initial

Markov.py

@@ -0,0 +1,41 @@
+import random
+import json
+import argparse
+
+def select(model: dict):
+    m_filtered = list(filter(lambda i: i[0] != "_count", model.items()))
+    return random.choices(
+        list(map(lambda i: i[0], m_filtered)),
+        list(map(lambda i: i[1]["_count"], m_filtered)),
+        k=1
+    )[0]
+
+def generate_token(model, former, current):
+    p = random.random()
+    if p <= 0.90:
+        try:
+            return select(model[former][current])
+        except: pass
+    if p <= 0.98:
+        try:
+            return select(model[current])
+        except: pass
+    return select(model)
+
+def main(args):
+    with open(args.model) as m_file: model = json.load(m_file)
+    former = select(model)
+    current = select(model[former])
+    print(f"{former} {current} ", end="")
+    for _ in range(args.size - 2):
+        prior = generate_token(model, former, current)
+        print(prior + " ", end="")
+        former = current
+        current = prior
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('model')
+    parser.add_argument('-s', '--size', default=100, type=int)
+    main(parser.parse_args())