Added norm function.

theano/sandbox/linalg/ops.py

@@ -1215,8 +1215,93 @@ def eigvalsh(a, b, lower=True):
     return Eigvalsh(lower)(a, b)
 
 
+<<<<<<< HEAD
 def matrix_power(M, n):
     result = 1
     for i in xrange(n):
         result = theano.dot(result, M)
     return result
+=======
+def norm(x,ord,axis):
+	x = as_tensor_variable(x)
+	ndim = x.ndim
+	if ndim == 0:
+		raise TypeError
+	elif ndim == 1:
+		if ord == None:
+			z = (tensor.sum(x**2))**(0.5)
+			return z
+		elif ord == 'inf':
+			z = tensor.max(abs(x))
+			return z
+		elif ord == '-inf':
+			z = tensor.min(abs(x))
+			return z
+		elif ord == 0:
+			z = (x[x.nonzero()]).shape[0]
+			return z
+		else:
+			try:
+				z = (tensor.sum(abs(x**ord)))**(1./ord)
+			except TypeError:
+				raise ValueError
+			return z
+	elif ndim >= 2:
+		if axis == None:
+			if ord == None or ord == 'fro':
+				z = (tensor.sum(abs(x**2)))**(0.5)
+				return z
+			elif ord == 'inf':
+				z = tensor.sum(abs(x),1)
+				return tensor.max(z)
+			elif ord == '-inf':
+				z = tensor.sum(abs(x),1)
+				return tensor.min(z)
+			elif ord == 1:
+				z = tensor.sum(abs(x),0)
+				return tensor.max(z)
+			elif ord == -1:
+				z = tensor.sum(abs(x),0)
+				return tensor.min(z)
+			else:
+				raise ValueError(0)
+		else:
+			try:
+				axis + 1
+				if ord == None:
+					z = (tensor.sum(x**2,axis))**(0.5)
+					return z
+				elif ord == 'inf':
+					z = tensor.max(abs(x),axis)
+					return z
+				elif ord == '-inf':
+					z = tensor.min(abs(x),axis)
+					return z
+				elif ord == 0:
+					z = tensor.sum(tensor.neq(x,0),1)
+					return z
+				else:
+					try:
+						z = (tensor.sum(abs(x**ord),axis))**(1./ord)
+					except TypeError:
+						raise ValueError
+					return z
+			except TypeError:
+				if ord == None or ord == 'fro':
+					z = (tensor.sum(abs(x**2),axis))**(0.5)
+					return z
+				elif ord == 'inf':
+					z = tensor.sum(abs(x),1)
+					return tensor.max(z,axis)
+				elif ord == '-inf':
+					z = tensor.sum(abs(x),1)
+					return tensor.min(z,axis)
+				elif ord == 1:
+					z = tensor.sum(abs(x),0)
+					return tensor.max(z,axis)
+				elif ord == -1:
+					z = tensor.sum(abs(x),0)
+					return tensor.min(z,axis)
+				else:
+					raise ValueError(0)
+>>>>>>> Added norm function.

theano/sandbox/linalg/tests/test_linalg.py

@@ -31,7 +31,9 @@ from theano.sandbox.linalg.ops import (cholesky,
                                        imported_scipy,
                                        Eig,
                                        inv_as_solve
+                                       norm
                                        )
+
 from theano.sandbox.linalg import eig, eigh, eigvalsh
 from nose.plugins.skip import SkipTest
 from nose.plugins.attrib import attr
@@ -611,6 +613,7 @@ def test_eigvalsh_grad():
                        [a, b], rng=numpy.random)
 
 
+<<<<<<< HEAD
 class Matrix_power():
 
     def test_numpy_compare(self):
@@ -631,3 +634,19 @@ class Matrix_power():
         f = function([A], [Q])
         a = rng.rand(4, 3).astype(theano.config.floatX)
         self.assertRaises(ValueError, f, a)
+
+class T_NormTests(unittest.TestCase):
+	def test_wrong_type_of_ord_for_vector(self):
+		self.assertRaises(ValueError, norm, [2,1],'fro',0)
+	def test_wrong_type_of_ord_for_vector_in_matrix(self):
+		self.assertRaises(ValueError, norm, [[2,1],[3,4]],'fro',0)	
+	def test_wrong_type_of_ord_for_vector_in_tensor(self):
+		self.assertRaises(ValueError, norm, [[[2,1],[3,4]],[[6,5],[7,8]]],'fro',0)
+	def test_wrong_type_of_ord_for_matrix(self):
+		self.assertRaises(ValueError, norm, [[2,1],[3,4]],0,None)
+	def test_wrong_type_of_ord_for_matrix_in_tensor(self):
+		self.assertRaises(ValueError, norm, [[[2,1],[3,4]],[[6,5],[7,8]]],0,None)
+	def test_non_tensorial_input(self):
+		self.assertRaises(TypeError, norm, 3, None, None)
+	def test_no_enough_dimensions(self):
+		self.assertRaises(ValueError, norm, [[2,1],[3,4]], None, 3)