# HG changeset patch
# User David Warde-Farley <wardefar@iro.umontreal.ca>
# Date 1285948504 14400
# Node ID b9bc9d5a08cc5e1838bef823173e7fcc2c9d61cf
# Parent  7af2a89bff3d16f1e51f83f11d6fee06c66f58e5
Some language fixes (don't capitalize 'variable', changement->change.

diff -r 7af2a89bff3d -r b9bc9d5a08cc pylearn/formulas/noise.py
--- a/pylearn/formulas/noise.py	Fri Oct 01 11:47:53 2010 -0400
+++ b/pylearn/formulas/noise.py	Fri Oct 01 11:55:04 2010 -0400
@@ -2,8 +2,8 @@
 
 This script define the different symbolic noise functions.
 The noise contract is simple: noise_lvl is a symbolic variable going from 0 to 1.
-0: no changement.
-1: max noise.
+0: no change.
+1: maximum noise.
 """
 import theano
 from tags import tags
@@ -21,12 +21,13 @@
 
 @tags('noise','binomial','salt')
 def binomial_noise(theano_rng,inp,noise_lvl):
-    """ This add binomial noise to inp. Only the salt part of pepper and salt.
+    """
+    This add binomial noise to inp. Only the salt part of pepper and salt.
 
-    :type inp: Theano Variable
+    :type inp: Theano variable
     :param inp: The input that we want to add noise
     :type noise_lvl: float
-    :param noise_lvl: The % of noise. Between 0(no noise) and 1.
+    :param noise_lvl: The %% of noise. Between 0 (no noise) and 1.
     """
     return theano_rng.binomial( size = inp.shape, n = 1, p =  1 - noise_lvl, dtype=theano.config.floatX) * inp
 
@@ -34,11 +35,11 @@
 @tags('noise','binomial NLP','pepper','salt')
 def pepper_and_salt_noise(theano_rng,inp,noise_lvl):
     """ This add pepper and salt noise to inp
-    
-    :type inp: Theano Variable
+
+    :type inp: Theano variable
     :param inp: The input that we want to add noise
     :type noise_lvl: tuple(float,float)
-    :param noise_lvl: The % of noise for the salt and pepper. Between 0(no noise) and 1.
+    :param noise_lvl: The %% of noise for the salt and pepper. Between 0 (no noise) and 1.
     """
     return theano_rng.binomial( size = inp.shape, n = 1, p =  1 - noise_lvl[0], dtype=theano.config.floatX) * inp \
                         + (inp==0) * theano_rng.binomial( size = inp.shape, n = 1, p =  noise_lvl[1], dtype=theano.config.floatX)
@@ -47,7 +48,7 @@
 def gaussian_noise(theano_rng,inp,noise_lvl):
     """ This add gaussian NLP noise to inp
 
-    :type inp: Theano Variable
+    :type inp: Theano variable
     :param inp: The input that we want to add noise
     :type noise_lvl: float
     :param noise_lvl: The standard deviation of the gaussian.