pep8

doc/tutorial/loop.txt

@@ -36,12 +36,12 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
   W = T.matrix("W")
   b_sym = T.vector("b_sym")
   
-  results, updates = theano.scan(lambda v:T.tanh(T.dot(v,W)+b_sym), sequences=X)
+  results, updates = theano.scan(lambda v:T.tanh(T.dot(v, W)+b_sym), sequences=X)
   compute_elementwise = theano.function(inputs = [X, W, b_sym], outputs=[results])
   
   # test values
   x = np.eye(2)
-  w = np.ones((2,2))
+  w = np.ones((2, 2))
   b = np.ones((2))
   b[1] = 2
   
@@ -68,27 +68,27 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
   P = T.matrix("P")
   
   results, updates = theano.scan(lambda
-            y,p,x_tm1:T.tanh(T.dot(x_tm1,W)+T.dot(y,U)+T.dot(p,V)), 
-            sequences=[Y,P[::-1]], outputs_info=[X])
+            y,p,x_tm1:T.tanh(T.dot(x_tm1, W)+T.dot(y, U)+T.dot(p, V)),
+            sequences=[Y, P[::-1]], outputs_info=[X])
   compute_seq = theano.function(inputs = [X, W, Y, U, P, V], outputs=[results])
   
   # test values
   x = np.zeros((2))
   x[1] = 1
-  w = np.ones((2,2))
-  y = np.ones((5,2))
-  y[0,:] = -3
-  u = np.ones((2,2))
-  p = np.ones((5,2))
-  p[0,:] = 3
-  v = np.ones((2,2))
+  w = np.ones((2, 2))
+  y = np.ones((5, 2))
+  y[0, :] = -3
+  u = np.ones((2, 2))
+  p = np.ones((5, 2))
+  p[0, :] = 3
+  v = np.ones((2, 2))
   
-  print compute_seq(x,w,y,u,p,v)[0]
+  print compute_seq(x, w, y, u, p, v)[0]
   
   # comparison with numpy
-  x_res = np.zeros((5,2))
+  x_res = np.zeros((5, 2))
   x_res[0] = np.tanh(x.dot(w) + y[0].dot(u) + p[4].dot(v))
-  for i in range(1,5):
+  for i in range(1, 5):
     x_res[i] = np.tanh(x_res[i-1].dot(w) + y[i].dot(u) + p[4-i].dot(v))
 
 **Scan Example: Computing norms of lines of X**
@@ -104,7 +104,7 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
   compute_norm_lines = theano.function(inputs = [X], outputs=[results])
   
   # test value
-  x = np.diag(np.arange(1,6),1)
+  x = np.diag(np.arange(1, 6), 1)
   print compute_norm_lines(x)[0]
   
   # comparison with numpy
@@ -123,7 +123,7 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
   compute_norm_cols = theano.function(inputs = [X], outputs=[results])
   
   # test value
-  x = np.diag(np.arange(1,6),1)
+  x = np.diag(np.arange(1, 6), 1)
   print compute_norm_cols(x)[0]
   
   # comparison with numpy
@@ -139,7 +139,7 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
   
   # define tensor variable
   X = T.matrix("X")
-  results, updates = theano.scan(lambda i, j, t_f:T.cast(X[i,j]+t_f, floatX), \
+  results, updates = theano.scan(lambda i, j, t_f:T.cast(X[i, j]+t_f, floatX), \
                     sequences=[T.arange(X.shape[0]), T.arange(X.shape[1])], \
                     outputs_info=np.asarray(0., dtype=floatX))
   result = results[-1]
@@ -168,29 +168,29 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
   V = T.matrix("V")
   n_sym = T.iscalar("n_sym")
 
-  results, updates = theano.scan(lambda x_tm2,x_tm1:T.dot(x_tm2,U) + T.dot(x_tm1,V) \
-                                + T.tanh(T.dot(x_tm1,W) + b_sym), \
-                      n_steps=n_sym, outputs_info=[dict(initial = X, taps = [-2,-1])])
+  results, updates = theano.scan(lambda x_tm2, x_tm1:T.dot(x_tm2, U) + T.dot(x_tm1, V) \
+                                + T.tanh(T.dot(x_tm1, W) + b_sym), \
+                      n_steps=n_sym, outputs_info=[dict(initial = X, taps = [-2, -1])])
   compute_seq2 = theano.function(inputs = [X, U, V, W, b_sym, n_sym], outputs=[results])
   
   # test values
-  x = np.zeros((2,2)) # the initial value must be able to return x[-2]
-  x[1,1] = 1
-  w = 0.5*np.ones((2,2))
-  u = 0.5*(np.ones((2,2))-np.eye(2))
-  v = 0.5*np.ones((2,2))
+  x = np.zeros((2, 2)) # the initial value must be able to return x[-2]
+  x[1, 1] = 1
+  w = 0.5*np.ones((2, 2))
+  u = 0.5*(np.ones((2, 2))-np.eye(2))
+  v = 0.5*np.ones((2, 2))
   n = 10
   b = np.ones((2))
   
-  print compute_seq2(x,u,v,w,b,n)
+  print compute_seq2(x, u, v, w, b, n)
   
   # comparison with numpy
-  x_res = numpy.zeros((10,2))
+  x_res = numpy.zeros((10, 2))
   x_res[0] = x[0].dot(u) + x[1].dot(v) + numpy.tanh(x[1].dot(w) + b)
   x_res[1] = x[1].dot(u) + x_res[0].dot(v) + numpy.tanh(x_res[0].dot(w) + b)
   x_res[2] = x_res[0].dot(u) + x_res[1].dot(v) \
             + numpy.tanh(x_res[1].dot(w) + b)
-  for i in range(2,10):
+  for i in range(2, 10):
     x_res[i] = (x_res[i-2].dot(u) + x_res[i-1].dot(v) \
             + numpy.tanh(x_res[i-1].dot(w) + b))
 
@@ -204,15 +204,15 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
   # define tensor variables
   v = T.vector()
   A = T.matrix()
-  y = T.tanh(T.dot(v,A))
+  y = T.tanh(T.dot(v, A))
   results, updates = theano.scan(lambda i:T.grad(y[i], v), sequences = [T.arange(y.shape[0])])
-  compute_jac_t = theano.function([A,v], [results], allow_input_downcast = True) # shape (d_out, d_in)
+  compute_jac_t = theano.function([A, v], [results], allow_input_downcast = True) # shape (d_out, d_in)
   
   # test values
   x = np.eye(5)[0]
-  w = np.eye(5,3)
+  w = np.eye(5, 3)
   w[2] = np.ones((3))
-  print compute_jac_t(w,x)[0]
+  print compute_jac_t(w, x)[0]
   
   # compare with numpy
   print ((1 - np.tanh(x.dot(w))**2)*w).T
@@ -253,18 +253,18 @@ Note that we need to iterate over the indices of ``y`` and not over the elements
   trng = T.shared_randomstreams.RandomStreams(1234)
   d=trng.binomial(size=W[1].shape)
   
-  results, updates = theano.scan(lambda v:T.tanh(T.dot(v,W)+b_sym)*d, sequences=X)
+  results, updates = theano.scan(lambda v:T.tanh(T.dot(v, W)+b_sym)*d, sequences=X)
   compute_with_bnoise = theano.function(inputs = [X, W, b_sym], outputs=[results], \
                             updates=updates, allow_input_downcast = True)
-  x = np.eye(10,2)
-  w = np.ones((2,2))
+  x = np.eye(10, 2)
+  w = np.ones((2, 2))
   b = np.ones((2))
   
   print compute_with_bnoise(x, w, b)
 
 Note that if you want to use a random variable ``d`` that will not be updated through scan loops, you should pass this variable as a ``non_sequences`` arguments. 
 
-**Scan Example: Computing pow(A,k)**
+**Scan Example: Computing pow(A, k)**
 
 .. code-block:: python
 
@@ -290,7 +290,7 @@ Note that if you want to use a random variable ``d`` that will not be updated th
   power = theano.function(inputs=[A, k], outputs=final_result,
                         updates=updates)
   
-  print power(range(10),2)
+  print power(range(10), 2)
   #[  0.   1.   4.   9.  16.  25.  36.  49.  64.  81.]