Get rid of floatX_array

pymc/pytensorf.py

@@ -715,10 +715,6 @@ def generator(gen, default=None):
     return GeneratorOp(gen, default)()
 
 
-def floatX_array(x):
-    return floatX(np.array(x))
-
-
 def ix_(*args):
     """
     PyTensor np.ix_ analog

tests/models.py

@@ -23,14 +23,13 @@
 import pymc as pm
 
 from pymc import Categorical, Metropolis, Model, Normal
-from pymc.pytensorf import floatX_array
 
 
 def simple_model():
     mu = -2.1
     tau = 1.3
     with Model() as model:
-        Normal("x", mu, tau=tau, size=2, initval=floatX_array([0.1, 0.1]))
+        Normal("x", mu, tau=tau, size=2, initval=np.array([0.1, 0.1]))
 
     return model.initial_point(), model, (mu, tau**-0.5)
 
@@ -43,8 +42,8 @@ def another_simple_model():
 
 
 def simple_categorical():
-    p = floatX_array([0.1, 0.2, 0.3, 0.4])
-    v = floatX_array([0.0, 1.0, 2.0, 3.0])
+    p = np.array([0.1, 0.2, 0.3, 0.4])
+    v = np.array([0.0, 1.0, 2.0, 3.0])
     with Model() as model:
         Categorical("x", p, size=3, initval=[1, 2, 3])
 
@@ -72,7 +71,7 @@ def arbitrary_det(value):
     with Model() as model:
         a = Normal("a")
         b = arbitrary_det(a)
-        Normal("obs", mu=b.astype("float64"), observed=floatX_array([1, 3, 5]))
+        Normal("obs", mu=b.astype("float64"), observed=np.array([1, 3, 5], dtype="float64"))
 
     return model.initial_point(), model
 
@@ -105,47 +104,47 @@ def simple_2model_continuous():
 
 
 def mv_simple():
-    mu = floatX_array([-0.1, 0.5, 1.1])
-    p = floatX_array([[2.0, 0, 0], [0.05, 0.1, 0], [1.0, -0.05, 5.5]])
+    mu = np.array([-0.1, 0.5, 1.1])
+    p = np.array([[2.0, 0, 0], [0.05, 0.1, 0], [1.0, -0.05, 5.5]])
     tau = np.dot(p, p.T)
     with pm.Model() as model:
         pm.MvNormal(
             "x",
             pt.constant(mu),
             tau=pt.constant(tau),
-            initval=floatX_array([0.1, 1.0, 0.8]),
+            initval=np.array([0.1, 1.0, 0.8]),
         )
     H = tau
     C = np.linalg.inv(H)
     return model.initial_point(), model, (mu, C)
 
 
 def mv_simple_coarse():
-    mu = floatX_array([-0.2, 0.6, 1.2])
-    p = floatX_array([[2.0, 0, 0], [0.05, 0.1, 0], [1.0, -0.05, 5.5]])
+    mu = np.array([-0.2, 0.6, 1.2])
+    p = np.array([[2.0, 0, 0], [0.05, 0.1, 0], [1.0, -0.05, 5.5]])
     tau = np.dot(p, p.T)
     with pm.Model() as model:
         pm.MvNormal(
             "x",
             pt.constant(mu),
             tau=pt.constant(tau),
-            initval=floatX_array([0.1, 1.0, 0.8]),
+            initval=np.array([0.1, 1.0, 0.8]),
         )
     H = tau
     C = np.linalg.inv(H)
     return model.initial_point(), model, (mu, C)
 
 
 def mv_simple_very_coarse():
-    mu = floatX_array([-0.3, 0.7, 1.3])
-    p = floatX_array([[2.0, 0, 0], [0.05, 0.1, 0], [1.0, -0.05, 5.5]])
+    mu = np.array([-0.3, 0.7, 1.3])
+    p = np.array([[2.0, 0, 0], [0.05, 0.1, 0], [1.0, -0.05, 5.5]])
     tau = np.dot(p, p.T)
     with pm.Model() as model:
         pm.MvNormal(
             "x",
             pt.constant(mu),
             tau=pt.constant(tau),
-            initval=floatX_array([0.1, 1.0, 0.8]),
+            initval=np.array([0.1, 1.0, 0.8]),
         )
     H = tau
     C = np.linalg.inv(H)
@@ -155,7 +154,7 @@ def mv_simple_very_coarse():
 def mv_simple_discrete():
     d = 2
     n = 5
-    p = floatX_array([0.15, 0.85])
+    p = np.array([0.15, 0.85])
     with pm.Model() as model:
         pm.Multinomial("x", n, pt.constant(p), initval=np.array([1, 4]))
         mu = n * p