fix the loop exercice solution.

doc/tutorial/loop_solution_1.py

@@ -2,17 +2,15 @@
 # Theano tutorial
 # Solution to Exercise in section 'Loop'
 
-"""
-
-# 1. First example (runs satisfactorily)
+# 1. First example
 
 import theano
-import theano.tensor as T
+import theano.tensor as tensor
 
 theano.config.warn.subtensor_merge_bug = False
 
-k = T.iscalar("k")
-A = T.vector("A")
+k = tensor.iscalar("k")
+A = tensor.vector("A")
 
 
 def inner_fct(prior_result, A):
@@ -20,76 +18,72 @@ def inner_fct(prior_result, A):
 
 # Symbolic description of the result
 result, updates = theano.scan(fn=inner_fct,
-                            outputs_info=T.ones_like(A),
-                            non_sequences=A, n_steps=k)
+                              outputs_info=tensor.ones_like(A),
+                              non_sequences=A, n_steps=k)
 
 # Scan has provided us with A ** 1 through A ** k.  Keep only the last
 # value. Scan notices this and does not waste memory saving them.
 final_result = result[-1]
 
 power = theano.function(inputs=[A, k], outputs=final_result,
-                      updates=updates)
+                        updates=updates)
 
 print power(range(10), 2)
 # [  0.   1.   4.   9.  16.  25.  36.  49.  64.  81.]
 
 
-# 2. Second example (runs satisfactorily)
+# 2. Second example
 
 import numpy
 import theano
-import theano.tensor as T
+import theano.tensor as tensor
 
 coefficients = theano.tensor.vector("coefficients")
-x = T.scalar("x")
+x = tensor.scalar("x")
 max_coefficients_supported = 10000
 
 # Generate the components of the polynomial
 full_range = theano.tensor.arange(max_coefficients_supported)
 components, updates = theano.scan(fn=lambda coeff, power, free_var:
-                                   coeff * (free_var ** power),
-                                outputs_info=None,
-                                sequences=[coefficients, full_range],
-                                non_sequences=x)
+                                  coeff * (free_var ** power),
+                                  sequences=[coefficients, full_range],
+                                  outputs_info=None,
+                                  non_sequences=x)
 polynomial = components.sum()
 calculate_polynomial1 = theano.function(inputs=[coefficients, x],
-                                     outputs=polynomial)
+                                        outputs=polynomial)
 
 test_coeff = numpy.asarray([1, 0, 2], dtype=numpy.float32)
 print calculate_polynomial1(test_coeff, 3)
 # 19.0
-"""
-
 
 # 3. Reduction performed inside scan
 
-# TODO: repair this code: yields 56.0 instead of 19.0
-
 import numpy
 import theano
-import theano.tensor as T
+import theano.tensor as tensor
 
 theano.config.warn.subtensor_merge_bug = False
 
 coefficients = theano.tensor.vector("coefficients")
-x = T.scalar("x")
+x = tensor.scalar("x")
 max_coefficients_supported = 10000
 
 # Generate the components of the polynomial
 full_range = theano.tensor.arange(max_coefficients_supported)
 
 
-outputs_info = T.as_tensor_variable(numpy.asarray(0, 'float64'))
+outputs_info = tensor.as_tensor_variable(numpy.asarray(0, 'float64'))
 
-components, updates = theano.scan(fn=lambda prior_value, coeff, power, free_var:
-                                 prior_value + (coeff * (free_var ** power)),
-                                 outputs_info=outputs_info,
-                                 sequences=[coefficients, full_range],
-                                 non_sequences=x)
+components, updates = theano.scan(fn=lambda coeff, power, prior_value, free_var:
+                                  prior_value + (coeff * (free_var ** power)),
+                                  sequences=[coefficients, full_range],
+                                  outputs_info=outputs_info,
+                                  non_sequences=x)
 
 polynomial = components[-1]
 calculate_polynomial = theano.function(inputs=[coefficients, x],
-                                     outputs=polynomial, updates=updates)
+                                       outputs=polynomial, updates=updates)
 
 test_coeff = numpy.asarray([1, 0, 2], dtype=numpy.float32)
 print calculate_polynomial(test_coeff, 3)