| /cts/tests/openglperf2/assets/fragment/ |
| blur | 19 float weights[11];
20 weights[0] = 0.047748641153356156;
21 weights[1] = 0.05979670798364139;
22 weights[2] = 0.07123260215138659;
23 weights[3] = 0.08071711293576822;
24 weights[4] = 0.08700369673862933;
25 weights[5] = 0.08920620580763855;
26 weights[6] = 0.08700369673862933;
27 weights[7] = 0.08071711293576822;
28 weights[8] = 0.07123260215138659
[all...] |
| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/ |
| WeightedEvaluation.java | 29 * using the supplied weights.
32 * @param weights array of weights
35 double evaluate(double[] values, double[] weights);
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);
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| 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,
[all...] |
| /external/libopus/src/ |
| mlp.h | 36 const float *weights; member in struct:__anon16733
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| /frameworks/base/libs/hwui/utils/ |
| Blur.h | 37 static void generateGaussianWeights(float* weights, float radius);
38 static void horizontal(float* weights, int32_t radius, const uint8_t* source,
40 static void vertical(float* weights, int32_t radius, const uint8_t* source,
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| Blur.cpp | 61 void Blur::generateGaussianWeights(float* weights, float radius) {
64 // Compute gaussian weights for the blur
83 weights[r + intRadius] = coeff1 * pow(e, floatR * floatR * coeff2);
84 normalizeFactor += weights[r + intRadius];
87 //Now we need to normalize the weights because all our coefficients need to add up to one
90 weights[r + intRadius] *= normalizeFactor;
94 void Blur::horizontal(float* weights, int32_t radius,
106 const float* gPtr = weights;
138 void Blur::vertical(float* weights, int32_t radius,
148 const float* gPtr = weights;
[all...] |
| /external/opencv3/modules/java/src/ |
| 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)
29 Mat weights_mat = weights;
35 //javadoc: groupRectangles(rectList, weights, groupThreshold)
36 public static void groupRectangles(MatOfRect rectList, MatOfInt weights, int groupThreshold)
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/ |
| 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[0] * val
[all...] |
| /external/pdfium/third_party/freetype/src/base/ |
| 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[0] * val
[all...] |
| /external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/ |
| 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.
92 * Weights can be used for example to combine residuals with different standard
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 residual
[all...] |
| DifferentiableMultivariateVectorialOptimizer.java | 101 * @param weights weight for the least squares cost computation
110 double[] target, double[] weights,
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| /external/clang/test/Profile/ |
| 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/ |
| 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(nfeatures * c_height + y, x) = weight
[all...] |
| /external/freetype/include/ |
| 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/ |
| 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...] |
| /prebuilts/misc/darwin-x86_64/freetype/include/freetype2/ |
| 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/ |
| 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,
203 if (test(values, weights, begin, length))
[all...] |
| 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 array does not have the same length as the values array</li
[all...] |
| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/summary/ |
| 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,
[all...] |
| 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,
161 if (test(values, weights, begin, length))
[all...] |
| /external/opencv3/modules/ml/src/ |
| 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[j] )
1303 vector<Mat> weights; member in class:cv::ml::ANN_MLPImpl
[all...] |
| /external/apache-commons-math/src/main/java/org/apache/commons/math/analysis/interpolation/ |
| 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)
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
[all...] |
| /hardware/intel/common/utils/ituxd/src/com/intel/thermal/ |
| VirtualThermalZone.java | 89 Integer weights[] = sa.getWeights(); local
90 int m = weights[0];
213 Integer weights[], order[]; local
221 weights = sa.getWeights();
223 if (weights == null && order == null) return rawSensorTemp;
224 if (weights != null) {
227 return (weights[0] * rawSensorTemp) / 1000;
228 } else if (order != null && weights.length == order.length) {
230 // it should be of same size as weights array
232 for (int i = 0; i < weights.length; i++)
[all...] |
| /external/apache-commons-math/src/main/java/org/apache/commons/math/analysis/integration/ |
| LegendreGaussIntegrator.java | 45 * Legendre polynomial. The weights a<sub>i</sub> of the quadrature formula
62 /** Weights for the 2 points method. */
75 /** Weights for the 3 points method. */
90 /** Weights for the 4 points method. */
107 /** Weights for the 5 points method. */
119 /** Weights for the current method. */
120 private final double[] weights; field in class:LegendreGaussIntegrator
135 weights = WEIGHTS_2;
139 weights = WEIGHTS_3;
143 weights = WEIGHTS_4
[all...] |
| /frameworks/ml/bordeaux/learning/stochastic_linear_ranker/java/android/bordeaux/learning/ |
| StochasticLinearRanker.java | 42 public HashMap<String, Float> weights = new HashMap<String, Float>(); field in class:StochasticLinearRanker.Model
95 slrModel.weights.put(wKeys[i], wValues[i]);
109 String[] wKeys = new String[model.weights.size()];
110 float[] wValues = new float[model.weights.size()];
112 for (Map.Entry<String, Float> e : model.weights.entrySet()){
144 for (Map.Entry<String, Float> e : model.weights.entrySet())
148 Log.i(TAG, "Weights are " + Sw);
|
