| /external/jemalloc/test/ |
| test.sh.in | 45 total_count=`expr ${pass_count} + ${skip_count} + ${fail_count}`
47 echo "Test suite summary: pass: ${pass_count}/${total_count}, skip: ${skip_count}/${total_count}, fail: ${fail_count}/${total_count}"
|
| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/ |
| negative_binomial_test.py | 39 total_count = [2.0] * 5
41 total_count=total_count, probs=probs)
51 total_count = [[2.]] * 5
53 total_count=total_count, probs=probs)
65 total_count=3., logits=logits)
90 total_count=invalid_rs, probs=0.1, validate_args=True)
91 negbinom.total_count.eval()
98 total_count = 5
[all...] |
| binomial_test.py | 33 binom = binomial.Binomial(total_count=1., probs=p)
43 binom = binomial.Binomial(total_count=n, probs=p)
54 binom = binomial.Binomial(total_count=n, probs=p)
55 self.assertEqual((2, 1), binom.total_count.get_shape())
56 self.assertAllClose(n, binom.total_count.eval())
61 binom = binomial.Binomial(total_count=3., probs=p)
69 binom = binomial.Binomial(total_count=3., logits=logits)
78 binom = binomial.Binomial(total_count=n, probs=p, validate_args=True)
97 binom = binomial.Binomial(total_count=n, probs=p, validate_args=True)
111 binom = binomial.Binomial(total_count=n, probs=p, validate_args=False
[all...] |
| /external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
| binomial.py | 34 after sampling `self.total_count` draws from this Binomial distribution, the
39 can be broadcast with `self.probs` and `self.total_count`. `value` is only legal
40 if it is less than or equal to `self.total_count` and its components are equal
71 drawing a `1` and `total_count`, the number of trials per draw from the
76 The Binomial is a distribution over the number of `1`'s in `total_count`
88 * `total_count = n`,
98 dist = Binomial(total_count=5., probs=.5)
104 dist = Binomial(total_count=5., logits=0.)
113 dist = Binomial(total_count=4., probs=p)
134 total_count,
188 def total_count(self): member in class:Binomial
[all...] |
| negative_binomial.py | 48 * `total_count = f`,
55 total_count,
64 total_count: Non-negative floating-point `Tensor` with shape
68 the number of negative Bernoulli trials to stop at (the `total_count`
70 `total_count` is a non-integer.
94 with ops.name_scope(name, values=[total_count, logits, probs]):
98 [check_ops.assert_positive(total_count)] if validate_args else []):
99 self._total_count = array_ops.identity(total_count)
111 def total_count(self): member in class:NegativeBinomial
127 array_ops.shape(self.total_count),
[all...] |
| /external/tensorflow/tensorflow/python/ops/distributions/ |
| dirichlet_multinomial.py | 41 sampling `self.total_count` draws from this Dirichlet-Multinomial distribution,
49 `tf.reduce_sum(value, -1) = self.total_count`. Its shape must be broadcastable
50 with `self.concentration` and `self.total_count`."""
58 length-`K` `concentration` vectors (`K > 1`) and a `total_count` number of
61 `tf.reduce_sum(counts, -1) = total_count`. The Dirichlet-Multinomial is
79 * `total_count = N`, `N` a positive integer,
95 `counts = [n_0,...,n_{K-1}] ~ Multinomial(total_count, probs)`
99 `concentration`, `total_count` and `counts` are broadcast to the same shape.
167 total_count,
175 total_count: Non-negative floating point tensor, whose dtype is the sam
225 def total_count(self): member in class:DirichletMultinomial
[all...] |
| multinomial.py | 41 ,n_{k-1}]`, `P[value]` is the probability that after sampling `self.total_count`
50 `tf.reduce_sum(value, -1) = self.total_count`. Its shape must be broadcastable
51 with `self.probs` and `self.total_count`."""
60 `tf.reduce_sum(probs, -1) = 1`, and a `total_count` number of trials, i.e.,
63 `tf.reduce_sum(counts, -1) = total_count`. The Multinomial is identically the
80 * `total_count = N`, `N` a positive integer,
112 dist = Multinomial(total_count=4., logits=logits)
119 dist = Multinomial(total_count=4., probs=p)
142 dist = Multinomial(total_count=[4., 5], probs=p)
152 total_count,
211 def total_count(self): member in class:Multinomial
[all...] |
| /external/tensorflow/tensorflow/python/kernel_tests/distributions/ |
| multinomial_test.py | 37 dist = multinomial.Multinomial(total_count=1., probs=p)
47 dist = multinomial.Multinomial(total_count=n, probs=p)
57 dist = multinomial.Multinomial(total_count=n, probs=p)
58 self.assertEqual((2, 1), dist.total_count.get_shape())
59 self.assertAllClose(n, dist.total_count.eval())
64 dist = multinomial.Multinomial(total_count=3., probs=p)
73 multinom = multinomial.Multinomial(total_count=3., logits=logits)
82 dist = multinomial.Multinomial(total_count=1., logits=logits)
90 dist = multinomial.Multinomial(total_count=n, probs=p, validate_args=True)
95 with self.assertRaisesOpError("counts must sum to `self.total_count`")
[all...] |
| /external/tensorflow/tensorflow/core/kernels/ |
| population_count_op_gpu.cu.cc | 72 int64 total_count = input.size(); \
73 CudaLaunchConfig config = GetCudaLaunchConfig(total_count, d); \
76 total_count, input.data(), output.data()); \
|
| depthtospace_op_gpu.cu.cc | 159 const int total_count = local
161 CudaLaunchConfig config = GetCudaLaunchConfig(total_count, d);
190 const int total_count = batch_size * output_depth_by_input_area; local
191 CudaLaunchConfig config = GetCudaLaunchConfig(total_count, d);
196 total_count, input.data(), input_width, output_width,
202 total_count, input.data(), input_width, output_width,
208 total_count, input.data(), input_width, output_width,
215 const int total_count = batch_size * input_depth_by_input_area; local
216 auto config = GetCudaLaunchConfig(total_count, d);
|
| resize_bilinear_op_gpu.cu.cc | 163 const int total_count = batch * out_height * out_width * channels; local
164 if (total_count == 0) return;
166 CudaLaunchConfig config = GetCudaLaunchConfig(total_count, d);
190 int total_count; local
194 total_count = batch * original_height * original_width * channels;
195 if (total_count == 0) return;
196 config = GetCudaLaunchConfig(total_count, d);
201 total_count = batch * resized_height * resized_width * channels;
202 config = GetCudaLaunchConfig(total_count, d);
|
| spacetodepth_op_gpu.cu.cc | 155 const int total_count = local
157 CudaLaunchConfig config = GetCudaLaunchConfig(total_count, d);
186 const int total_count = batch_size * input_depth_by_output_area; local
187 CudaLaunchConfig config = GetCudaLaunchConfig(total_count, d);
192 total_count, input.data(), output_width, input_width,
198 total_count, input.data(), output_width, input_width,
204 total_count, input.data(), output_width, input_width,
211 const int total_count = batch_size * output_depth_by_output_area; local
212 CudaLaunchConfig config = GetCudaLaunchConfig(total_count, d);
|
| bias_op_gpu.cu.cc | 74 const int32 total_count = batch * bias_size * image_size; local
75 if (total_count == 0) {
78 CudaLaunchConfig config = GetCudaLaunchConfig(total_count, d);
156 int32 total_count = batch * image_size; local
159 index < total_count; index += blockDim.x * group_size) {
194 const int32 total_count = batch * bias_size * image_size; local
195 if (total_count == 0) {
199 CudaLaunchConfig config = GetCudaLaunchConfig(total_count, d);
211 d.stream()>>>(total_count, output_backprop, bias_backprop,
232 total_count, output_backprop, bias_backprop, bias_size)
[all...] |
| dilation_ops_gpu.cu.cc | 196 const int total_count = batch * output_rows * output_cols * depth; local
197 CudaLaunchConfig config = GetCudaLaunchConfig(total_count, d);
227 int total_count; local
231 total_count = batch * input_rows * input_cols * depth;
232 config = GetCudaLaunchConfig(total_count, d);
234 total_count, in_backprop.data());
237 total_count = batch * output_rows * output_cols * depth;
238 config = GetCudaLaunchConfig(total_count, d);
267 int total_count; local
271 total_count = filter_rows * filter_cols * depth
[all...] |
| crop_and_resize_op_gpu.cu.cc | 338 const int total_count = num_boxes * crop_height * crop_width * depth; local
341 if (total_count > 0) {
342 CudaLaunchConfig config = GetCudaLaunchConfig(total_count, d);
369 int total_count; local
373 total_count = batch * image_height * image_width * depth;
374 if (total_count > 0) {
375 config = GetCudaLaunchConfig(total_count, d);
381 total_count = num_boxes * crop_height * crop_width * depth;
382 if (total_count > 0) {
383 config = GetCudaLaunchConfig(total_count, d)
411 int total_count; local
[all...] |
| spacetobatch_functor_gpu.cu.cc | 133 int64 total_count = 1; local
136 total_count *= args.batch_tensor_shape[dim];
138 if (total_count > std::numeric_limits<int32>::max()) {
143 GetCudaLaunchConfig(static_cast<int32>(total_count), d);
|
| /prebuilts/gdb/darwin-x86/share/gdb/python/gdb/command/ |
| pretty_printers.py | 191 total_count = 0
194 total_count += t_total
197 total_count += t_total
201 total_count += t_total
202 return (enabled_count, total_count)
217 (enabled_count, total_count) = count_all_enabled_printers()
218 print ("%d of %d printers enabled" % (enabled_count, total_count))
|
| /prebuilts/gdb/linux-x86/share/gdb/python/gdb/command/ |
| pretty_printers.py | 191 total_count = 0
194 total_count += t_total
197 total_count += t_total
201 total_count += t_total
202 return (enabled_count, total_count)
217 (enabled_count, total_count) = count_all_enabled_printers()
218 print ("%d of %d printers enabled" % (enabled_count, total_count))
|
| /external/vulkan-validation-layers/loader/ |
| extension_manual.c | 939 uint32_t total_count = 0; local
1120 uint32_t total_count = 0; local
[all...] |
| /external/tensorflow/tensorflow/contrib/lite/examples/ios/camera/ |
| CameraExampleViewController.h | 41 int total_count; variable
|
| /frameworks/base/tests/JankBench/scripts/ |
| collect.py | 46 self.total_count = 0
76 total_count = row[4]
80 scoremap[run_id][name][iteration].total_count = long(total_count)
137 pj = round_to_2(100 * res.jank_count / float(res.total_count))
139 score = 100 * len(res.durations) / float(res.total_count)
|
| itr_collect.py | 24 self.total_count = 0
50 total_count = row[3]
54 scoremap[run_id][name].total_count = long(total_count)
96 pj = 100 * res.jank_count / float(res.total_count)
|
| /external/libchrome/base/metrics/ |
| sparse_histogram.cc | 231 Count total_count = snapshot->TotalCount(); local
232 double scaled_total_count = total_count / 100.0;
234 WriteAsciiHeader(total_count, output);
279 void SparseHistogram::WriteAsciiHeader(const Count total_count,
284 total_count);
|
| /external/autotest/client/site_tests/kernel_LTP/ |
| parse_ltp_out.py | 115 total_count = pass_count + notpass_count
116 if total_count:
117 score = float(pass_count) / float(total_count) * 100.0
|
| /external/autotest/client/tests/ltp/ |
| parse_ltp_out.py | 120 total_count = pass_count + notpass_count
121 if total_count:
122 score = float(pass_count) / float(total_count) * 100.0
|
