linalg

theano/linalg/__init__.py

+
+from ops import (cholesky, matrix_inverse, solve,
+        diag, extract_diag, alloc_diag,
+        det, PSD_hint,
+        trace, spectral_radius_bound)

theano/linalg/tests/test_linalg.py

+
+import numpy
+from theano import tensor, function
+
+from .ops import *
+
+if 0:
+    def test_cholesky():
+        #TODO: test upper and lower triangular
+        #todo: unittest randomseed 
+        rng = numpy.random.RandomState(1234)
+
+        r = rng.randn(5,5)
+
+        pd = numpy.dot(r,r.T)
+
+        x = tensor.matrix()
+        chol = Cholesky()(x)
+        f = function([x], tensor.dot(chol, chol.T)) # an optimization could remove this
+
+        ch_f = function([x], chol)
+
+        # quick check that chol is upper-triangular
+        ch = ch_f(pd)
+        print ch
+        assert ch[0,4] != 0
+        assert ch[4,0] == 0
+        assert numpy.allclose(numpy.dot(ch.T,ch),pd)
+        assert not numpy.allclose(numpy.dot(ch,ch.T),pd)
+
+
+def test_inverse_correctness():
+    #todo: unittest randomseed
+    rng = numpy.random.RandomState(12345)
+
+    r = rng.randn(4,4)
+
+    x = tensor.matrix()
+    xi = matrix_inverse(x)
+
+    ri = function([x], xi)(r)
+    assert ri.shape == r.shape
+    assert ri.dtype == r.dtype
+
+    rir = numpy.dot(ri,r)
+    rri = numpy.dot(r,ri)
+
+    assert numpy.allclose(numpy.identity(4), rir), rir
+    assert numpy.allclose(numpy.identity(4), rri), rri
+
+def test_inverse_grad():
+
+    rng = numpy.random.RandomState(1234)
+
+    r = rng.randn(4,4)
+    tensor.verify_grad(matrix_inverse, [r], rng=numpy.random)
+
+
+def test_det_grad():
+    rng = numpy.random.RandomState(1234)
+
+    r = rng.randn(5,5)
+    tensor.verify_grad(det, [r], rng=numpy.random)
+