| /cts/suite/cts/deviceTests/opengl/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...] |
| /frameworks/base/libs/hwui/utils/ |
| Blur.h | 27 static void generateGaussianWeights(float* weights, int32_t radius);
28 static void horizontal(float* weights, int32_t radius, const uint8_t* source,
30 static void vertical(float* weights, int32_t radius, const uint8_t* source,
|
| Blur.cpp | 26 void Blur::generateGaussianWeights(float* weights, int32_t radius) {
27 // Compute gaussian weights for the blur
52 weights[r + radius] = coeff1 * pow(e, floatR * floatR * coeff2);
53 normalizeFactor += weights[r + radius];
56 //Now we need to normalize the weights because all our coefficients need to add up to one
59 weights[r + radius] *= normalizeFactor;
63 void Blur::horizontal(float* weights, int32_t radius,
75 const float* gPtr = weights;
107 void Blur::vertical(float* weights, int32_t radius,
117 const float* gPtr = weights;
[all...] |
| /external/chromium_org/third_party/opus/src/src/ |
| mlp.h | 36 const float *weights; member in struct:__anon13818
|
| mlp_train.c | 58 net->weights = malloc((nbLayers-1)*sizeof(net->weights));
59 net->best_weights = malloc((nbLayers-1)*sizeof(net->weights));
62 net->weights[i] = malloc((topo[i]+1)*topo[i+1]*sizeof(net->weights[0][0]));
63 net->best_weights[i] = malloc((topo[i]+1)*topo[i+1]*sizeof(net->weights[0][0]));
83 net->weights[0][k*(topo[0]+1)+j+1] = randn(std);
90 sum += inMean[k]*net->weights[0][j*(topo[0]+1)+k+1];
91 net->weights[0][j*(topo[0]+1)] = -sum;
101 net->weights[nbLayers-2][j*(topo[nbLayers-2]+1)] = mean
[all...] |
| /external/chromium_org/third_party/freetype/src/base/ |
| ftlcdfil.c | 38 FT_Byte* weights = library->lcd_weights; local
56 fir[0] = weights[2] * val1;
57 fir[1] = weights[3] * val1;
58 fir[2] = weights[4] * val1;
63 fir[0] += weights[1] * val1;
64 fir[1] += weights[2] * val1;
65 fir[2] += weights[3] * val1;
66 fir[3] += weights[4] * val1;
74 pix = fir[0] + weights[0] * val;
75 fir[0] = fir[1] + weights[1] * val
[all...] |
| /external/freetype/src/base/ |
| ftlcdfil.c | 38 FT_Byte* weights = library->lcd_weights; local
56 fir[0] = weights[2] * val1;
57 fir[1] = weights[3] * val1;
58 fir[2] = weights[4] * val1;
63 fir[0] += weights[1] * val1;
64 fir[1] += weights[2] * val1;
65 fir[2] += weights[3] * val1;
66 fir[3] += weights[4] * val1;
74 pix = fir[0] + weights[0] * val;
75 fir[0] = fir[1] + weights[1] * val
[all...] |
| /external/chromium_org/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...] |
| /external/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...] |
| /external/openfst/src/include/fst/script/ |
| 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...] |
| 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, sdopts)
[all...] |
| /external/jmonkeyengine/engine/src/core/com/jme3/animation/ |
| PoseTrack.java | 56 float[] weights; field in class:PoseTrack.PoseFrame
58 public PoseFrame(Pose[] poses, float[] weights) {
60 this.weights = weights;
71 result.weights = this.weights.clone();
87 out.write(weights, "weights", null);
93 weights = in.readFloatArray("weights", null)
[all...] |
| /external/llvm/include/llvm/Analysis/ |
| ProfileDataLoader.h | 78 EdgeWeights weights = EdgeInformation.find(f)->second; local
80 assert((weights.find(e) != weights.end())
82 return weights.find(e)->second;
|
| /external/jmonkeyengine/engine/src/test/jme3test/model/anim/ |
| TestCustomAnim.java | 77 FloatBuffer weights = FloatBuffer.allocate( box.getVertexCount() * 4 ); local
79 weightsBuf.setupData(Usage.CpuOnly, 4, Format.Float, weights);
106 weights.array()[i+0] = 1;
107 weights.array()[i+1] = 0;
108 weights.array()[i+2] = 0;
109 weights.array()[i+3] = 0;
112 // Maximum number of weights per bone is 1
|
| /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);
|
| /external/opencv/ml/src/ |
| 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, layer->weights ))
[all...] |
| mlboost.cpp | 185 const double* weights = ensemble->get_subtree_weights()->data.db; local
200 double w = weights[i];
221 double w = weights[idx];
229 double w = weights[idx];
249 const double* weights = ensemble->get_subtree_weights()->data.db; local
252 const double* rcw0 = weights + n;
263 double w = weights[idx];
278 double w = weights[idx], w2 = w*w;
303 double w = weights[idx];
341 const double* weights = ensemble->get_subtree_weights()->data.db local
445 const double* weights = ensemble->get_subtree_weights()->data.db; local
493 const double* weights = ensemble->get_subtree_weights()->data.db; local
575 const double* weights = ensemble->get_subtree_weights()->data.db; local
641 const double* weights = ensemble->get_subtree_weights()->data.db; local
702 const double* weights = ensemble->get_weights()->data.db; local
[all...] |
| mlem.cpp | 74 means = weights = probs = inv_eigen_values = log_weight_div_det = 0;
81 means = weights = probs = inv_eigen_values = log_weight_div_det = 0;
99 cvReleaseMat( &weights );
156 const CvMat* p = params.weights;
178 if( params.weights )
180 const CvMat* w = params.weights;
186 CV_ERROR( CV_StsBadArg, "The array of weights must be a valid "
344 CV_CALL( weights = cvCreateMat( 1, nclusters, CV_64FC1 ));
429 if( params.weights && params.covs )
432 cvReshape( weights, weights, 1, params.weights->rows )
[all...] |
| mlann_mlp.cpp | 97 weights = 0;
110 weights = 0;
127 cvFree( &weights );
185 double* w = weights[i];
187 // initialize weights using Nguyen-Widrow algorithm
254 CV_CALL( weights = (double**)cvAlloc( (l_count+1)*sizeof(weights[0]) ));
256 weights[0] = wbuf->data.db;
257 weights[1] = weights[0] + l_dst[0]*2
[all...] |
| /development/samples/ApiDemos/src/com/example/android/apis/graphics/ |
| SensorTest.java | 44 public RunAve(float[] weights) {
45 mWeights = weights;
48 for (int i = 0; i < weights.length; i++) {
49 sum += weights[i];
53 mDepth = weights.length;
|
| /external/eigen/unsupported/Eigen/src/MatrixFunctions/ |
| MatrixLogarithm.h | 247 const RealScalar weights[] = { 0.2777777777777777777777777777777778L, 0.4444444444444444444444444444444444L, local
253 result += weights[k] * (MatrixType::Identity(T.rows(), T.rows()) + nodes[k] * TminusI)
263 const RealScalar weights[] = { 0.1739274225687269286865319746109997L, 0.3260725774312730713134680253890003L, local
269 result += weights[k] * (MatrixType::Identity(T.rows(), T.rows()) + nodes[k] * TminusI)
280 const RealScalar weights[] = { 0.1184634425280945437571320203599587L, 0.2393143352496832340206457574178191L, local
287 result += weights[k] * (MatrixType::Identity(T.rows(), T.rows()) + nodes[k] * TminusI)
298 const RealScalar weights[] = { 0.0856622461895851725201480710863665L, 0.1803807865240693037849167569188581L, local
305 result += weights[k] * (MatrixType::Identity(T.rows(), T.rows()) + nodes[k] * TminusI)
317 const RealScalar weights[] = { 0.0647424830844348466353057163395410L, 0.1398526957446383339507338857118898L, local
325 result += weights[k] * (MatrixType::Identity(T.rows(), T.rows()) + nodes[k] * TminusI
337 const RealScalar weights[] = { 0.0506142681451881295762656771549811L, 0.1111905172266872352721779972131204L, local
358 const RealScalar weights[] = { 0.0406371941807872059859460790552618L, 0.0903240803474287020292360156214564L, local
380 const RealScalar weights[] = { 0.0333356721543440687967844049466659L, 0.0747256745752902965728881698288487L, local
403 const RealScalar weights[] = { 0.0278342835580868332413768602212743L, 0.0627901847324523123173471496119701L, local
[all...] |
| /frameworks/base/media/mca/filterpacks/java/android/filterpacks/imageproc/ |
| SaturateFilter.java | 48 "uniform vec3 weights;\n" +
52 " float kv = dot(color.rgb, weights) + shift;\n" +
60 "uniform vec3 weights;\n" +
65 " float de = dot(color.rgb, weights);\n" +
145 float weights[] = { 2f/8f, 5f/8f, 1f/8f}; local
147 mBenProgram.setHostValue("weights", weights);
150 mHerfProgram.setHostValue("weights", weights);
|
| SepiaFilter.java | 106 float weights[] = { 805.0f / 2048.0f, 715.0f / 2048.0f, 557.0f / 2048.0f, local
109 mProgram.setHostValue("matrix", weights);
|
| /external/srec/srec/include/ |
| swimodel.h | 39 const wtdata *weights; /*pointer to weights*/ member in struct:__anon26539
|
| /external/jmonkeyengine/engine/src/core/com/jme3/math/ |
| 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...] |
