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  /packages/inputmethods/PinyinIME/jni/share/
userdict.cpp 1108 uint32 *predicts = NULL;
1129 predicts = (uint32 *)malloc((dict_info.lemma_count + kUserDictPreAlloc) << 2);
1130 if (!predicts) goto error;
1170 readed += fread((((uint8*)predicts) + readed), 1, toread - readed, fp);
1208 predicts_ = predicts;
1230 if (predicts) free(predicts);
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  /external/antlr/antlr-3.4/tool/src/main/java/org/antlr/analysis/
DFA.java 133 * predicts it lest the parser not know what to do. Nondeterminisms can
890 * be computed or for which no single DFA accept state predicts those
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  /external/oprofile/events/i386/nehalem/
events 72 event:0xA7 counters:0,1,2,3 um:one minimum:6000 name:BACLEAR_FORCE_IQ : Counts number of times a BACLEAR was forced by the Instruction Queue. The IQ is also responsible for providing conditional branch prediciton direction based on a static scheme and dynamic data provided by the L2 Branch Prediction Unit. If the conditional branch target is not found in the Target Array and the IQ predicts that the branch is taken, then the IQ will force the Branch Address Calculator to issue a BACLEAR. Each BACLEAR asserted by the BAC generates approximately an 8 cycle bubble in the instruction fetch pipeline.
  /external/ceres-solver/docs/
solving.tex 47 Here, $\mu$ is the trust region radius, $D(x)$ is some matrix used to define a metric on the domain of $F(x)$ and $\rho$ measures the quality of the step $\Delta x$, i.e., how well did the linear model predict the decrease in the value of the non-linear objective. The idea is to increase or decrease the radius of the trust region depending on how well the linearization predicts the behavior of the non-linear objective, which in turn is reflected in the value of $\rho$.
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  /external/antlr/antlr-3.4/lib/
antlr-3.4-complete.jar 
  /prebuilts/misc/common/antlr/
antlr-3.4-complete.jar 

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