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 +1,21 @@
+import unittest
+import numpy as np
+from prml import nn
+
+
+class TestBernoulli(unittest.TestCase):
+
+    def test_bernoulli(self):
+        np.random.seed(1234)
+        obs = np.random.choice(2, 1000, p=[0.1, 0.9])
+        a = nn.Parameter(0)
+        for _ in range(100):
+            a.cleargrad()
+            x = nn.random.Bernoulli(logit=a, data=obs)
+            x.log_pdf().sum().backward()
+            a.value += a.grad * 0.01
+        self.assertAlmostEqual(x.mu.value, np.mean(obs))
+
+
+if __name__ == '__main__':
+    unittest.main()
@@ -0,0 +1,22 @@
+import unittest
+import numpy as np
+from prml import nn
+
+
+class TestCategorical(unittest.TestCase):
+
+    def test_categorical(self):
+        np.random.seed(1234)
+        obs = np.random.choice(3, 100, p=[0.2, 0.3, 0.5])
+        obs = np.eye(3)[obs]
+        a = nn.Parameter(np.zeros(3))
+        for _ in range(100):
+            a.cleargrad()
+            x = nn.random.Categorical(logit=a, data=obs)
+            x.log_pdf().sum().backward()
+            a.value += 0.01 * a.grad
+        self.assertTrue(np.allclose(np.mean(obs, 0), x.mu.value))
+
+
+if __name__ == '__main__':
+    unittest.main()
@@ -0,0 +1,26 @@
+import unittest
+import numpy as np
+from prml import nn
+
+
+class TestCauchy(unittest.TestCase):
+
+    def test_cauchy(self):
+        np.random.seed(1234)
+        obs = np.random.standard_cauchy(size=10000)
+        obs = 2 * obs + 1
+        loc = nn.Parameter(0)
+        s = nn.Parameter(1)
+        for _ in range(100):
+            loc.cleargrad()
+            s.cleargrad()
+            x = nn.random.Cauchy(loc, nn.softplus(s), data=obs)
+            x.log_pdf().sum().backward()
+            loc.value += loc.grad * 0.001
+            s.value += s.grad * 0.001
+        self.assertAlmostEqual(x.loc.value, 1, places=1)
+        self.assertAlmostEqual(x.scale.value, 2, places=1)
+
+
+if __name__ == '__main__':
+    unittest.main()
@@ -0,0 +1,28 @@
+import unittest
+import numpy as np
+from prml import nn
+
+
+class TestDirichlet(unittest.TestCase):
+
+    def test_dirichlet(self):
+        np.random.seed(1234)
+        obs = np.random.choice(3, 100, p=[0.2, 0.3, 0.5])
+        obs = np.eye(3)[obs]
+        a = nn.Parameter(np.zeros(3))
+        for _ in range(100):
+            a.cleargrad()
+            mu = nn.softmax(a)
+            d = nn.random.Dirichlet(np.ones(3) * 10, data=mu)
+            x = nn.random.Categorical(mu, data=obs)
+            log_posterior = x.log_pdf().sum() + d.log_pdf().sum()
+            log_posterior.backward()
+            a.value += 0.01 * a.grad
+
+        count = np.sum(obs, 0) + 10
+        p = count / count.sum(keepdims=True)
+        self.assertTrue(np.allclose(p, mu.value, 1e-2, 1e-2))
+
+
+if __name__ == '__main__':
+    unittest.main()
@@ -0,0 +1,21 @@
+import unittest
+import numpy as np
+from prml import nn
+
+
+class TestExponential(unittest.TestCase):
+
+    def test_exponential(self):
+        np.random.seed(1234)
+        obs = np.random.gamma(1, 1 / 0.5, size=1000)
+        a = nn.Parameter(0)
+        for _ in range(100):
+            a.cleargrad()
+            x = nn.random.Exponential(nn.softplus(a), data=obs)
+            x.log_pdf().sum().backward()
+            a.value += a.grad * 0.001
+        self.assertAlmostEqual(x.rate.value, 0.475135117)
+
+
+if __name__ == '__main__':
+    unittest.main()
@@ -0,0 +1,14 @@
+import unittest
+import numpy as np
+from prml import nn
+
+
+class TestGaussian(unittest.TestCase):
+
+    def test_gaussian(self):
+        self.assertRaises(ValueError, nn.random.Gaussian, 0, -1)
+        self.assertRaises(ValueError, nn.random.Gaussian, 0, np.array([1, -1]))
+
+
+if __name__ == '__main__':
+    unittest.main()
@@ -0,0 +1,24 @@
+import unittest
+import numpy as np
+from prml import nn
+
+
+class TestLaplace(unittest.TestCase):
+
+    def test_laplace(self):
+        obs = np.arange(3)
+        loc = nn.Parameter(0)
+        s = nn.Parameter(1)
+        for _ in range(1000):
+            loc.cleargrad()
+            s.cleargrad()
+            x = nn.random.Laplace(loc, nn.softplus(s), data=obs)
+            x.log_pdf().sum().backward()
+            loc.value += loc.grad * 0.01
+            s.value += s.grad * 0.01
+        self.assertAlmostEqual(x.loc.value, np.median(obs), places=1)
+        self.assertAlmostEqual(x.scale.value, np.mean(np.abs(obs - x.loc.value)), places=1)
+
+
+if __name__ == '__main__':
+    unittest.main()
@@ -0,0 +1,33 @@
+import unittest
+import numpy as np
+from prml import nn
+
+
+class TestMultivariateGaussian(unittest.TestCase):
+
+    def test_multivariate_gaussian(self):
+        self.assertRaises(ValueError, nn.random.MultivariateGaussian, np.zeros(2), np.eye(3))
+        self.assertRaises(ValueError, nn.random.MultivariateGaussian, np.zeros(2), np.eye(2) * -1)
+
+        x_train = np.array([
+            [1., 1.],
+            [1., -1],
+            [-1., 1.],
+            [-1., -2.]
+        ])
+        mu = nn.Parameter(np.ones(2))
+        cov = nn.Parameter(np.eye(2) * 2)
+        for _ in range(1000):
+            mu.cleargrad()
+            cov.cleargrad()
+            x = nn.random.MultivariateGaussian(mu, cov + cov.transpose(), data=x_train)
+            log_likelihood = x.log_pdf().sum()
+            log_likelihood.backward()
+            mu.value += 0.1 * mu.grad
+            cov.value += 0.1 * cov.grad
+        self.assertTrue(np.allclose(mu.value, x_train.mean(axis=0)))
+        self.assertTrue(np.allclose(np.cov(x_train, rowvar=False, bias=True), x.cov.value))
+
+
+if __name__ == '__main__':
+    unittest.main()