Added sys.path manipulation to make imports from ex2 easier

ex3/ex3.py

@@ -2,6 +2,8 @@
 import numpy as np
 from matplotlib import use
 use('TkAgg')
+import sys
+sys.path.append('../ex2/')
 
 from oneVsAll import oneVsAll
 from predictOneVsAll import predictOneVsAll
@@ -13,7 +15,7 @@
 # 
 #  This file contains code that helps you get started on the
 #  linear exercise. You will need to complete the following functions 
-#  in this exericse:
+#  in this exercise:
 #
 #     lrCostFunction.m (logistic regression cost function)
 #     oneVsAll.m
@@ -37,7 +39,7 @@
 # Load Training Data
 print('Loading and Visualizing Data ...')
 
-data = scipy.io.loadmat('ex3data1.mat') # training data stored in arrays X, y
+data = scipy.io.loadmat('ex3data1.mat')  # training data stored in arrays X, y
 X = data['X']
 y = data['y']
 m, _ = X.shape
@@ -68,7 +70,6 @@
 #  After ...
 
 pred = predictOneVsAll(all_theta, X)
-
 accuracy = np.mean(np.double(pred == np.squeeze(y))) * 100
 print('\nTraining Set Accuracy: %f\n' % accuracy)
 

ex3/ex3_nn.py

@@ -3,6 +3,8 @@
 import scipy.io
 import numpy as np
 import matplotlib.pyplot as plt
+import sys
+sys.path.append('../ex2/')
 
 from displayData import displayData
 from predict import predict

ex3/submit.py

@@ -1,4 +1,6 @@
 import numpy as np
+import sys
+sys.path.append('../ex2/')
 
 from Submission import Submission
 from Submission import sprintf
@@ -44,7 +46,7 @@ def output(part_id):
     t2 = np.cos(np.array(range(1, 40, 2)).reshape(5, 4).T)
 
     fname = srcs[part_id - 1].rsplit('.', 1)[0]
-    mod = __import__(fname, fromlist=[fname], level=1)
+    mod = __import__(fname, fromlist=[fname], level=0)
     func = getattr(mod, fname)
 
     if part_id == 1: