Merge branch 'master' into 3.2rc1

pymc-devs · Oct 4, 2017 · 6324129 · 6324129
2 parents 1de0f86 + c7c86a0
commit 6324129
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Showing 2 changed files with 15 additions and 14 deletions.
diff --git a/docs/source/notebooks/GP-MeansAndCovs.ipynb b/docs/source/notebooks/GP-MeansAndCovs.ipynb
@@ -1045,22 +1045,22 @@
     }
    ],
    "source": [
-    "def tanh_func(x, x1, x2, w, x0):\n",
+    "def tanh_func(x, ls1, ls2, w, x0):\n",
     "    \"\"\"\n",
-    "    l1: Left saturation value\n",
-    "    l2: Right saturation value\n",
-    "    lw: Transition width\n",
-    "    x0: Transition location.\n",
+    "    ls1: left saturation value\n",
+    "    ls2: right saturation value\n",
+    "    w:   transition width\n",
+    "    x0:  transition location.\n",
     "    \"\"\"\n",
-    "    return (x1 + x2) / 2.0 - (x1 - x2) / 2.0 * tt.tanh((x - x0) / w)\n",
+    "    return (ls1 + ls2) / 2.0 - (ls1 - ls2) / 2.0 * tt.tanh((x - x0) / w)\n",
     "\n",
     "ls1 = 0.05\n",
     "ls2 = 0.6\n",
-    "lw = 0.3\n",
+    "w = 0.3\n",
     "x0 = 1.0\n",
-    "cov = pm.gp.cov.Gibbs(1, tanh_func, args=(ls1, ls2, lw, x0))\n",
+    "cov = pm.gp.cov.Gibbs(1, tanh_func, args=(ls1, ls2, w, x0))\n",
     "    \n",
-    "wf = theano.function([], tanh_func(X, ls1, ls2, lw, x0))()\n",
+    "wf = theano.function([], tanh_func(X, ls1, ls2, w, x0))()\n",
     "plt.plot(X, wf); plt.ylabel(\"tanh_func(X)\"); plt.xlabel(\"X\"); plt.title(\"Lengthscale as a function of X\");\n",
     "\n",
     "K = cov(X).eval()\n",

diff --git a/pymc3/gp/cov.py b/pymc3/gp/cov.py
@@ -490,15 +490,16 @@ def square_dist(self, X, Xs=None):
         return tt.clip(sqd, 0.0, np.inf)
 
     def full(self, X, Xs=None):
-        X, Xs = self._slice(X, Xs)
-        rx = self.lfunc(X, self.args)
-        rx2 = tt.reshape(tt.square(rx), (-1, 1))
+        rx = self.lfunc(tt.as_tensor_variable(X), self.args)
         if Xs is None:
+            rz = self.lfunc(tt.as_tensor_variable(X), self.args)
+            X, Xs = self._slice(X, Xs)
             r2 = self.square_dist(X, X)
-            rz = self.lfunc(X, self.args)
         else:
+            rz = self.lfunc(tt.as_tensor_variable(Xs), self.args)
+            X, Xs = self._slice(X, Xs)
             r2 = self.square_dist(X, Xs)
-            rz = self.lfunc(Xs, self.args)
+        rx2 = tt.reshape(tt.square(rx), (-1, 1))
         rz2 = tt.reshape(tt.square(rz), (1, -1))
         return (tt.sqrt((2.0 * tt.outer(rx, rz)) / (rx2 + rz2))
                 * tt.exp(-1.0 * r2 / (rx2 + rz2)))