| /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);
|
| 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/libtextclassifier/tests/ |
| embedding-network_test.cc | 38 MatrixParams* weights, MatrixParams* bias) {
39 weights->set_rows(3);
40 weights->set_cols(3);
41 weights->add_value(diagonal_value);
42 weights->add_value(0);
43 weights->add_value(0);
44 weights->add_value(0);
45 weights->add_value(diagonal_value);
46 weights->add_value(0);
47 weights->add_value(0)
[all...] |
| /frameworks/ml/nn/runtime/test/specs/ |
| fully_connected_float_large_weights_as_inputs.mod.py | 19 weights = Input("op2", "TENSOR_FLOAT32", "{1, 5}") # num_units = 1, input_size = 5 variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
28 weights:
|
| fully_connected_float_weights_as_inputs.mod.py | 19 weights = Input("op2", "TENSOR_FLOAT32", "{1, 1}") variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
28 weights: [2],
|
| fully_connected_quant8_large_weights_as_inputs.mod.py | 19 weights = Input("op2", "TENSOR_QUANT8_ASYMM", "{1, 5}, 0.2, 0") # num_units = 1, input_size = 5 variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
28 weights:
|
| fully_connected_quant8_weights_as_inputs.mod.py | 19 weights = Input("op2", "TENSOR_QUANT8_ASYMM", "{1, 1}, 0.5f, 0") variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
28 weights: [2],
|
| fully_connected_float.mod.py | 19 weights = Parameter("op2", "TENSOR_FLOAT32", "{1, 1}", [2]) variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
|
| fully_connected_float_large.mod.py | 19 weights = Parameter("op2", "TENSOR_FLOAT32", "{1, 5}", [2, 3, 4, 5, 6]) # num_units = 1, input_size = 5 variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
|
| fully_connected_quant8.mod.py | 19 weights = Parameter("op2", "TENSOR_QUANT8_ASYMM", "{1, 1}, 0.5f, 0", [2]) variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
|
| fully_connected_quant8_large.mod.py | 19 weights = Parameter("op2", "TENSOR_QUANT8_ASYMM", "{1, 5}, 0.2, 0", [10, 20, 20, 20, 10]) # num_units = 1, input_size = 5 variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
|
| rnn_state.mod.py | 24 weights = Input("weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size)) variable
34 model = model.Operation("RNN", input, weights, recurrent_weights, bias, hidden_state_in,
38 weights: [
|
| /external/libopus/src/ |
| mlp.h | 36 const float *weights; member in struct:__anon24172
|
| /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,
|
| /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...] |
| /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/freetype/include/freetype/ |
| ftlcdfil.h | 154 * weights are [0x08 0x4D 0x56 0x4D 0x08].
164 * onto surfaces. The light filter weights are
250 * This function can be used to enable LCD filter with custom weights,
257 * weights ::
259 * uses them to specify the filter weights.
276 unsigned char *weights );
|
| /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...] |
| /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...] |
