Fixes exponentiation support in auto_diff.

auto_diff/vecvalder.py

@@ -4,6 +4,11 @@
 
 _HANDLED_FUNCS_AND_UFUNCS = {}
 
+def _defer_to_val(f):
+    def fn(self, *args, **kwargs):
+        return getattr(self.val, f)(*args, **kwargs)
+    fn.__name__ = f
+    return fn
 
 class VecValDer(np.lib.mixins.NDArrayOperatorsMixin):
     __slots__ = 'val', 'der'
@@ -25,6 +30,34 @@ def transpose(self, *axes):
             axes = None
         return np.transpose(self, axes)
 
+    all = _defer_to_val('all')
+    any = _defer_to_val('any')
+    argmax = _defer_to_val('argmax')
+    argmin = _defer_to_val('argmin')
+    argpartition = _defer_to_val('argpartition')
+    argsort = _defer_to_val('argsort')
+    nonzero = _defer_to_val('nonzero')
+
+    def copy(self):
+        return VecValDer(self.val.copy(), self.der.copy())
+
+    def fill(self, value):
+        if isinstance(value, VecValDer):
+            self.val.fill(value.val)
+            self.der[:] = value.der
+        else:
+            self.val.fill(value)
+            self.der.fill(0.0)
+
+    def reshape(self, shape):
+        der_dim_shape = self.der.shape[len(self.val.shape):]
+        new_der_shape = shape + der_dim_shape
+        self.val.reshape(shape)
+        self.der.reshape(new_der_shape)
+
+    def trace(self, *args, **kwargs):
+        return np.trace(*args, **kwargs)
+
     def __array_ufunc__(self, ufunc, method, *args, **kwargs):
         if method == '__call__' and ufunc in _HANDLED_FUNCS_AND_UFUNCS:
             return _HANDLED_FUNCS_AND_UFUNCS[ufunc](*args, **kwargs)

auto_diff/vecvalder_funcs_and_ufuncs.py

@@ -43,7 +43,7 @@ def _chain_rule(f_x, dx, out=None):
     if out is None:
         out = np.ndarray(dx.shape)  # Uninitialized memory is fine because
     # we're about to overwrite each element. If we do compression of the for
-    # loop in the future be sure to swtich to np.zeros.
+    # loop in the future be sure to switch to np.zeros.
     for index, y in np.ndenumerate(f_x):
         out[index] = y * dx[index]
     return out
@@ -284,25 +284,33 @@ def true_divide(x1, x2, /, out):
         raise RuntimeError("This should not be occuring.")
 
 
-# Tested
 @register(np.float_power)
 @_add_out_support
-def float_power(x1, x2):
+def float_power(x1, x2, /, out):
     if isinstance(x1, cls) and isinstance(x2, cls):
-        return cls(x1.val ** x2.val, x1.val**(x2.val - 1) * (
-            x2.val * x1.der + x1.val * np.log(x1.val) * x2.der))
+        return cls(np.float_power(x1.val, x2.val, out=out.val),
+                np.multiply(x1.val**(x2.val - 1), (x2.val * x1.der + x1.val * np.log(x1.val) * x2.der), out=out.der))
     elif isinstance(x1, cls):
-        return cls(x1.val ** x2, x1.val**(x2 - 1) * x2 * x1.der)
+        return cls(np.float_power(x1.val, x2, out=out.val), np.multiply(x1.val**(x2 - 1) * x2, x1.der, out=out.der))
     elif isinstance(x2, cls):
-        return cls(x1.val ** x2.val, x1**(x2.val) * np.log(x1.val) * x2.der)
+        return cls(np.float_power(x1, x2.val, out=out.val), np.multiply(x1**(x2.val) * np.log(x1.val), x2.der, out=out.der))
     else:
         raise RuntimeError("This should not be occuring.")
 
 
 @register(np.power)
 @_add_out_support
-def power(x1, x2):
-    return float_power(x1, x2)
+def power(x1, x2, /, out):
+    if isinstance(x1, cls) and isinstance(x2, cls):
+        return cls(np.power(x1.val, x2.val, out=out.val),
+                np.multiply(x1.val**(x2.val - 1), (x2.val * x1.der + x1.val * np.log(x1.val) * x2.der), out=out.der))
+    elif isinstance(x1, cls):
+        return cls(np.power(x1.val, x2, out=out.val), np.multiply(x1.val**(x2 - 1) * x2, x1.der, out=out.der))
+    elif isinstance(x2, cls):
+        return cls(np.power(x1, x2.val, out=out.val), np.multiply(x1**(x2.val) * np.log(x1.val), x2.der, out=out.der))
+    else:
+        raise RuntimeError("This should not be occuring.")
+
 
 
 # Partially Tested