Clear comment, add sanity check, change algorithmic_reverse_nlplike m…

…ax_length and add __pycache__ entry in .gitignore

.gitignore

@@ -1,5 +1,7 @@
 # Compiled python modules.
 *.pyc
+# Byte-compiled
+__pycache__/
 
 # Python egg metadata, regenerated from source files by setuptools.
 /*.egg-info

tensor2tensor/bin/t2t-datagen

@@ -87,15 +87,15 @@ _SUPPORTED_PROBLEM_GENERATORS = {
         lambda: algorithmic.multiplication_generator(10, 40, 100000),
         lambda: algorithmic.multiplication_generator(10, 400, 10000)),
     "algorithmic_reverse_nlplike_decimal8K": (
-        lambda: algorithmic.reverse_generator_nlplike(8000, 40, 100000,
-                                                      10, 1.250),
-        lambda: algorithmic.reverse_generator_nlplike(8000, 400, 10000,
-                                                      10, 1.250)),
+        lambda: algorithmic.reverse_generator_nlplike(8000, 70, 100000,
+                                                      10, 1.300),
+        lambda: algorithmic.reverse_generator_nlplike(8000, 700, 10000,
+                                                      10, 1.300)),
     "algorithmic_reverse_nlplike_decimal32K": (
-        lambda: algorithmic.reverse_generator_nlplike(32000, 40, 100000,
-                                                      10, 1.005),
-        lambda: algorithmic.reverse_generator_nlplike(32000, 400, 10000,
-                                                      10, 1.005)),
+        lambda: algorithmic.reverse_generator_nlplike(32000, 70, 100000,
+                                                      10, 1.050),
+        lambda: algorithmic.reverse_generator_nlplike(32000, 700, 10000,
+                                                      10, 1.050)),
     "algorithmic_algebra_inverse": (
         lambda: algorithmic_math.algebra_inverse(26, 0, 2, 100000),
         lambda: algorithmic_math.algebra_inverse(26, 3, 3, 10000)),

tensor2tensor/data_generators/algorithmic.py

@@ -123,7 +123,12 @@ def zipf_random_sample(distr_map, sample_len):
 
   """
   u = np.random.random(sample_len)
-  return [t+1 for t in np.searchsorted(distr_map, u)] # 0 pad and 1 EOS
+  # Random produces values in range [0.0,1.0); even if it is almost
+  # improbable(but possible) that it can generate a clear 0.000..0,
+  # we have made a sanity check to overcome this issue. On the other hand,
+  # t+1 is enough from saving us to generate PAD(0) and EOS(1) which are
+  # reservated symbols.
+  return [t+1 if t > 0 else t+2 for t in np.searchsorted(distr_map, u)]
 
 
 def reverse_generator_nlplike(nbr_symbols, max_length, nbr_cases, \