| /external/ceres-solver/internal/ceres/ |
| evaluator_test_utils.h | 42 const double residuals[50]; member in struct:ceres::internal::ExpectedEvaluation
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| autodiff_cost_function_test.cc | 73 double residuals = 0.0; local
75 cost_function->Evaluate(parameters, &residuals, NULL);
76 EXPECT_EQ(10.0, residuals);
77 cost_function->Evaluate(parameters, &residuals, jacobians);
125 double residuals = 0.0; local
127 cost_function->Evaluate(parameters, &residuals, NULL);
128 EXPECT_EQ(45.0, residuals);
130 cost_function->Evaluate(parameters, &residuals, jacobians);
131 EXPECT_EQ(residuals, 45.0);
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| corrector_test.cc | 59 double residuals = sqrt(3.0); local
61 double sq_norm = residuals * residuals;
72 residuals * sqrt(kRho[1]) / (1 - kAlpha);
79 c.CorrectJacobian(1.0, 1.0, &residuals, &jacobian);
80 c.CorrectResiduals(1.0, &residuals);
82 ASSERT_NEAR(residuals, kExpectedResidual, 1e-6);
87 double residuals = 0.0; local
89 double sq_norm = residuals * residuals;
115 double residuals = sqrt(3.0); local
146 double residuals[3]; local
214 double residuals[3]; local
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| numeric_diff_test_utils.cc | 47 double* residuals) const {
48 residuals[0] = residuals[1] = residuals[2] = 0;
50 residuals[0] += x1[i] * x2[i];
51 residuals[2] += x2[i] * x2[i];
53 residuals[1] = residuals[0] * residuals[0];
68 double residuals[3] = {-1e-100, -2e-100, -3e-100 } local
138 double residuals[2]; local
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| residual_block_test.cc | 56 double* residuals,
59 residuals[i] = i;
112 double residuals[3]; local
113 residual_block.Evaluate(true, &cost, residuals, NULL, scratch);
115 EXPECT_EQ(0.0, residuals[0]);
116 EXPECT_EQ(1.0, residuals[1]);
117 EXPECT_EQ(2.0, residuals[2]);
121 VectorRef(residuals, 3).setConstant(0.0);
137 residual_block.Evaluate(true, &cost, residuals, jacobian_ptrs, scratch);
139 EXPECT_EQ(0.0, residuals[0])
251 double residuals[3]; local
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| residual_block_utils_test.cc | 61 double residuals; local
67 &residuals,
73 // valid residuals and jacobians.
77 double* residuals,
79 residuals[0] = 1;
92 double* residuals,
94 // Forget to update the residuals.
95 // residuals[0] = 1;
106 double* residuals,
108 residuals[0] = 1
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| evaluator_test.cc | 62 double* residuals,
65 residuals[i] = i + 1;
80 // between Jacobians of different residuals for the same parameter.
149 Vector residuals(num_residuals);
150 residuals.setConstant(-2000);
167 expected_residuals != NULL ? &residuals[0] : NULL,
183 &residuals[0],
195 (i & 1) ? expected.residuals : NULL,
224 // Residuals
262 // Residuals
630 double residuals[2] = { -2, -2 }; local
640 double residuals[2] = { -2, -2}; local
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| autodiff_test.cc | 357 double residuals[kMaxResiduals]; local
361 // residuals.
365 // Tweak the number of residuals to produce.
368 // Run autodiff with the new number of residuals.
370 functor, parameters, num_residuals, residuals, jacobians)));
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| problem_test.cc | 66 double* residuals,
69 residuals[i] = 1;
87 double* residuals,
90 residuals[i] = 2;
110 double* residuals,
113 residuals[i] = 3;
314 double* residuals,
407 // Verify that the hash set of residuals is maintained consistently.
843 // Remove a parameter block, which in turn removes the dependent residuals
1095 vector<double> residuals; local
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/ |
| LeastSquaresConverter.java | 39 * This class computes a possibly weighted squared sum of the residuals, which is
40 * a scalar value. The residuals are the difference between the theoretical model
49 * This class support combination of residuals with or without weights and correlations.
63 /** Observations to be compared to objective function to compute residuals. */
66 /** Optional weights for the residuals. */
69 /** Optional scaling matrix (weight and correlations) for the residuals. */
72 /** Build a simple converter for uncorrelated residuals with the same weight.
73 * @param function vectorial residuals function to wrap
74 * @param observations observations to be compared to objective function to compute residuals
84 /** Build a simple converter for uncorrelated residuals with the specific weights
163 final double[] residuals = function.value(point); local
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/regression/ |
| GLSMultipleLinearRegression.java | 130 RealVector residuals = calculateResiduals(); local
131 double t = residuals.dotProduct(getOmegaInverse().operate(residuals));
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| OLSMultipleLinearRegression.java | 143 * Returns the sum of squared residuals.
149 final RealVector residuals = calculateResiduals(); local
150 return residuals.dotProduct(residuals);
157 * where SSR is the {@link #calculateResidualSumOfSquares() sum of squared residuals}
171 * where SSR is the {@link #calculateResidualSumOfSquares() sum of squared residuals},
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| AbstractMultipleLinearRegression.java | 346 RealVector residuals = calculateResiduals(); local
347 return residuals.dotProduct(residuals) /
352 * Calculates the residuals of multiple linear regression in matrix
359 * @return The residuals [n,1] matrix
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/analysis/interpolation/ |
| LoessInterpolator.java | 235 final double[] residuals = new double[n]; local
310 residuals[i] = FastMath.abs(yval[i] - res[i]);
324 System.arraycopy(residuals, 0, sortedResiduals, 0, n);
333 final double arg = residuals[i] / (6 * medianResidual);
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/estimation/ |
| AbstractEstimator.java | 66 /** Residuals array.
72 protected double[] residuals; field in class:AbstractEstimator
74 /** Cost value (square root of the sum of the residuals). */
147 * Update the residuals array and cost function value.
164 residuals[i] = FastMath.sqrt(wm.getWeight()) * residual;
174 * mean of the square of all weighted residuals. This is related to the
295 residuals = new double[rows];
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/general/ |
| AbstractLeastSquaresOptimizer.java | 86 /** Current residuals. */
87 protected double[] residuals; field in class:AbstractLeastSquaresOptimizer
92 /** Weighted residuals */
95 /** Cost value (square root of the sum of the residuals). */
208 * Update the residuals array and cost function value.
229 residuals[i] = residual;
241 * mean of the square of all weighted residuals. This is related to the
253 * Get a Chi-Square-like value assuming the N residuals follow N
347 this.residuals = new double[target.length];
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