/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 [all...] |
/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; [all...] |
/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))
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