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Results 41 - 50 of 130 for multiplication (0.42 sec)
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src/internal/types/testdata/check/const0.go
const _ = maxInt + /* ERROR "constant addition overflow" */ 1 const _ = -maxInt - /* ERROR "constant subtraction overflow" */ 1 const _ = maxInt ^ /* ERROR "constant bitwise XOR overflow" */ -1 const _ = maxInt * /* ERROR "constant multiplication overflow" */ 2 const _ = maxInt << /* ERROR "constant shift overflow" */ 2 const _ = 1 << /* ERROR "constant shift overflow" */ prec
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Tue Jan 17 19:54:25 UTC 2023 - 9.2K bytes - Viewed (0) -
src/math/expm1.go
// From step 1, we have // expm1(x) = either 2**k*[expm1(r)+1] - 1 // = or 2**k*[expm1(r) + (1-2**-k)] // 4. Implementation notes: // (A). To save one multiplication, we scale the coefficient Qi // to Qi*2**i, and replace z by (x**2)/2. // (B). To achieve maximum accuracy, we compute expm1(x) by // (i) if x < -56*ln2, return -1.0, (raise inexact if x!=inf)
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Thu Oct 19 11:59:09 UTC 2023 - 7.9K bytes - Viewed (0) -
guava/src/com/google/common/hash/BloomFilterStrategies.java
return true; } }, /** * This strategy uses all 128 bits of {@link Hashing#murmur3_128} when hashing. It looks different * from the implementation in MURMUR128_MITZ_32 because we're avoiding the multiplication in the * loop and doing a (much simpler) += hash2. We're also changing the index to a positive number by * AND'ing with Long.MAX_VALUE instead of flipping the bits. */ MURMUR128_MITZ_64() { @Override
Registered: Wed Jun 12 16:38:11 UTC 2024 - Last Modified: Mon Oct 10 19:45:10 UTC 2022 - 10.7K bytes - Viewed (0) -
guava-tests/test/com/google/common/util/concurrent/AbstractAbstractFutureTest.java
assertEquals(1, future.get(-1, SECONDS).intValue()); } @J2ktIncompatible @GwtIncompatible // threads public void testOverflowTimeout() throws Exception { // First, sanity check that naive multiplication would really overflow to a negative number: long nanosPerSecond = NANOSECONDS.convert(1, SECONDS); assertThat(nanosPerSecond * Long.MAX_VALUE).isLessThan(0L);
Registered: Wed Jun 12 16:38:11 UTC 2024 - Last Modified: Tue Feb 13 14:28:25 UTC 2024 - 15.5K bytes - Viewed (0) -
guava/src/com/google/common/math/PairedStatsAccumulator.java
double ySumOfSquaresOfDeltas = yStats.sumOfSquaresOfDeltas(); checkState(xSumOfSquaresOfDeltas > 0.0); checkState(ySumOfSquaresOfDeltas > 0.0); // The product of two positive numbers can be zero if the multiplication underflowed. We // force a positive value by effectively rounding up to MIN_VALUE. double productOfSumsOfSquaresOfDeltas = ensurePositive(xSumOfSquaresOfDeltas * ySumOfSquaresOfDeltas);
Registered: Wed Jun 12 16:38:11 UTC 2024 - Last Modified: Fri May 12 17:02:53 UTC 2023 - 10.3K bytes - Viewed (0) -
android/guava-tests/test/com/google/common/util/concurrent/AbstractAbstractFutureTest.java
assertEquals(1, future.get(-1, SECONDS).intValue()); } @J2ktIncompatible @GwtIncompatible // threads public void testOverflowTimeout() throws Exception { // First, sanity check that naive multiplication would really overflow to a negative number: long nanosPerSecond = NANOSECONDS.convert(1, SECONDS); assertThat(nanosPerSecond * Long.MAX_VALUE).isLessThan(0L);
Registered: Wed Jun 12 16:38:11 UTC 2024 - Last Modified: Tue Feb 13 14:28:25 UTC 2024 - 15.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_ops.td
} def TF_XlaSparseDenseMatmulWithStaticBufferSizeOp : TF_Op<"XlaSparseDenseMatmulWithStaticBufferSize", [Pure]> { let summary = "A XLA op which performs the dense-sparse matrix multiplication."; let arguments = (ins TF_Int32Tensor:$row_pointers, TF_Int32Tensor:$sorted_sample_ids, TF_Int32Tensor:$sorted_token_ids, TF_Float32Tensor:$sorted_gains,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 04:08:35 UTC 2024 - 90.5K bytes - Viewed (0) -
src/runtime/mcentral.go
size := uintptr(class_to_size[c.spanclass.sizeclass()]) s := mheap_.alloc(npages, c.spanclass) if s == nil { return nil } // Use division by multiplication and shifts to quickly compute: // n := (npages << _PageShift) / size n := s.divideByElemSize(npages << _PageShift) s.limit = s.base() + size*n s.initHeapBits(false) return s
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Mon Mar 25 19:53:03 UTC 2024 - 8.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/unroll_batch_matmul.cc
} const int64_t rows = lhs_shape[lhs_dims - 2]; const int64_t cols = rhs_shape[rhs_dims - 1]; if (lhs_shape[lhs_dims - 1] != rhs_shape[rhs_dims - 2]) { // Input dimensions must be compatible for multiplication. return failure(); } const auto matmul_type = RankedTensorType::get({rows, cols}, element_type); if (lhs_dims == 2 && rhs_dims == 2) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 11.6K bytes - Viewed (0) -
src/crypto/internal/nistec/p256_asm.go
// The following assembly functions are implemented in p256_asm_*.s // Montgomery multiplication. Sets res = in1 * in2 * R⁻¹ mod p. // //go:noescape func p256Mul(res, in1, in2 *p256Element) // Montgomery square, repeated n times (n >= 1). // //go:noescape func p256Sqr(res, in *p256Element, n int) // Montgomery multiplication by R⁻¹, or 1 outside the domain.
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Mon May 13 18:57:38 UTC 2024 - 21.4K bytes - Viewed (0)