--- a
+++ b/dataset/read_raw_dataset.py
@@ -0,0 +1,50 @@
+import numpy as np
+from os import listdir
+from os.path import isfile, join
+
+def read_raw_dataset():
+    
+    """
+    Returns a dictionary `raw_dataset` which contains following keys pairs.
+    A, B, C, D, and F with values as 2D numpy array of shape (m, n).
+    
+    m = no. of training examples in each set
+    n - no. of data points in time series
+    """
+    
+    # Directory names are saved as F, O, N, etc.
+    # And set names are processed as A, B, C, etc.
+    mapping_set_to_dir = {
+        'A': (0,'Z'),
+        'B': (1,'O'),
+        'C': (2,'N'),
+        'D': (3,'F'),
+        'E': (4,'S')
+    }
+
+    file_lists = []
+    
+    # get the list of files for each set
+    # 1 file corresponds to 1 training example
+    for s,d in mapping_set_to_dir.items():
+        file_lists.insert(d[0], [f for f in listdir(d[1]) if isfile(join(d[1], f))])
+    
+    raw_dataset = { }
+
+    # loop over all sets
+    for s,d in mapping_set_to_dir.items():
+
+        # loop over every file (training example) in each set
+        for f in file_lists[d[0]]:
+            
+            # read the time series data
+            curr_example = np.loadtxt(join(d[1], f))
+
+            # create a key in the raw_database dict in case it doesn't exist already
+            # otherwise just append the new example in the 2D array
+            if (s in raw_dataset):
+                raw_dataset[s] = np.append(raw_dataset[s], [curr_example], axis=0)
+            else:
+                raw_dataset[s] = np.array([curr_example])
+    
+    return raw_dataset
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