| /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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| /cts/apps/CameraITS/tests/scene1/ |
| test_linearity.py | 105 line, residuals, _, _, _ = numpy.polyfit(range(len(sensitivities)),
107 print 'Line: m=%f, b=%f, resid=%f'%(line[0], line[1], residuals[0])
109 residuals[0], RESIDUAL_THRESHOLD)
110 assert residuals[0] < RESIDUAL_THRESHOLD, msg
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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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| LevenbergMarquardtEstimator.java | 284 qTy(residuals);
324 sum += jacobian[index] * residuals[i];
349 double[] tmpVec = residuals;
350 residuals = oldRes;
438 tmpVec = residuals;
439 residuals = oldRes;
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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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| LevenbergMarquardtOptimizer.java | 350 double[] tmpVec = residuals;
351 residuals = oldRes;
448 tmpVec = residuals;
449 residuals = oldRes;
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| /external/webp/src/enc/ |
| predictor_enc.c | 295 // applied, quantizing residuals to multiples of quantization levels up to
341 uint32_t residuals[1 << MAX_TRANSFORM_BITS]; local
380 used_subtract_green, residuals);
382 UpdateHisto(histo_argb, residuals[relative_x]);
409 // Converts pixels of the image to residuals with respect to predictions.
411 // residuals to multiples of quantization levels up to max_quantization
447 // residuals before proceeding with the next row.
471 // Finds the best predictor for each tile, and converts the image to residuals
473 // near lossless processing, shaving off more bits of residuals for lower
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| /external/tensorflow/tensorflow/python/eager/ |
| function_gradients_test.py | 735 residuals = value - model
736 loss = 0.5 * math_ops.reduce_mean(math_ops.pow(residuals, 2))
|
