/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/libopus/src/ |
H A D | mlp.h | 36 const float *weights; member in struct:__anon10617
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/external/opencv3/modules/java/src/ |
H A D | objdetect+Objdetect.java | 22 // C++: void groupRectangles(vector_Rect& rectList, vector_int& weights, int groupThreshold, double eps = 0.2) 25 //javadoc: groupRectangles(rectList, weights, groupThreshold, eps) 26 public static void groupRectangles(MatOfRect rectList, MatOfInt weights, int groupThreshold, double eps) argument 29 Mat weights_mat = weights; 35 //javadoc: groupRectangles(rectList, weights, groupThreshold) 36 public static void groupRectangles(MatOfRect rectList, MatOfInt weights, int groupThreshold) argument 39 Mat weights_mat = weights; 48 // C++: void groupRectangles(vector_Rect& rectList, vector_int& weights, int groupThreshold, double eps = 0.2)
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/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/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/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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/external/clang/test/Profile/ |
H A D | c-general.c | 124 // Never reached -> no weights 141 // Never reached -> no weights 200 // never reached -> no weights 217 static int weights[] = {1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5}; local 219 // No cases -> no weights 220 switch (weights[0]) { 229 for (int i = 0, len = sizeof(weights) / sizeof(weights[0]); i < len; ++i) { 232 switch (i[weights]) { 279 // Never reached -> no weights [all...] |
/external/opencv3/modules/cudalegacy/src/cuda/ |
H A D | gmg.cu | 76 __device__ float findFeature(const int color, const PtrStepi& colors, const PtrStepf& weights, const int x, const int y, const int nfeatures) 81 return weights(fy, x); 88 __device__ void normalizeHistogram(PtrStepf weights, const int x, const int y, const int nfeatures) 92 total += weights(fy, x); 97 weights(fy, x) /= total; 101 __device__ bool insertFeature(const int color, const float weight, PtrStepi colors, PtrStepf weights, const int x, const int y, int& nfeatures) 109 weights(fy, x) += weight; 123 const float w = weights(fy, x); 132 weights(idx, x) = weight; 138 weights(nfeature [all...] |
/external/freetype/include/ |
H A D | ftlcdfil.h | 73 * weights (as given by FT_LCD_FILTER_DEFAULT) are no longer optimal, as 75 * gamma correction. To preserve color neutrality, weights for a FIR5 77 * and the FIR weights should be 83 * This formula generates equal weights for all the color primaries 85 * set of weights is 91 * where `a' has value 0x30 and `b' value 0x20. The weights in filter 209 * Use this function to override the filter weights selected by 219 * weights :: 221 * uses them to specify the filter weights. 241 unsigned char *weights ); [all...] |
/external/pdfium/third_party/freetype/include/freetype/ |
H A D | ftlcdfil.h | 73 * weights (as given by FT_LCD_FILTER_DEFAULT) are no longer optimal, as 75 * gamma correction. To preserve color neutrality, weights for a FIR5 77 * and the FIR weights should be 83 * This formula generates equal weights for all the color primaries 85 * set of weights is 91 * where `a' has value 0x30 and `b' value 0x20. The weights in filter 209 * Use this function to override the filter weights selected by 219 * weights :: 221 * uses them to specify the filter weights. 241 unsigned char *weights ); [all...] |
/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/chromium-trace/catapult/third_party/mapreduce/mapreduce/ |
H A D | property_range.py | 309 weights = _get_weights(_STRING_LENGTH) 310 start_ord = _str_to_ord(start, weights) 313 end_ord = _str_to_ord(end, weights) 321 splitpoints = [_ord_to_str(start_ord, weights)] 328 splitpoints.append(_ord_to_str(point, weights)) 329 end_str = _ord_to_str(end_ord, weights) 341 """Get weights for each offset in str of certain max length. 347 A list of ints as weights. 353 weights = [1] 355 weights [all...] |
/external/opencv3/modules/ml/src/ |
H A D | ann_mlp.cpp | 101 weights.clear(); 181 double* w = weights[i].ptr<double>(); 183 // initialize weights using Nguyen-Widrow algorithm 217 weights.resize(l_count + 2); 231 weights[i].create(layer_sizes[i-1]+1, n, CV_64F); 236 weights[0].create(1, ninputs*2, CV_64F); 237 weights[l_count].create(1, noutputs*2, CV_64F); 238 weights[l_count+1].create(1, noutputs*2, CV_64F); 296 Mat w = weights[j].rowRange(0, layer_in.cols); 298 calc_activ_func( layer_out, weights[ 1303 vector<Mat> weights; member in class:cv::ml::ANN_MLPImpl [all...] |
H A D | em.cpp | 101 weights.release(); 237 const std::vector<Mat>* covs, const Mat* weights) 261 CV_Assert(!weights || 262 (!weights->empty() && 263 (weights->cols == 1 || weights->rows == 1) && static_cast<int>(weights->total()) == nclusters && 264 (weights->type() == CV_32FC1 || weights->type() == CV_64FC1))); 341 // set weights 235 checkTrainData(int startStep, const Mat& samples, int nclusters, int covMatType, const Mat* probs, const Mat* means, const std::vector<Mat>* covs, const Mat* weights) argument 775 fs << "weights" << weights; local 802 fn["weights"] >> weights; local 831 Mat weights; member in class:cv::ml::EMImpl [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/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/opencv/ml/src/ |
H A D | mlcnn.cpp | 297 // 3) Update weights by the gradient descent 635 CvMat* connect_mask, CvMat* weights ) 656 CV_CALL(layer->weights = cvCreateMat( n_output_planes, K*K+1, CV_32FC1 )); 659 if( weights ) 661 if( !ICV_IS_MAT_OF_TYPE( weights, CV_32FC1 ) ) 662 CV_ERROR( CV_StsBadSize, "Type of initial weights matrix must be CV_32FC1" ); 663 if( !CV_ARE_SIZES_EQ( weights, layer->weights ) ) 664 CV_ERROR( CV_StsBadSize, "Invalid size of initial weights matrix" ); 665 CV_CALL(cvCopy( weights, laye [all...] |
/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/icu/android_icu4j/src/main/tests/android/icu/dev/util/ |
H A D | Pick.java | 225 result += items[i].getInternal(depth+1, alreadySeen) + "/" + weightedIndex.weights[i]; 233 for (int i = 0; i < weightedIndex.weights.length; ++i) { 287 for (int i = 0; i < weightedIndex.weights.length; ++i) { 340 // give weights to the above. make sure we delete about the same as we insert 614 * Item weights may be zero, but cannot be negative. 619 private int[] weights = new int[0]; field in class:Pick.WeightedIndex 643 int oldLen = weights.length; 645 weights = (int[]) realloc(weights, weights [all...] |
/external/icu/icu4j/main/tests/framework/src/com/ibm/icu/dev/util/ |
H A D | Pick.java | 224 result += items[i].getInternal(depth+1, alreadySeen) + "/" + weightedIndex.weights[i]; 232 for (int i = 0; i < weightedIndex.weights.length; ++i) { 286 for (int i = 0; i < weightedIndex.weights.length; ++i) { 339 // give weights to the above. make sure we delete about the same as we insert 613 * Item weights may be zero, but cannot be negative. 618 private int[] weights = new int[0]; field in class:Pick.WeightedIndex 642 int oldLen = weights.length; 644 weights = (int[]) realloc(weights, weights [all...] |