diff --git a/nnfwtbn/tests/test_toydata.py b/nnfwtbn/tests/test_toydata.py
new file mode 100644
index 0000000000000000000000000000000000000000..a67fecbcdc581a5ac9f898a3bda147108d63e4d3
--- /dev/null
+++ b/nnfwtbn/tests/test_toydata.py
@@ -0,0 +1,53 @@
+
+import unittest
+import numpy as np
+from nnfwtbn.toydata import rand
+
+
+class ToyDataTestBase(unittest.TestCase):
+
+    def test_rand_length(self):
+        """
+        Check that the returned array has the requested length.
+        """
+        self.assertEqual(len(rand()), 1)
+        self.assertEqual(len(rand(size=1)), 1)
+        self.assertEqual(len(rand(312)), 312)
+
+    def test_rand_repeatable(self):
+        """
+        Check that the method returns the same array twice when called with
+        the same seed.
+        """
+        numbers_1 = list(rand(143))
+        numbers_2 = list(rand(143))
+        self.assertEqual(numbers_1, numbers_2)
+
+    def test_rand_seed(self):
+        """
+        Check that using different seeds returns different values.
+        """
+        numbers_1 = list(rand(143, seed=1))
+        numbers_2 = list(rand(143, seed=2))
+        self.assertNotEqual(numbers_1, numbers_2)
+
+    def test_rand_independent(self):
+        """
+        Check that setting the numpy seed does not affect return value.
+        """
+        np.random.seed(1234)
+        numbers_1 = list(rand(143))
+
+        np.random.seed(4321)
+        numbers_2 = list(rand(143))
+
+        self.assertEqual(numbers_1, numbers_2)
+
+    def test_rand_values(self):
+        """
+        Check for specific values.
+        """
+        a, b, c = rand(3)
+        self.assertAlmostEqual(a, 0.90141859)
+        self.assertAlmostEqual(b, 0.85225178)
+        self.assertAlmostEqual(c, 0.93632300)
diff --git a/nnfwtbn/toydata.py b/nnfwtbn/toydata.py
new file mode 100644
index 0000000000000000000000000000000000000000..2dffb59d85c44e7d9331221dfecf8847e13d0da4
--- /dev/null
+++ b/nnfwtbn/toydata.py
@@ -0,0 +1,33 @@
+"""
+This module implements method to generate a deterministic, physics-inspired
+toy dataset. The dataset is intended for documentations and examples. The
+module does not rely on external random number generators (seeding numpy
+might break user code).
+"""
+
+import numpy as np
+from numba import njit
+
+@njit
+def rand(size=1, seed=1991):
+    """
+    Returns a numpy array with random floats between 0 and 1. Calling the
+    method twice (with the same seed) returns the same array.
+
+    The parameter n defines the length of the returned array.
+    """
+    # This method uses a Linear congruential generator with the same
+    # parameters used in Virtual Pascal.
+    # https://en.wikipedia.org/wiki/Linear_congruential_generator
+    m = 2**32
+    a = 134775813
+    c = 1 
+
+    output = np.empty(size)
+    seed = (a * seed + c) % m  # make the output look more random
+    output[0] = (a * seed + c) % m
+
+    for i in range(1, size):
+        output[i] = (a * output[i - 1] + c) % m
+
+    return output / m
diff --git a/requirements.txt b/requirements.txt
index c0077b061bcd7461817d8383d03c0a421648dc56..fcadea7ecc2055e4ccb7a66d2be3136986f6a9df 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,3 +2,4 @@ numpy
 matplotlib
 seaborn
 pandas
+numba