test ml lr

Author: Ehsan Totoni

hpat/tests/test_ml.py

@@ -0,0 +1,52 @@
+import unittest
+import pandas as pd
+import numpy as np
+import numba
+import h5py
+import hpat
+from hpat.tests.test_utils import (count_array_REPs, count_parfor_REPs,
+                            count_parfor_OneDs, count_array_OneDs, dist_IR_contains)
+
+
+class TestML(unittest.TestCase):
+    def test_logistic_regression(self):
+        def test_impl(n, d):
+            iterations = 3
+            X = np.ones((n,d))+.5
+            Y = np.ones(n)
+            D = X.shape[1]
+            w = np.ones(D)-0.5
+            for i in range(iterations):
+                w -= np.dot(((1.0 / (1.0 + np.exp(-Y * np.dot(X,w))) - 1.0) * Y), X)
+            return w
+
+        hpat_func = hpat.jit(test_impl)
+        n = 11
+        d = 4
+        np.testing.assert_allclose(hpat_func(n, d), test_impl(n, d))
+        self.assertEqual(count_array_OneDs(), 3)
+        self.assertEqual(count_parfor_OneDs(), 3)
+
+    def test_logistic_regression_acc(self):
+        def test_impl(N, D):
+            iterations = 3
+            g = 2 * np.ones(D) - 1
+            X = 2 * np.ones((N, D)) - 1
+            Y = (np.dot(X, g) > 0.0) == (np.ones(N) > .90)
+
+            w = 2 * np.ones(D) - 1
+            for i in range(iterations):
+                w -= np.dot(((1.0 / (1.0 + np.exp(-Y * np.dot(X, w))) - 1.0) * Y), X)
+                #R = np.dot(X,w) > 0.0
+                #accuracy = np.sum(R == Y) / N
+            return w
+
+        hpat_func = hpat.jit(test_impl)
+        n = 11
+        d = 4
+        #np.testing.assert_allclose(hpat_func(n, d), test_impl(n, d))
+        #self.assertEqual(count_array_OneDs(), 3)
+        #self.assertEqual(count_parfor_OneDs(), 3)
+
+if __name__ == "__main__":
+    unittest.main()