optimise bpnn benchmark

Demos/benchmarks/bpnn3.pxd

+cimport cython
+
+cdef double rand(double a, double b, random=*)
+
+@cython.locals(i=Py_ssize_t)
+cdef list makeMatrix(Py_ssize_t I, Py_ssize_t J, fill=*)
+
+cdef class NN:
+    cdef Py_ssize_t ni, nh, no
+    cdef list ai, ah, ao
+    cdef list wi, wo
+    cdef list ci, co
+
+    @cython.locals(i=Py_ssize_t, j=Py_ssize_t, k=Py_ssize_t)
+    cpdef update(self, list inputs)
+
+    @cython.locals(i=Py_ssize_t, j=Py_ssize_t, k=Py_ssize_t, change=double)
+    cpdef double backPropagate(self, list targets, double N, M)
+
+    @cython.locals(i=Py_ssize_t, p=list, error=double)
+    cpdef train(self, list patterns, Py_ssize_t iterations=*, double N=*, M=*)

Demos/benchmarks/bpnn3.py

@@ -15,8 +15,8 @@ import util
 random.seed(0)
 
 # calculate a random number where:  a <= rand < b
-def rand(a, b):
-        return (b-a)*random.random() + a
+def rand(a, b, random=random.random):
+        return (b-a)*random() + a
 
 # Make a matrix (we could use NumPy to speed this up)
 def makeMatrix(I, J, fill=0.0):