| /external/guava/guava/src/com/google/common/collect/ |
| RegularImmutableSortedMultiset.java | 33 private final transient int[] counts; field in class:RegularImmutableSortedMultiset
40 int[] counts,
45 this.counts = counts;
55 counts[offset + index]);
71 return (index == -1) ? 0 : counts[index + offset];
106 subElementSet, counts, cumulativeCounts, offset + from, to - from);
112 return offset > 0 || length < counts.length;
|
| /external/tensorflow/tensorflow/python/ops/distributions/ |
| multinomial.py | 40 _multinomial_sample_note = """For each batch of counts, `value = [n_0, ...
45 sequences have the same counts so the probability includes a combinatorial
62 (batch of) length-`K` vector `counts` such that
63 `tf.reduce_sum(counts, -1) = total_count`. The Multinomial is identically the
68 The Multinomial is a distribution over `K`-class counts, i.e., a length-`K`
69 vector of non-negative integer `counts = n = [n_0, ..., n_{K-1}]`.
122 The distribution functions can be evaluated on counts.
125 # counts same shape as p.
126 counts = [1., 0, 3]
127 dist.prob(counts) # Shape [
[all...] |
| dirichlet_multinomial.py | 39 _dirichlet_multinomial_sample_note = """For each batch of counts,
44 different sequences have the same counts so the probability includes a
60 is defined over a (batch of) length-`K` vector `counts` such that
61 `tf.reduce_sum(counts, -1) = total_count`. The Dirichlet-Multinomial is
66 The Dirichlet-Multinomial is a distribution over `K`-class counts, i.e., a
67 length-`K` vector of non-negative integer `counts = n = [n_0, ..., n_{K-1}]`.
95 `counts = [n_0,...,n_{K-1}] ~ Multinomial(total_count, probs)`
98 distribution. When calling distribution functions (e.g., `dist.prob(counts)`),
99 `concentration`, `total_count` and `counts` are broadcast to the same shape.
100 The last dimension of `counts` corresponds single Dirichlet-Multinomia
[all...] |
| /external/skia/tools/lua/ |
| ngrams.lua | 24 -- which generates counts for each n-gram.
49 local counts = {}
53 if counts[ngram] == nil then
54 counts[ngram] = 1
56 counts[ngram] = counts[ngram] + 1
62 for ngram, count in pairs(counts) do
63 io.write("if counts['", ngram, "'] == nil then counts['", ngram, "'] = ", count, " else counts['", ngram, "'] = counts['", ngram, "'] + ", count, " end\n"
[all...] |
| ngrams_aggregate.lua | 4 counts = {}
9 for ngram, count in pairs(counts) do
|
| /external/skqp/tools/lua/ |
| ngrams.lua | 24 -- which generates counts for each n-gram.
49 local counts = {}
53 if counts[ngram] == nil then
54 counts[ngram] = 1
56 counts[ngram] = counts[ngram] + 1
62 for ngram, count in pairs(counts) do
63 io.write("if counts['", ngram, "'] == nil then counts['", ngram, "'] = ", count, " else counts['", ngram, "'] = counts['", ngram, "'] + ", count, " end\n"
[all...] |
| ngrams_aggregate.lua | 4 counts = {}
9 for ngram, count in pairs(counts) do
|
| /external/libvpx/libvpx/ |
| rate_hist.h | 31 void show_q_histogram(const int counts[64], int max_buckets);
|
| /external/icu/android_icu4j/src/main/java/android/icu/impl/duration/ |
| BasicPeriodFormatter.java | 45 return format(period.timeLimit, period.inFuture, period.counts);
58 private String format(int tl, boolean inFuture, int[] counts) {
60 for (int i = 0; i < counts.length; ++i) {
61 if (counts[i] > 0) {
71 for (int i = 0, m = 1; i < counts.length; ++i, m <<= 1) {
72 if ((mask & m) != 0 && counts[i] == 1) {
95 counts[sx] += (counts[mx]-1)/1000;
104 counts[sx] = 1;
106 counts[sx] += (counts[mx]-1)/1000
[all...] |
| /external/icu/icu4j/main/classes/core/src/com/ibm/icu/impl/duration/ |
| BasicPeriodFormatter.java | 44 return format(period.timeLimit, period.inFuture, period.counts);
57 private String format(int tl, boolean inFuture, int[] counts) {
59 for (int i = 0; i < counts.length; ++i) {
60 if (counts[i] > 0) {
70 for (int i = 0, m = 1; i < counts.length; ++i, m <<= 1) {
71 if ((mask & m) != 0 && counts[i] == 1) {
94 counts[sx] += (counts[mx]-1)/1000;
103 counts[sx] = 1;
105 counts[sx] += (counts[mx]-1)/1000
[all...] |
| /external/libvpx/libvpx/vp9/common/ |
| vp9_entropymv.c | 141 void vp9_inc_mv(const MV *mv, nmv_context_counts *counts) {
142 if (counts != NULL) {
144 ++counts->joints[j];
147 inc_mv_component(mv->row, &counts->comps[0], 1, 1);
151 inc_mv_component(mv->col, &counts->comps[1], 1, 1);
161 const nmv_context_counts *counts = &cm->counts.mv; local
163 vpx_tree_merge_probs(vp9_mv_joint_tree, pre_fc->joints, counts->joints,
169 const nmv_component_counts *c = &counts->comps[i];
|
| vp9_entropymode.c | 341 const FRAME_COUNTS *counts = &cm->counts; local
345 counts->intra_inter[i]);
348 mode_mv_merge_probs(pre_fc->comp_inter_prob[i], counts->comp_inter[i]);
351 mode_mv_merge_probs(pre_fc->comp_ref_prob[i], counts->comp_ref[i]);
355 pre_fc->single_ref_prob[i][j], counts->single_ref[i][j]);
359 counts->inter_mode[i], fc->inter_mode_probs[i]);
363 counts->y_mode[i], fc->y_mode_prob[i]);
367 counts->uv_mode[i], fc->uv_mode_prob[i]);
371 counts->partition[i], fc->partition_prob[i])
[all...] |
| vp9_entropy.c | 1059 vp9_coeff_count_model *counts = cm->counts.coef[tx_size]; local
[all...] |
| /external/testng/src/main/java/org/testng/ |
| SuiteRunnerWorker.java | 65 SuiteResultCounts counts = new SuiteResultCounts(); local
67 counts.calculateResultCounts(xmlSuite, suiteRunnerMap);
73 .append(counts.m_total).append(", Failures: ").append(counts.m_failed)
74 .append(", Skips: ").append(counts.m_skipped);
75 if(counts.m_confFailures > 0 || counts.m_confSkips > 0) {
76 bufLog.append("\nConfiguration Failures: ").append(counts.m_confFailures)
77 .append(", Skips: ").append(counts.m_confSkips);
130 * Class to help calculate result counts for tests run as part of a suite an
[all...] |
| /external/tensorflow/tensorflow/python/kernel_tests/random/ |
| multinomial_op_big_test.py | 43 indices, counts = np.unique(x, return_counts=True)
44 for index, count in zip(indices, counts):
61 indices, counts = np.unique(x, return_counts=True)
62 for index, count in zip(indices, counts):
83 indices, counts = np.unique(x, return_counts=True)
84 for index, count in zip(indices, counts):
|
| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/ |
| binomial_test.py | 122 counts = 1.
124 pmf = binom.prob(counts)
125 cdf = binom.cdf(counts)
127 self.assertAllClose(stats.binom.cdf(counts, n=1, p=p), cdf.eval())
135 counts = 3.
137 pmf = binom.prob(counts)
138 cdf = binom.cdf(counts)
139 self.assertAllClose(stats.binom.pmf(counts, n=5., p=p), pmf.eval())
140 self.assertAllClose(stats.binom.cdf(counts, n=5., p=p), cdf.eval())
147 counts = [[1., 2.]
[all...] |
| /external/brotli/c/enc/ |
| entropy_encode.c | 65 especially when population counts are longer than 2**tree_limit, but
242 void BrotliOptimizeHuffmanCountsForRle(size_t length, uint32_t* counts,
252 if (counts[i]) {
259 while (length != 0 && counts[length - 1] == 0) {
265 /* Now counts[0..length - 1] does not have trailing zeros. */
270 if (counts[i] != 0) {
272 if (smallest_nonzero > counts[i]) {
273 smallest_nonzero = counts[i];
285 if (counts[i - 1] != 0 && counts[i] == 0 && counts[i + 1] != 0)
[all...] |
| /external/rappor/client/javatest/com/google/android/rappor/ |
| EncoderTest.java | 666 int counts[] = new int[64]; local
682 counts[iBit]++;
687 assertEquals(9843, counts[0]); // input = 1, expectation = 9843.75
688 assertEquals(173, counts[1]); // input = 0, expectation = 156.25
689 assertEquals(9839, counts[2]); // input = 1, expectation = 9843.75
690 assertEquals(9831, counts[3]); // input = 1, expectation = 9843.75
691 assertEquals(9848, counts[4]); // input = 1, expectation = 9843.75
692 assertEquals(9828, counts[5]); // input = 1, expectation = 9843.75
693 assertEquals(9834, counts[6]); // input = 1, expectation = 9843.75
694 assertEquals(9837, counts[7]); // input = 1, expectation = 9843.7
715 int counts[] = new int[64]; local
[all...] |
| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/inference/ |
| ChiSquareTestImpl.java | 58 * expected and observed counts are equal.</p>
60 * @param observed array of observed frequency counts
61 * @param expected array of expected frequency counts
107 * expected and observed counts are equal.</p>
109 * @param observed array of observed frequency counts
110 * @param expected array of expected frequency counts
126 * expected and observed counts are equal.</p>
128 * @param observed array of observed frequency counts
129 * @param expected array of expected frequency counts
147 * @param counts array representation of 2-way tabl
[all...] |
| /external/libvpx/libvpx/vp9/encoder/ |
| vp9_encodemv.h | 23 nmv_context_counts *const counts);
|
| /toolchain/binutils/binutils-2.27/gprof/ |
| bb_exit_func.c | 38 long *counts;
90 fwrite (&ptr->counts[i], sizeof (ptr->counts[0]), 1, fp);
37 long *counts; member in struct:bb
|
| /external/tensorflow/tensorflow/python/kernel_tests/distributions/ |
| dirichlet_multinomial_test.py | 107 counts = [1., 0]
109 pmf = dist.prob(counts)
119 counts = [3., 2]
121 pmf = dist.prob(counts)
130 counts = [3., 2]
133 pmf = dist.prob(counts)
142 counts = [[1., 0], [0., 1]]
144 pmf = dist.prob(counts)
153 counts = [[1., 0], [0., 1]]
154 pmf = ds.DirichletMultinomial(1., alpha).prob(counts)
[all...] |
| /external/tensorflow/tensorflow/core/lib/random/ |
| distribution_sampler_test.cc | 36 std::unique_ptr<float[]> counts(new float[weights.size()]);
37 memset(counts.get(), 0, sizeof(float) * weights.size());
45 counts[r] += 1.0;
49 counts[i] /= iters;
50 float err = (counts[i] - weights[i]);
|
| /external/skia/debugger/ |
| SkDebugger.cpp | 61 SkTDArray<int> counts; local
62 counts.setCount(SkDrawCommand::kOpTypeCount);
64 counts[i] = 0;
68 counts[commands[i]->getType()]++;
77 if (0 == counts[i]) {
85 overview->appendS32(counts[i]);
100 total += counts[i];
|
| /external/skqp/debugger/ |
| SkDebugger.cpp | 61 SkTDArray<int> counts; local
62 counts.setCount(SkDrawCommand::kOpTypeCount);
64 counts[i] = 0;
68 counts[commands[i]->getType()]++;
77 if (0 == counts[i]) {
85 overview->appendS32(counts[i]);
100 total += counts[i];
|
