From 87a3e9a0244469dec92d01e9e5e61c8f40a2deaf Mon Sep 17 00:00:00 2001
From: Antonis Maronikolakis <antmarakis@programmers.gr>
Date: Wed, 8 Mar 2017 21:12:01 +0200
Subject: [PATCH] "epoches" to "epochs"

---
 learning.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/learning.py b/learning.py
index df5d6fce3..8345c1b58 100644
--- a/learning.py
+++ b/learning.py
@@ -438,12 +438,12 @@ def predict(example):
 
 
 def NeuralNetLearner(dataset, hidden_layer_sizes=[3],
-                     learning_rate=0.01, epoches=100):
+                     learning_rate=0.01, epochs=100):
     """
     Layered feed-forward network.
     hidden_layer_sizes: List of number of hidden units per hidden layer
     learning_rate: Learning rate of gradient descent
-    epoches: Number of passes over the dataset
+    epochs: Number of passes over the dataset
     """
 
     i_units = len(dataset.inputs)
@@ -452,7 +452,7 @@ def NeuralNetLearner(dataset, hidden_layer_sizes=[3],
     # construct a network
     raw_net = network(i_units, hidden_layer_sizes, o_units)
     learned_net = BackPropagationLearner(dataset, raw_net,
-                                         learning_rate, epoches)
+                                         learning_rate, epochs)
 
     def predict(example):
 
@@ -517,7 +517,7 @@ def network(input_units, hidden_layer_sizes, output_units):
     return net
 
 
-def BackPropagationLearner(dataset, net, learning_rate, epoches):
+def BackPropagationLearner(dataset, net, learning_rate, epochs):
     """[Figure 18.23] The back-propagation algorithm for multilayer network"""
     # Initialise weights
     for layer in net:
@@ -537,7 +537,7 @@ def BackPropagationLearner(dataset, net, learning_rate, epoches):
     o_nodes = net[-1]
     i_nodes = net[0]
 
-    for epoch in range(epoches):
+    for epoch in range(epochs):
         # Iterate over each example
         for e in examples:
             i_val = [e[i] for i in idx_i]
@@ -590,13 +590,13 @@ def BackPropagationLearner(dataset, net, learning_rate, epoches):
     return net
 
 
-def PerceptronLearner(dataset, learning_rate=0.01, epoches=100):
+def PerceptronLearner(dataset, learning_rate=0.01, epochs=100):
     """Logistic Regression, NO hidden layer"""
     i_units = len(dataset.inputs)
     o_units = 1  # As of now, dataset.target gives only one index.
     hidden_layer_sizes = []
     raw_net = network(i_units, hidden_layer_sizes, o_units)
-    learned_net = BackPropagationLearner(dataset, raw_net, learning_rate, epoches)
+    learned_net = BackPropagationLearner(dataset, raw_net, learning_rate, epochs)
 
     def predict(example):
         # Input nodes