some cython improvments for stationary kerns

GPy/kern/_src/stationary_cython.pyx

@@ -1,7 +1,9 @@
 #cython: boundscheck=False
+#cython: nonecheck=False
 #cython: wraparound=False
 import numpy as np
 cimport numpy as np
+from cython.parallel import prange
 
 ctypedef np.float64_t DTYPE_t
  
@@ -22,7 +24,18 @@ def grad_X(int N, int D, int M,
     cdef double *grad = <double*> _grad.data
     _grad_X(N, D, M, X, X2, tmp, grad) # return nothing, work in place.
 
-def lengthscale_grads(int N, int M, int Q,
+def grad_X_cython(int N, int D, int M, double[:,:] X, double[:,:] X2, double[:,:] tmp, double[:,:] grad):
+    cdef int n,d,nd,m
+    for nd in prange(N*D, nogil=True):
+        n = nd/D
+        d = nd%D
+        grad[n,d] = 0.0
+        for m in range(M):
+            grad[n,d] += tmp[n,m]*(X[n,d]-X2[m,d])
+
+
+
+def lengthscale_grads_in_c(int N, int M, int Q,
         np.ndarray[DTYPE_t, ndim=2] _tmp,
         np.ndarray[DTYPE_t, ndim=2] _X,
         np.ndarray[DTYPE_t, ndim=2] _X2,
@@ -33,4 +46,14 @@ def lengthscale_grads(int N, int M, int Q,
     cdef double *grad = <double*> _grad.data
     _lengthscale_grads(N, M, Q, tmp, X, X2, grad) # return nothing, work in place.
 
+def lengthscale_grads(int N, int M, int Q, double[:,:] tmp, double[:,:] X, double[:,:] X2, double[:] grad):
+    cdef int q, n, m
+    cdef double gradq, dist
+    for q in range(Q):
+        grad[q] = 0.0
+        for n in range(N):
+            for m in range(M):
+                dist = X[n,q] - X2[m,q]
+                grad[q] += tmp[n,m]*dist*dist
+