From 8ff8739b236a00169339b0b78e1f39357fdfff17 Mon Sep 17 00:00:00 2001
From: Marcin Chrzanowski <m@m-chrzan.xyz>
Date: Sat, 22 May 2021 19:23:23 +0200
Subject: Generate data

---
 data/generate.py |  7 +++++++
 data/testset.py  | 18 ++++++++++++++++++
 2 files changed, 25 insertions(+)
 create mode 100644 data/generate.py
 create mode 100644 data/testset.py

diff --git a/data/generate.py b/data/generate.py
new file mode 100644
index 0000000..4b0ba9c
--- /dev/null
+++ b/data/generate.py
@@ -0,0 +1,7 @@
+import numpy as np
+
+def get_single_example(n_tokens=16, seqlen=64, max_count=9):
+  seq = np.random.randint(low=0, high=n_tokens, size=(seqlen,))
+  label = [min(list(seq[:i]).count(x), max_count) for i, x in enumerate(seq)]
+  label = np.array(label)
+  return seq, label
diff --git a/data/testset.py b/data/testset.py
new file mode 100644
index 0000000..07fc811
--- /dev/null
+++ b/data/testset.py
@@ -0,0 +1,18 @@
+import torch
+
+from data.generate import get_single_example
+
+TEST_SIZE = 128
+
+test_examples = [get_single_example() for i in range(TEST_SIZE)]
+
+def get_testset(device='cpu'):
+    # Transpositions are used, because the convention in PyTorch is to represent
+    # sequence tensors as <seq_len, batch_size> instead of <batch_size, seq_len>.
+    test_X = torch.tensor(
+        [x[0] for x in test_examples], device=device
+    ).transpose(0, 1)
+    test_Y = torch.tensor(
+        [x[1] for x in test_examples], device=device
+    ).transpose(0, 1)
+    return test_X, test_Y
-- 
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