Wrap arcsinh from numpy, elemwise.

theano/scalar/basic.py

@@ -2157,6 +2157,25 @@ class Sinh(UnaryScalarOp):
 sinh = Sinh(upgrade_to_float, name='sinh')
 
 
+class ArcSinh(UnaryScalarOp):
+    def impl(self, x):
+        return numpy.arcsinh(x)
+
+    def grad(self, (x, ), (gz, )):
+        if x.type in complex_types:
+            raise NotImplementedError()
+        if x.type in float_types:
+            return gz / sqrt(sqr(x) + numpy.cast[x.type](1)),
+        else:
+            return None,
+
+    def c_code(self, node, name, (x, ), (z, ), sub):
+        if node.inputs[0].type in complex_types:
+            raise NotImplementedError('type not supported', type)
+        return "%(z)s = asinh(%(x)s);" % locals()
+arcsinh = ArcSinh(upgrade_to_float, name='arcsinh')
+
+
 class Tanh(UnaryScalarOp):
     """
     tanh(x) = sinh(x) / cosh(x)

theano/tensor/basic.py

@@ -2606,6 +2606,11 @@ def sinh(a):
     """hyperbolic sine of a"""
 
 
+@_scal_elemwise_with_nfunc('arcsinh', 1, 1)
+def arcsinh(a):
+    """hyperbolic arc sine of a"""
+
+
 @_scal_elemwise_with_nfunc('tanh', 1, 1)
 def tanh(a):
     """hyperbolic tangent of a"""

theano/tensor/inplace.py

@@ -175,6 +175,10 @@ def arccosh_inplace(a):
 def sinh_inplace(a):
     """hyperbolic sine of `a` (inplace on `a`)"""
 
+@_scal_inplace
+def arcsinh_inplace(a):
+    """hyperbolic arc sine of `a` (inplace on `a`)"""
+
 @_scal_inplace
 def tanh_inplace(a):
     """hyperbolic tangent of `a` (inplace on `a`)"""

theano/tensor/tests/test_basic.py

@@ -1156,6 +1156,18 @@ SinhInplaceTester = makeBroadcastTester(op = inplace.sinh_inplace,
                                           grad = _grad_broadcast_unary_normal,
                                           inplace = True)
 
+
+ArcsinhTester = makeBroadcastTester(op = tensor.arcsinh,
+                                    expected = numpy.arcsinh,
+                                    good = _good_broadcast_unary_normal,
+                                    grad = _grad_broadcast_unary_normal)
+ArcsinhInplaceTester = makeBroadcastTester(op = inplace.arcsinh_inplace,
+                                           expected = numpy.arcsinh,
+                                           good = _good_broadcast_unary_normal,
+                                           grad = _grad_broadcast_unary_normal,
+                                           inplace = True)
+
+
 TanhTester = makeBroadcastTester(op = tensor.tanh,
                                    expected = numpy.tanh,
                                    good = _good_broadcast_unary_normal,