Searched refs:weights (Results 1 - 25 of 99) sorted by relevance

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/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/
H A DWeightedEvaluation.java29 * 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
H A DAbstractUnivariateStatistic.java165 * 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
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/external/libopus/src/
H A Dmlp.h36 const float *weights; member in struct:__anon10617
/external/opencv3/modules/java/src/
H A Dobjdetect+Objdetect.java22 // 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)
/external/freetype/src/base/
H A Dftlcdfil.c38 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[
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/external/pdfium/third_party/freetype/src/base/
H A Dftlcdfil.c38 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[
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/external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/
H A DLeastSquaresConverter.java49 * 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
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H A DDifferentiableMultivariateVectorialOptimizer.java101 * @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
/external/clang/test/Profile/
H A Dc-general.c124 // 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
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/external/opencv3/modules/cudalegacy/src/cuda/
H A Dgmg.cu76 __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
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/external/freetype/include/
H A Dftlcdfil.h73 * 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 );
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/external/pdfium/third_party/freetype/include/freetype/
H A Dftlcdfil.h73 * 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 );
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/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/moment/
H A DMean.java180 * 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
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H A DVariance.java266 * &Sigma;(weights[i]*(values[i] - weightedMean)<sup>2</sup>)/(&Sigma;(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
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/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/summary/
H A DProduct.java139 * <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 = &prod;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
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H A DSum.java138 * <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 = &Sigma;(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
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/external/chromium-trace/catapult/third_party/mapreduce/mapreduce/
H A Dproperty_range.py309 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
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/external/opencv3/modules/ml/src/
H A Dann_mlp.cpp101 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
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H A Dem.cpp101 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
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/external/apache-commons-math/src/main/java/org/apache/commons/math/analysis/interpolation/
H A DLoessInterpolator.java189 * @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
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/external/apache-commons-math/src/main/java/org/apache/commons/math/analysis/integration/
H A DLegendreGaussIntegrator.java45 * 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]);
/external/opencv/ml/src/
H A Dmlcnn.cpp297 // 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
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/external/eigen/unsupported/Eigen/src/MatrixFunctions/
H A DMatrixLogarithm.h238 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
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/external/icu/android_icu4j/src/main/tests/android/icu/dev/util/
H A DPick.java225 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
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/external/icu/icu4j/main/tests/framework/src/com/ibm/icu/dev/util/
H A DPick.java224 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
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