquardtEstimator.java290 jacobian[k * cols + pk] = diagR[pk]; in estimate()
324 sum += jacobian[index] * residuals[i]; in estimate()
390 work1[i] += jacobian[index] * dirJ; in estimate()
513 lmDir[permutation[i]] -= ypk * jacobian[index]; in determineLMParameter()
551 sum += jacobian[index] * work1[permutation[i]]; in determineLMParameter()
568 sum += jacobian[index] * qy[i]; in determineLMParameter()
629 work1[permutation[i]] -= jacobian[i * cols + pj] * tmp; in determineLMParameter()
680 jacobian[i * cols + pj] = jacobian[j * cols + permutation[i]]; in determineLMDirection()
711 double rkk = jacobian[k * cols + pk]; in determineLMDirection()
724 jacobian[k * cols + pk] = cos * rkk + sin * lmDiag[k]; in determineLMDirection()
[all …]
DAbstractEstimator.java58 protected double[] jacobian; field in AbstractEstimator
128 Arrays.fill(jacobian, 0); in updateJacobian()
134 jacobian[index++] = factor * wm.getPartial(parameters[j]); in updateJacobian()
229 sum += jacobian[k + i] * jacobian[k + j]; in getCovariances()
294 jacobian = new double[rows * cols]; in initializeEstimate()
/external/eigen/unsupported/Eigen/src/AutoDiff/
DAutoDiffVector.h73 : m_values(other.values()), m_jacobian(other.jacobian()) in AutoDiffVector()
77 : m_values(other.values()), m_jacobian(other.jacobian()) in AutoDiffVector()
84 m_jacobian = other.jacobian();
91 m_jacobian = other.jacobian();
98 inline const JacobianType& jacobian() const { return m_jacobian; } in jacobian() function
99 inline JacobianType& jacobian() { return m_jacobian; } in jacobian() function
111 m_jacobian + other.jacobian());
119 m_jacobian += other.jacobian();
133 m_jacobian - other.jacobian());
141 m_jacobian -= other.jacobian();
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/external/boringssl/src/crypto/fipsmodule/ec/
Dmake_p256-x86_64-tests.go37 jacobian const
139 if aCoord == jacobian {
148 if bCoord == jacobian {
173 printTestCase(zero, zero, jacobian, zero, zero, jacobian)
180 printTestCase(gx, gy, affine, zero, zero, jacobian)
190 printTestCase(ax, ay, jacobian, bx, by, jacobian)
196 printTestCase(ax, ay, jacobian, ax, ay, jacobian)
/external/tensorflow/tensorflow/python/ops/
Dgradient_checker_v2.py161 jacobian = np.zeros((y_size, x_size), dtype=x.dtype.real_dtype.as_numpy_dtype)
180 jacobian[row, c_begin:c_end] += v.flat
182 jacobian[row, :] = grad.ravel().view(jacobian.dtype)
197 logging.vlog(1, "Theoretical Jacobian =\n%s", jacobian)
198 return jacobian
238 jacobian = np.zeros((y_size, x_size), dtype=x_dtype)
253 jacobian[:, col] = diff.ravel().view(y_dtype)
255 logging.vlog(1, "Numeric Jacobian =\n%s", jacobian)
256 return jacobian
Dgradient_checker.py94 jacobian = np.zeros((x_size, dy_size),
112 jacobian[r_begin:r_end, col] += v.flat
117 jacobian[:, col] = backprop.ravel().view(jacobian.dtype)
131 logging.vlog(1, "Theoretical Jacobian =\n%s", jacobian)
132 return jacobian
178 jacobian = np.zeros((x_size, y_size), dtype=x_dtype)
190 jacobian[row, :] = diff.ravel().view(y_dtype)
192 logging.vlog(1, "Numeric Jacobian =\n%s", jacobian)
193 return jacobian
/external/tensorflow/tensorflow/python/ops/parallel_for/
Dgradients_test.py148 pfor_jacobians = gradients.jacobian(output, weights, use_pfor=True)
150 gradients.jacobian(x, weights, use_pfor=True) for x in pfor_jacobians
156 gradients.jacobian(x, weights, use_pfor=False) for x in while_jacobians
320 return gradients.jacobian(
337 jacobians = gradients.jacobian(output, variables.trainable_variables())
342 return gradients.jacobian(
369 for grad_func in [gradients.jacobian, gradients.batch_jacobian]:
378 jacobian_pfor = gradients.jacobian(y, x, use_pfor=True)
379 jacobian_while = gradients.jacobian(y, x, use_pfor=False)
396 jacobian = gradients.jacobian(y, x)
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D__init__.py25 from tensorflow.python.ops.parallel_for.gradients import jacobian
Dgradients.py28 def jacobian(output, inputs, use_pfor=True, parallel_iterations=None): function
/external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/general/
DAbstractLeastSquaresOptimizer.java60 protected double[][] jacobian; field in AbstractLeastSquaresOptimizer
192 jacobian = jF.value(point); in updateJacobian()
193 if (jacobian.length != rows) { in updateJacobian()
195 jacobian.length, rows); in updateJacobian()
198 final double[] ji = jacobian[i]; in updateJacobian()
343 jF = f.jacobian(); in optimize()
352 jacobian = new double[rows][cols]; in optimize()
DGaussNewtonOptimizer.java84 final double[] grad = jacobian[i]; in doOptimize()
/external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/fitting/
DCurveFitter.java163 public MultivariateMatrixFunction jacobian() { in jacobian() method in CurveFitter.TheoreticalValuesFunction
168 final double[][] jacobian = new double[observations.size()][]; in jacobian()
172 jacobian[i++] = f.gradient(observed.getX(), point); in jacobian()
175 return jacobian; in jacobian()
/external/tensorflow/tensorflow/cc/framework/
Dgradient_checker.cc68 typename TTypes<JAC_T>::Matrix* jacobian, const int row, const int col, in SetJacobian() argument
70 (*jacobian)(row, col) = JAC_T{value}; in SetJacobian()
75 typename TTypes<JAC_T>::Matrix* jacobian, const int row, const int col, in SetJacobian() argument
77 (*jacobian)(row, col) = JAC_T{value.real()}; in SetJacobian()
79 (*jacobian)(row + 1, col) = JAC_T{value.imag()}; in SetJacobian()
81 (*jacobian)(row, col + 1) = JAC_T{value.imag()}; in SetJacobian()
162 auto jacobian = (*jacobian_ts)[x_idx * y_num + y_idx].matrix<JAC_T>(); in ComputeTheoreticalJacobianTranspose() local
165 SetJacobian<X_T, JAC_T>(&jacobian, r * x_stride, in ComputeTheoreticalJacobianTranspose()
251 auto jacobian = (*jacobian_ts)[x_idx * y_num + y_idx].matrix<JAC_T>(); in ComputeNumericJacobianTranspose() local
253 SetJacobian<Y_T, JAC_T>(&jacobian, r * x_stride + unit_dimension, in ComputeNumericJacobianTranspose()
/external/tensorflow/tensorflow/python/keras/integration_test/
Dgradients_test.py95 def jacobian(x): function
102 eager_result = jacobian(inp)
103 function_result = tf.function(jacobian)(inp)
Dforwardprop_test.py215 jac_back = tape.jacobian(output,
/external/tensorflow/tensorflow/python/eager/
Dbackprop_test.py852 g.jacobian(z, [x])
855 g.jacobian(y, [x])
864 self.assertAllClose(4. * 3. ** 3., g.jacobian(z, x))
1276 2 * math_ops.reduce_sum(dy * g.jacobian(y, v)) for v in variables
1688 jacobian = g.jacobian(
1691 return jacobian, answer
1695 jacobian, answer = self._jacobian(experimental_use_pfor=True)
1696 for j, a in zip(jacobian, answer):
1701 jacobian, answer = self._jacobian(experimental_use_pfor=False)
1702 for j, a in zip(jacobian, answer):
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/external/apache-commons-math/src/main/java/org/apache/commons/math/analysis/
DDifferentiableMultivariateVectorialFunction.java34 MultivariateMatrixFunction jacobian(); in jacobian() method
/external/tensorflow/tensorflow/python/ops/numpy_ops/
Dnp_interop_test.py353 jacobian = g.jacobian(z, [x, y])
356 self.assertIsInstance(jacobian[0], np.ndarray)
357 self.assertIsInstance(jacobian[1], np.ndarray)
358 self.assertAllClose(jacobian, answer)
/external/eigen/unsupported/test/
Dautodiff.cpp136 void operator() (const T1 &input, T2 *output, T3 *jacobian, const Scalar dt) const in operator ()()
144 if (jacobian) in operator ()()
146 T3 &j = *jacobian; in operator ()()
/external/tensorflow/tensorflow/tools/api/golden/v2/
Dtensorflow.autodiff.-gradient-tape.pbtxt18 name: "jacobian"
Dtensorflow.-gradient-tape.pbtxt18 name: "jacobian"
/external/tensorflow/tensorflow/tools/api/golden/v1/
Dtensorflow.-gradient-tape.pbtxt18 name: "jacobian"
/external/eigen/unsupported/Eigen/
DNonLinearOptimization61 * of the jacobian if ever).
78 * Both algorithms can use either the jacobian (provided by the user) or compute
/external/tensorflow/tensorflow/python/training/experimental/
Dloss_scaling_gradient_tape.py183 def jacobian(self, member in LossScaleGradientTape
/external/eigen/unsupported/Eigen/src/LevenbergMarquardt/
DLevenbergMarquardt.h223 JacobianType& jacobian() {return m_fjac; } in jacobian() function

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