From dfdaa316dc6da927384deaa3b65a02e975d3c5e5 Mon Sep 17 00:00:00 2001
From: Chen Lixiang <chenlixiang.dev@gmail.com>
Date: Fri, 30 Jun 2023 02:21:53 +0800
Subject: [PATCH] fix format

---
 README.md  | 10 +++++++---
 src/hnsw.c |  8 ++++----
 src/test.c |  8 ++++----
 3 files changed, 15 insertions(+), 11 deletions(-)

diff --git a/README.md b/README.md
index 631ce65..0099270 100644
--- a/README.md
+++ b/README.md
@@ -35,17 +35,21 @@
    ./hnsw_test ../dataset/siftsmall/siftsmall_base.fvecs 10000 ../dataset/siftsmall/siftsmall_query.fvecs 100 ../dataset/siftsmall/siftsmall_groundtruth.ivecs
    ```
 
+   Our test program will report the recall value and time costs of your algorithm.
+
 ### 2. Mission Description
 
 You need to implement two functions inside hnsw.h and hnsw.c in HNSW way:
 
 ```C
-HNSWContext *hnsw_init_context(const char *filename, size_t dim, size_t len);
-void hnsw_approximate_knn(HNSWContext *ctx, VecData *q, int *results, int k);
+HNSWContext *hnsw_init_context(const char *filename, size_t dim, size_t len); // load data and build graph
+void hnsw_approximate_knn(HNSWContext *ctx, VecData *q, int *results, int k); // search KNN results
 ```
 
-We have implemented data loading and provided a simplest KNN algorithm. But our implementation can only handle small batches of data(SIFTSMALL dataset), please implement a new approximate KNN algorithm based on the HNSW algorithm so that it can handle large batches of data(SIFT dataset).
+We have implemented data loading and provided a simplest KNN algorithm. But our implementation can only handle small batches of data(SIFTSMALL dataset), please implement a new approximate KNN algorithm based on the HNSW algorithm so that it can handle large batches of data(SIFT dataset) efficiently.
 
 ### 3. Data Download
 
 Please visit http://corpus-texmex.irisa.fr/ 
+
+TODO: We should provide a script to download datasets automatically
diff --git a/src/hnsw.c b/src/hnsw.c
index fc8ab67..386f115 100644
--- a/src/hnsw.c
+++ b/src/hnsw.c
@@ -3,18 +3,18 @@
 
 HNSWContext *hnsw_init_context(const char *filename, size_t dim, size_t len)
 {
-    HNSWContext *ctx = (HNSWContext *) malloc(sizeof(HNSWContext));
+    HNSWContext *ctx = (HNSWContext *)malloc(sizeof(HNSWContext));
     ctx->dim = dim;
     ctx->len = len;
-    ctx->data = (VecData *) malloc(sizeof(VecData) * len);
+    ctx->data = (VecData *)malloc(sizeof(VecData) * len);
 
     // init file context
-    FileContext* f_ctx = init_file_context(filename);
+    FileContext *f_ctx = init_file_context(filename);
 
     for (int i = 0; i < len; i++)
     {
         ctx->data[i].id = i;
-        ctx->data[i].vec = (float *) malloc(sizeof(float) * GLOBAL_DIM);
+        ctx->data[i].vec = (float *)malloc(sizeof(float) * GLOBAL_DIM);
         read_vec_data(f_ctx, ctx->data[i].vec);
     }
 
diff --git a/src/test.c b/src/test.c
index 5517b33..88bf23e 100644
--- a/src/test.c
+++ b/src/test.c
@@ -21,7 +21,7 @@ float cal_recall_value(int *results, int *trueset, int k)
         }
     }
 
-    return ((float) cnt) / ((float) k);
+    return ((float)cnt) / ((float)k);
 }
 
 int main(int argc, char *argv[])
@@ -43,10 +43,10 @@ int main(int argc, char *argv[])
 
     // TODO: report time cost here
 
-    HNSWContext* ctx = hnsw_init_context(argv[1], GLOBAL_DIM, data_size);
+    HNSWContext *ctx = hnsw_init_context(argv[1], GLOBAL_DIM, data_size);
     printf("HNSW context initialized!\n");
     VecData q_vec;
-    q_vec.vec = (float *) malloc(sizeof(float) * GLOBAL_DIM);
+    q_vec.vec = (float *)malloc(sizeof(float) * GLOBAL_DIM);
     int q_results[100];
     int true_results[100];
     float total_recall_values = 0.0;
@@ -59,7 +59,7 @@ int main(int argc, char *argv[])
     }
 
     // report recall value
-    printf("Recall value: %.4f\n", total_recall_values / ((float) query_size));
+    printf("Recall value: %.4f\n", total_recall_values / ((float)query_size));
 
     free_file_context(query_file_ctx);
     free_file_context(gt_file_ctx);