restore to unoptimized version

Author: Codeflash Bot

code_to_optimize/discrete_riccati.py

@@ -1,4 +1,5 @@
-"""Utility functions used in CompEcon
+"""
+Utility functions used in CompEcon
 
 Based routines found in the CompEcon toolbox by Miranda and Fackler.
 
@@ -8,15 +9,13 @@
 and Finance, MIT Press, 2002.
 
 """
-
 from functools import reduce
-
 import numpy as np
-from numba import njit
 
 
 def ckron(*arrays):
-    """Repeatedly applies the np.kron function to an arbitrary number of
+    """
+    Repeatedly applies the np.kron function to an arbitrary number of
     input arrays
 
     Parameters
@@ -43,7 +42,8 @@ def ckron(*arrays):
 
 
 def gridmake(*arrays):
-    """Expands one or more vectors (or matrices) into a matrix where rows span the
+    """
+    Expands one or more vectors (or matrices) into a matrix where rows span the
     cartesian product of combinations of the input arrays. Each column of the
     input arrays will correspond to one column of the output matrix.
 
@@ -78,12 +78,13 @@ def gridmake(*arrays):
                 out = _gridmake2(out, arr)
 
         return out
-    raise NotImplementedError("Come back here")
+    else:
+        raise NotImplementedError("Come back here")
 
 
-@njit(cache=True, fastmath=True)
 def _gridmake2(x1, x2):
-    """Expands two vectors (or matrices) into a matrix where rows span the
+    """
+    Expands two vectors (or matrices) into a matrix where rows span the
     cartesian product of combinations of the input arrays. Each column of the
     input arrays will correspond to one column of the output matrix.
 
@@ -112,23 +113,11 @@ def _gridmake2(x1, x2):
 
     """
     if x1.ndim == 1 and x2.ndim == 1:
-        n1 = x1.shape[0]
-        n2 = x2.shape[0]
-        out = np.empty((n1 * n2, 2), dtype=x1.dtype)
-        for i in range(n2):
-            for j in range(n1):
-                out[i * n1 + j, 0] = x1[j]
-                out[i * n1 + j, 1] = x2[i]
-        return out
-    if x1.ndim > 1 and x2.ndim == 1:
-        n1 = x1.shape[0]
-        n2 = x2.shape[0]
-        ncol1 = x1.shape[1]
-        out = np.empty((n1 * n2, ncol1 + 1), dtype=x1.dtype)
-        for i in range(n2):
-            for j in range(n1):
-                for k in range(ncol1):
-                    out[i * n1 + j, k] = x1[j, k]
-                out[i * n1 + j, ncol1] = x2[i]
-        return out
-    raise NotImplementedError("Come back here")
+        return np.column_stack([np.tile(x1, x2.shape[0]),
+                               np.repeat(x2, x1.shape[0])])
+    elif x1.ndim > 1 and x2.ndim == 1:
+        first = np.tile(x1, (x2.shape[0], 1))
+        second = np.repeat(x2, x1.shape[0])
+        return np.column_stack([first, second])
+    else:
+        raise NotImplementedError("Come back here")