chore: import upstream snapshot with attribution

2026-07-13 13:30:25 +08:00
commit f19b2512d7
562 changed files with 38082 additions and 0 deletions
@@ -0,0 +1,19 @@
+import unittest
+import numpy as np
+from prml import nn
+from prml.nn.array.broadcast import broadcast_to
+
+
+class TestBroadcastTo(unittest.TestCase):
+
+    def test_broadcast(self):
+        x = nn.Parameter(np.ones((1, 1)))
+        shape = (5, 2, 3)
+        y = broadcast_to(x, shape)
+        self.assertEqual(y.shape, shape)
+        y.backward(np.ones(shape))
+        self.assertTrue((x.grad == np.ones((1, 1)) * 30).all())
+
+
+if __name__ == '__main__':
+    unittest.main()
@@ -0,0 +1,21 @@
+import unittest
+import numpy as np
+from prml import nn
+
+
+class TestFlatten(unittest.TestCase):
+
+    def test_flatten(self):
+        self.assertRaises(TypeError, nn.flatten, "abc")
+        self.assertRaises(ValueError, nn.flatten, np.ones(1))
+
+        x = np.random.rand(5, 4)
+        p = nn.Parameter(x)
+        y = p.flatten()
+        self.assertTrue((y.value == x.flatten()).all())
+        y.backward(np.ones(20))
+        self.assertTrue((p.grad == np.ones((5, 4))).all())
+
+
+if __name__ == '__main__':
+    unittest.main()
@@ -0,0 +1,20 @@
+import unittest
+import numpy as np
+from prml import nn
+
+
+class TestReshape(unittest.TestCase):
+
+    def test_reshape(self):
+        self.assertRaises(ValueError, nn.reshape, 1, (2, 3))
+
+        x = np.random.rand(2, 6)
+        p = nn.Parameter(x)
+        y = p.reshape(3, 4)
+        self.assertTrue((x.reshape(3, 4) == y.value).all())
+        y.backward(np.ones((3, 4)))
+        self.assertTrue((p.grad == np.ones((2, 6))).all())
+
+
+if __name__ == '__main__':
+    unittest.main()
@@ -0,0 +1,21 @@
+import unittest
+import numpy as np
+from prml import nn
+
+
+class TestSplit(unittest.TestCase):
+
+    def test_split(self):
+        x = np.random.rand(10, 7)
+        a = nn.Parameter(x)
+        b, c = nn.split(a, (3,), axis=-1)
+        self.assertTrue((b.value == x[:, :3]).all())
+        self.assertTrue((c.value == x[:, 3:]).all())
+        b.backward(np.ones((10, 3)))
+        self.assertIs(a.grad, None)
+        c.backward(np.ones((10, 4)))
+        self.assertTrue((a.grad == np.ones((10, 7))).all())
+
+
+if __name__ == '__main__':
+    unittest.main()
@@ -0,0 +1,28 @@
+import unittest
+import numpy as np
+from prml import nn
+
+
+class TestTranspose(unittest.TestCase):
+
+    def test_transpose(self):
+        arrays = [
+            np.random.normal(size=(2, 3)),
+            np.random.normal(size=(2, 3, 4))
+        ]
+        axes = [
+            None,
+            (2, 0, 1)
+        ]
+
+        for arr, ax in zip(arrays, axes):
+            arr = nn.Parameter(arr)
+            arr_t = nn.transpose(arr, ax)
+            self.assertEqual(arr_t.shape, np.transpose(arr.value, ax).shape)
+            da = np.random.normal(size=arr_t.shape)
+            arr_t.backward(da)
+            self.assertEqual(arr.grad.shape, arr.shape)
+
+
+if __name__ == '__main__':
+    unittest.main()