53 lines
2.0 KiB
C
53 lines
2.0 KiB
C
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#include "c_NlpNgramGraph.h"
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#include <stdlib.h>
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#include <stdio.h>
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#define RUN_TEST(test_case, name) \
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do { \
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printf("[RUN] %s... ", name); \
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if (test_case) { \
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printf("\033[32mPASSED\033[0m\n"); \
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} else { \
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printf("\033[31mFAILED\033[0m (%s:%d)\n", __FILE__, __LINE__); \
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return C_ERR_FAIL; \
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} \
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} while(0)
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static c_err_t test(void) {
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/* 33. 多元馬爾可夫鏈（Trigram）核心通路驗證 */
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c_NlpNgramGraph_t trigram;
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// 建立 3元 模型：需要根據 2 个字的历史預測第 3 个字
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c_err_t err = c_NlpNgramGraph_Init(&trigram, 3, 512, 8);
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RUN_TEST(err == C_ERR_OK, "Trigram Graph initialized cleanly");
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// 載入預測語料："自然语言、自然界、自然语言"
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// 當已知歷史短语是 "自然" 时，"语" 出现了 2 次，"界" 出现了 1 次
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c_ucs4_t corpus_stream[] = {
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0x81EA, 0x7136, 0x8BED, 0x8A00, // 自然语言
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0x81EA, 0x7136, 0x754C, // 自然界
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0x81EA, 0x7136, 0x8BED, 0x8A00 // 自然语言
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};
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err = c_NlpNgramGraph_Process(&trigram, corpus_stream, 11);
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RUN_TEST(err == C_ERR_OK && trigram.total_states > 0, "Trigram processed training data stream");
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// 構建查詢歷史：[0x81EA, 0x7136] ➔ 代表 "自然"
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c_ucs4_t query_history[] = { 0x81EA, 0x7136 };
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c_ucs4_t pred_res = 0;
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double pred_conf = 0.0;
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err = c_NlpNgramGraph_PredictNext(&trigram, query_history, &pred_res, &pred_conf);
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RUN_TEST(err == C_ERR_OK, "PredictNext completed high-order context traversal paths");
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// 預期：準確命中 "语" (0x8BED)，置信度為 2 / 3 = 66.67%
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RUN_TEST(pred_res == 0x8BED, "Trigram language model predicts '语' given advanced context '自然'");
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RUN_TEST(pred_conf > 0.66 && pred_conf < 0.67, "Context-bound model confidence targets 66.67%% correctly");
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c_NlpNgramGraph_Destroy(&trigram);
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return C_ERR_OK;
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}
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int main(int argc, char** argv){
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return test();
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}
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