update README

README.md

@@ -8,7 +8,9 @@ size_categories:
 
 # Brogue Map Dataset
 
-This is the Map dataset from the open-sourced game [Brogue](https://github.com/tmewett/BrogueCE).
+## 1. Data Explanation
+
+This is the Map dataset from the open-sourced game [Brogue](https://github.com/tmewett/BrogueCE). It contains 40,000 train dataset, 10,000 test dataset and 10,000 validation dataset.
 
 Each map is stored in a `.csv` file. The map is a `(32x32)` array, which is the map size.
 
@@ -31,3 +33,86 @@ Each cell in the array is a `int` number ranged from 0 to 13, which represented
   "G_FIRE": 12,
   "G_BRIDGE": 13
 ```
+An example map datapoint is in the format of
+
+```
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+0,0,1,1,1,8,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+0,1,1,1,8,8,8,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+0,1,1,8,8,8,8,0,0,0,1,1,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0
+0,1,1,1,8,8,0,0,0,1,1,1,1,1,1,1,1,0,0,1,0,0,0,0,0,0,0,0
+0,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,0,1,8,0,0,1,1,1,1,0,0
+0,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0
+0,0,1,1,1,0,0,0,0,0,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,0
+0,0,0,0,1,0,0,0,0,0,0,1,1,1,0,0,0,0,1,1,1,1,0,0,1,1,1,9
+0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,1,1,0
+0,0,1,1,1,1,1,0,0,0,0,0,0,1,1,1,1,0,0,0,1,0,0,0,0,0,0,0
+0,0,1,1,1,1,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0
+0,1,1,1,1,1,1,0,0,0,0,0,0,1,1,1,1,0,1,1,1,1,0,0,0,0,0,0
+0,1,8,1,1,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0
+0,1,8,8,8,8,1,8,0,0,0,0,1,8,1,1,0,0,0,0,0,0,0,0,0,1,1,1
+0,0,8,8,8,8,8,8,0,0,0,8,8,8,8,8,1,0,0,0,1,1,0,0,0,1,1,1
+0,0,1,8,8,8,8,8,8,0,1,8,8,8,8,8,1,0,0,0,1,1,0,0,0,0,1,1
+0,0,0,1,8,8,8,8,8,0,1,1,1,8,8,1,0,0,0,0,1,1,0,1,0,1,1,1
+0,0,0,8,8,8,8,8,8,1,1,1,1,8,1,1,0,0,0,0,1,1,1,1,0,1,1,0
+0,0,0,8,8,8,8,1,0,0,0,3,1,0,1,0,0,0,0,0,0,1,1,1,0,1,1,0
+0,0,0,0,8,8,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0
+0,0,0,1,1,0,0,0,0,0,0,0,0,0,11,1,1,1,1,1,1,1,1,1,1,1,0,0
+0,1,1,1,8,1,0,0,0,0,0,0,0,0,11,11,11,1,1,1,1,1,1,1,1,1,1,0
+0,0,1,1,1,1,0,0,0,0,0,0,0,0,11,11,0,0,1,1,0,0,1,1,1,1,1,1
+0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+```
+
+## 2. Data processing
+
+Huggingface does not store the map data in the correct format. To get each correct map data, use the following code:
+
+```python
+from datasets import load_dataset
+import numpy as np
+import matplotlib.pyplot as plt
+
+# load dataset from hugging face
+dataset = load_dataset("DolphinNie/dungeon-dataset")
+
+# Dataset stored by huggingface is not in a correct format, we need to do further process
+def dataset_convert(dataset):
+    dataset_train = list()
+    dataset_test = list()
+    dataset_valid = list()
+    datasets = [dataset_train, dataset_test, dataset_valid]
+    name = ['train', 'test', 'validation']
+    for i in range(3):
+        datapoint_num = int(dataset[name[i]].num_rows / 32)
+        dataset_tf = dataset[name[i]].to_pandas()
+        for n in range(datapoint_num):
+            env_num = tf_train[n*32:(n+1)*32]
+            datasets[i].append(env_num)
+    return dataset_train, dataset_test, dataset_valid
+
+dataset_train, dataset_test, dataset_valid = dataset_convert(dataset)
+
+# Visualize the datapoints if you want
+def visualize_map(dungeon_map):
+    """
+    Visualization of the map
+    :param map: the dungeon map representation (32x32x6)
+    :return:
+    """
+    plt.imshow(dungeon_map, cmap='viridis', interpolation='nearest')
+    plt.title('dungeon map')
+    plt.show()
+    
+visualize_map(dataset_train[10000])
+```
+
+<img src="./README.assets/image-20240411203604268.png" alt="image-20240411203604268" style="zoom:50%;" />	
+
+Note that this dataset contains a two-dimensional representation of the map, not a three-dimensional one-hot representation. If you need to train a new model, you need to further process the data set.