/external/libopus/src/ |
H A D | mlp_data.c | 2 It contains multi-layer perceptron (MLP) weights. */ 8 static const float weights[422] = { variable 104 weights
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H A D | mlp.h | 36 const float *weights; member in struct:__anon9031
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/external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/ |
H A D | LeastSquaresConverter.java | 49 * This class support combination of residuals with or without weights and correlations. 66 /** Optional weights for the residuals. */ 67 private final double[] weights; field in class:LeastSquaresConverter 80 this.weights = null; 84 /** Build a simple converter for uncorrelated residuals with the specific weights. 96 * In this case, the weights array should be initialized with value 102 * weights array must have consistent sizes or a {@link FunctionEvaluationException} will be 107 * @param weights weights to apply to the residuals 108 * @exception IllegalArgumentException if the observations vector and the weights 112 LeastSquaresConverter(final MultivariateVectorialFunction function, final double[] observations, final double[] weights) argument [all...] |
H A D | DifferentiableMultivariateVectorialOptimizer.java | 101 * @param weights weight for the least squares cost computation 110 double[] target, double[] weights, 109 optimize(DifferentiableMultivariateVectorialFunction f, double[] target, double[] weights, double[] startPoint) argument
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H A D | MultiStartDifferentiableMultivariateVectorialOptimizer.java | 177 final double[] target, final double[] weights, 192 optima[i] = optimizer.optimize(f, target, weights, 221 sum += weights[i] * ri * ri; 176 optimize(final DifferentiableMultivariateVectorialFunction f, final double[] target, final double[] weights, final double[] startPoint) argument
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/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/ |
H A D | WeightedEvaluation.java | 29 * using the supplied weights. 32 * @param weights array of weights 35 double evaluate(double[] values, double[] weights); argument 39 * in the input array, using corresponding entries in the supplied weights array. 42 * @param weights array of weights 47 double evaluate(double[] values, double[] weights, int begin, int length); argument
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H A D | AbstractUnivariateStatistic.java | 165 * and the weights are all non-negative, non-NaN, finite, and not all zero. 169 * positive length and the weights array contains legitimate values.</li> 172 * <li>the weights array is null</li> 173 * <li>the weights array does not have the same length as the values array</li> 174 * <li>the weights array contains one or more infinite values</li> 175 * <li>the weights array contains one or more NaN values</li> 176 * <li>the weights array contains negative values</li> 184 * @param weights the weights array 193 final double[] weights, 191 test( final double[] values, final double[] weights, final int begin, final int length) argument [all...] |
/external/clang/test/Profile/ |
H A D | c-general.c | 123 // Never reached -> no weights 140 // Never reached -> no weights 199 // never reached -> no weights 216 static int weights[] = {1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5}; local 218 // No cases -> no weights 219 switch (weights[0]) { 228 for (int i = 0, len = sizeof(weights) / sizeof(weights[0]); i < len; ++i) { 231 switch (i[weights]) { 278 // Never reached -> no weights [all...] |
/external/apache-commons-math/src/main/java/org/apache/commons/math/analysis/integration/ |
H A D | LegendreGaussIntegrator.java | 45 * Legendre polynomial. The weights a<sub>i</sub> of the quadrature formula 120 private final double[] weights; field in class:LegendreGaussIntegrator 135 weights = WEIGHTS_2; 139 weights = WEIGHTS_3; 143 weights = WEIGHTS_4; 147 weights = WEIGHTS_5; 227 sum += weights[j] * f.value(midPoint + halfStep * abscissas[j]);
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/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/moment/ |
H A D | Mean.java | 180 * described above is used here, with weights applied in computing both the original 185 * <li>the weights array is null</li> 186 * <li>the weights array does not have the same length as the values array</li> 187 * <li>the weights array contains one or more infinite values</li> 188 * <li>the weights array contains one or more NaN values</li> 189 * <li>the weights array contains negative values</li> 194 * @param weights the weights array 201 public double evaluate(final double[] values, final double[] weights, argument 203 if (test(values, weights, begi 244 evaluate(final double[] values, final double[] weights) argument [all...] |
H A D | Variance.java | 266 * Σ(weights[i]*(values[i] - weightedMean)<sup>2</sup>)/(Σ(weights[i]) - 1) 271 * weights are equal, unless all weights are equal to 1. The formula assumes that 272 * weights are to be treated as "expansion values," as will be the case if for example 273 * the weights represent frequency counts. To normalize weights so that the denominator 275 * <code>evaluate(values, MathUtils.normalizeArray(weights, values.length)); </code> 282 * <li>the weights array is null</li> 283 * <li>the weights arra 302 evaluate(final double[] values, final double[] weights, final int begin, final int length) argument 358 evaluate(final double[] values, final double[] weights) argument 490 evaluate(final double[] values, final double[] weights, final double mean, final int begin, final int length) argument 564 evaluate(final double[] values, final double[] weights, final double mean) argument [all...] |
/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/summary/ |
H A D | Product.java | 139 * <li>the weights array is null</li> 140 * <li>the weights array does not have the same length as the values array</li> 141 * <li>the weights array contains one or more infinite values</li> 142 * <li>the weights array contains one or more NaN values</li> 143 * <li>the weights array contains negative values</li> 148 * weighted product = ∏values[i]<sup>weights[i]</sup> 150 * that is, the weights are applied as exponents when computing the weighted product.</p> 153 * @param weights the weights array 160 public double evaluate(final double[] values, final double[] weights, argument 195 evaluate(final double[] values, final double[] weights) argument [all...] |
H A D | Sum.java | 138 * <li>the weights array is null</li> 139 * <li>the weights array does not have the same length as the values array</li> 140 * <li>the weights array contains one or more infinite values</li> 141 * <li>the weights array contains one or more NaN values</li> 142 * <li>the weights array contains negative values</li> 147 * weighted sum = Σ(values[i] * weights[i]) 151 * @param weights the weights array 158 public double evaluate(final double[] values, final double[] weights, argument 161 if (test(values, weights, begi 192 evaluate(final double[] values, final double[] weights) argument [all...] |
/external/freetype/src/base/ |
H A D | ftlcdfil.c | 38 FT_Byte* weights = library->lcd_weights; local 54 /* the values in `weights' can exceed 0xFF */ 63 fir[0] = weights[2] * val1; 64 fir[1] = weights[3] * val1; 65 fir[2] = weights[4] * val1; 69 fir[0] += weights[1] * val1; 70 fir[1] += weights[2] * val1; 71 fir[2] += weights[3] * val1; 72 fir[3] += weights[4] * val1; 80 pix = fir[0] + weights[ [all...] |
/external/jmonkeyengine/engine/src/core/com/jme3/animation/ |
H A D | PoseTrack.java | 56 float[] weights; field in class:PoseTrack.PoseFrame 58 public PoseFrame(Pose[] poses, float[] weights) { argument 60 this.weights = weights; 71 result.weights = this.weights.clone(); 87 out.write(weights, "weights", null); 93 weights = in.readFloatArray("weights", nul [all...] |
/external/openfst/src/include/fst/script/ |
H A D | shortest-path.h | 65 vector<typename Arc::Weight> weights; local 73 ArcFilter>::Construct(ifst, &weights); 78 ShortestPath(ifst, ofst, &weights, spopts); 85 ArcFilter>::Construct(ifst, &weights); 90 ShortestPath(ifst, ofst, &weights, spopts); 97 ArcFilter >::Construct(ifst, &weights); 102 ShortestPath(ifst, ofst, &weights, spopts); 109 ArcFilter>::Construct(ifst, &weights); 114 ShortestPath(ifst, ofst, &weights, spopts); 121 ArcFilter>::Construct(ifst, &weights); [all...] |
H A D | rmepsilon.h | 174 vector<typename Arc::Weight> weights; local 176 RmEpsilonHelper(fst, &weights, opts); 178 // Copy the weights back 179 args->arg2->resize(weights.size()); 180 for (unsigned i = 0; i < weights.size(); ++i) { 181 (*args->arg2)[i] = WeightClass(weights[i]);
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H A D | shortest-distance.h | 95 const Fst<Arc> &fst, const vector<typename Arc::Weight> *weights) { 106 vector<typename Arc::Weight> weights; local 112 fst, &weights); 115 ShortestDistance(fst, &weights, sdopts); 122 fst, &weights); 126 ShortestDistance(fst, &weights, sdopts); 133 fst, &weights); 137 ShortestDistance(fst, &weights, sdopts); 145 fst, &weights); 149 ShortestDistance(fst, &weights, sdopt 94 Construct( const Fst<Arc> &fst, const vector<typename Arc::Weight> *weights) argument [all...] |
/external/pdfium/third_party/freetype/src/base/ |
H A D | ftlcdfil.c | 38 FT_Byte* weights = library->lcd_weights; local 54 /* the values in `weights' can exceed 0xFF */ 63 fir[0] = weights[2] * val1; 64 fir[1] = weights[3] * val1; 65 fir[2] = weights[4] * val1; 69 fir[0] += weights[1] * val1; 70 fir[1] += weights[2] * val1; 71 fir[2] += weights[3] * val1; 72 fir[3] += weights[4] * val1; 80 pix = fir[0] + weights[ [all...] |
/external/apache-commons-math/src/main/java/org/apache/commons/math/analysis/interpolation/ |
H A D | LoessInterpolator.java | 189 * @param weights point weights: coefficients by which the robustness weight of a point is multiplied 199 public final double[] smooth(final double[] xval, final double[] yval, final double[] weights) argument 214 checkAllFiniteReal(weights, LocalizedFormats.NON_REAL_FINITE_WEIGHT); 242 // starting with all robustness weights set to 1. 254 updateBandwidthInterval(xval, weights, i, bandwidthInterval); 270 // the product of robustness weights and the tricube 286 final double w = tricube(dist * denom) * robustnessWeights[k] * weights[k]; 313 // No need to recompute the robustness weights at the last 319 // Recompute the robustness weights 386 updateBandwidthInterval(final double[] xval, final double[] weights, final int i, final int[] bandwidthInterval) argument 408 nextNonzero(final double[] weights, final int i) argument [all...] |
/external/opencv/cv/src/ |
H A D | cvlinefit.cpp | 46 icvFitLine2D_wods( CvPoint2D32f * points, int _count, float *weights, float *line ) argument 56 if( weights == 0 ) 72 x += weights[i] * points[i].x; 73 y += weights[i] * points[i].y; 74 x2 += weights[i] * points[i].x * points[i].x; 75 y2 += weights[i] * points[i].y * points[i].y; 76 xy += weights[i] * points[i].x * points[i].y; 77 w += weights[i]; 102 icvFitLine3D_wods( CvPoint3D32f * points, int count, float *weights, float *line ) argument 116 if( weights ) [all...] |
/external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/general/ |
H A D | AbstractLeastSquaresOptimizer.java | 255 * the reciprocal of the weights. 327 final double[] target, final double[] weights, 331 if (target.length != weights.length) { 333 target.length, weights.length); 345 residualsWeights = weights.clone(); 326 optimize(final DifferentiableMultivariateVectorialFunction f, final double[] target, final double[] weights, final double[] startPoint) argument
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/external/eigen/unsupported/Eigen/src/MatrixFunctions/ |
H A D | MatrixLogarithm.h | 238 const RealScalar weights[] = { 0.2777777777777777777777777777777778L, 0.4444444444444444444444444444444444L, local 244 result += weights[k] * (MatrixType::Identity(T.rows(), T.rows()) + nodes[k] * TminusI) 254 const RealScalar weights[] = { 0.1739274225687269286865319746109997L, 0.3260725774312730713134680253890003L, local 260 result += weights[k] * (MatrixType::Identity(T.rows(), T.rows()) + nodes[k] * TminusI) 271 const RealScalar weights[] = { 0.1184634425280945437571320203599587L, 0.2393143352496832340206457574178191L, local 278 result += weights[k] * (MatrixType::Identity(T.rows(), T.rows()) + nodes[k] * TminusI) 289 const RealScalar weights[] = { 0.0856622461895851725201480710863665L, 0.1803807865240693037849167569188581L, local 296 result += weights[k] * (MatrixType::Identity(T.rows(), T.rows()) + nodes[k] * TminusI) 308 const RealScalar weights[] = { 0.0647424830844348466353057163395410L, 0.1398526957446383339507338857118898L, local 316 result += weights[ 328 const RealScalar weights[] = { 0.0506142681451881295762656771549811L, 0.1111905172266872352721779972131204L, local 349 const RealScalar weights[] = { 0.0406371941807872059859460790552618L, 0.0903240803474287020292360156214564L, local 371 const RealScalar weights[] = { 0.0333356721543440687967844049466659L, 0.0747256745752902965728881698288487L, local 394 const RealScalar weights[] = { 0.0278342835580868332413768602212743L, 0.0627901847324523123173471496119701L, local [all...] |
/external/jmonkeyengine/engine/src/core/com/jme3/math/ |
H A D | Spline.java | 28 private float[] weights; //weights of NURBS spline field in class:Spline 57 throw new IllegalArgumentException("To create NURBS spline use: 'public Spline(Vector3f[] controlPoints, float[] weights, float[] nurbKnots)' constructor!"); 86 throw new IllegalArgumentException("To create NURBS spline use: 'public Spline(Vector3f[] controlPoints, float[] weights, float[] nurbKnots)' constructor!"); 111 this.weights = new float[controlPoints.size()]; 113 this.basisFunctionDegree = nurbKnots.size() - weights.length; 117 this.weights[i] = controlPoint.w; 379 return knots.get(weights.length); 391 * This method returns NURBS' spline weights. 392 * @return NURBS' spline weights [all...] |
/external/skia/src/core/ |
H A D | SkPathRef.cpp | 287 SkScalar** weights) { 356 SkASSERT(weights); 357 *weights = fConicWeights.append(numVbs); 285 growForRepeatedVerb(int verb, int numVbs, SkScalar** weights) argument
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