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#ifndef LM_BLANK__
#define LM_BLANK__
#include <limits>
#include <inttypes.h>
#include <math.h>
namespace lm {
namespace ngram {
/* Suppose "foo bar" appears with zero backoff but there is no trigram
* beginning with these words. Then, when scoring "foo bar", the model could
* return out_state containing "bar" or even null context if "bar" also has no
* backoff and is never followed by another word. Then the backoff is set to
* kNoExtensionBackoff. If the n-gram might be extended, then out_state must
* contain the full n-gram, in which case kExtensionBackoff is set. In any
* case, if an n-gram has non-zero backoff, the full state is returned so
* backoff can be properly charged.
* These differ only in sign bit because the backoff is in fact zero in either
* case.
*/
const float kNoExtensionBackoff = -0.0;
const float kExtensionBackoff = 0.0;
inline void SetExtension(float &backoff) {
if (backoff == kNoExtensionBackoff) backoff = kExtensionBackoff;
}
// This compiles down nicely.
inline bool HasExtension(const float &backoff) {
typedef union { float f; uint32_t i; } UnionValue;
UnionValue compare, interpret;
compare.f = kNoExtensionBackoff;
interpret.f = backoff;
return compare.i != interpret.i;
}
/* Suppose "foo bar baz quux" appears in the ARPA but not "bar baz quux" or
* "baz quux" (because they were pruned). 1.2% of n-grams generated by SRI
* with default settings on the benchmark data set are like this. Since search
* proceeds by finding "quux", "baz quux", "bar baz quux", and finally
* "foo bar baz quux" and the trie needs pointer nodes anyway, blanks are
* inserted. The blanks have probability kBlankProb and backoff kBlankBackoff.
* A blank is recognized by kBlankProb in the probability field; kBlankBackoff
* must be 0 so that inference asseses zero backoff from these blanks.
*/
const float kBlankProb = -std::numeric_limits<float>::infinity();
const float kBlankBackoff = kNoExtensionBackoff;
} // namespace ngram
} // namespace lm
#endif // LM_BLANK__
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