diff --git a/README.md b/README.md
index 2de7c15..cef9b2b 100644
--- a/README.md
+++ b/README.md
@@ -177,11 +177,11 @@ gt = adata.obs["gt"]
Then, set the parameters.
```python
-k = 20 # number of clusters
-epochs = 120
-seed = 666
-alpha = 1 # recommended value
-learning_rate = 0.0001
+k = 20 # number of clusters
+epochs = 120 # epoch in training
+seed = 666 # random seed
+alpha = 1 # recommended value
+learning_rate = 0.0001 # learning rate in training
```
Now, read the results from 10 SOTA methods.
@@ -193,8 +193,69 @@ mul_reults = pd.read_csv(
index_col=0
)
mul_reults = mul_reults.iloc[:, 2:]
+
+```
+
+
+Now, we can observe the consistency between the results of different methods.
+
+```python
+plot_results_ari(mul_reults)
+```
+
+
+
+
+ Consistency between different methods
+
+
+
+```python
+# Discard methods that show poor consistency
+mul_reults = mul_reults.drop("SpaceFlow", axis=1)
+mul_reults = mul_reults.drop("MENDER", axis=1)
+
+# compute the positional similarity matrix
+pos_similarity = calculate_location_adj(adata.obsm["spatial"], l=123)
+
+model = Space.Space(
+ get_bool_martix(mul_reults),
+ pos_similarity,
+ epochs=epochs,
+ gt=gt.values,
+ k=k,
+ seed=seed,
+ alpha=alpha,
+ beta=1,
+ learning_rate=learning_rate,
+)
+
+# tarining model
+con_martix = model.train()
+
+# set spectral cluster model
+sc = SpectralClustering(n_clusters=k, affinity="precomputed", random_state=666)
+
+# clustering
+labels = sc.fit_predict(con_martix)
+
+adata.obs["consensus"] = labels
+
+ari = adjusted_rand_score(labels, gt.values)
+
+print(ari)
```
+you will obtain a result from Space with an ARI of 0.648.
+
+*In most cases, Space does not yield a fixed result. This is not due to an issue with Space, but because some methods exhibit randomness even when the random seed is fixed. Please refer to [this](https://github.com/QIFEIDKN/STAGATE/issues/10) for more information. However, the variations in the results we obtain are minimal. The outcomes are stable across multiple runs.*
+
+
#### Visualization
@@ -210,4 +271,5 @@ mul_reults = mul_reults.iloc[:, 2:]
### 3.3 How to choose and use different baseline algorithms
-## 4 Citation
\ No newline at end of file
+## 4 Citation
+