Minor fixes

ex2/costFunction.py

@@ -1,4 +1,4 @@
-from numpy import log
+import numpy as np
 from sigmoid import sigmoid
 
 
@@ -10,13 +10,13 @@ def costFunction(theta, X, y):
     # Initialize some useful values
     m = y.size  # number of training examples
 
+    # ====================== YOUR CODE HERE ======================
+    # Instructions: Compute the cost of a particular choice of theta.
+    #               You should set J to the cost.
+    #               Compute the partial derivatives and set grad to the partial
+    #               derivatives of the cost w.r.t. each parameter in theta
+    #
+    # Note: grad should have the same dimensions as theta
+    # =============================================================
 
-# ====================== YOUR CODE HERE ======================
-# Instructions: Compute the cost of a particular choice of theta.
-#               You should set J to the cost.
-#               Compute the partial derivatives and set grad to the partial
-#               derivatives of the cost w.r.t. each parameter in theta
-#
-# Note: grad should have the same dimensions as theta
-# =============================================================
     return J

ex2/costFunctionReg.py

@@ -1,3 +1,4 @@
+import numpy as np
 from costFunction import costFunction
 
 
@@ -11,11 +12,11 @@ def costFunctionReg(theta, X, y, Lambda):
     # Initialize some useful values
     m = len(y)   # number of training examples
 
-# ====================== YOUR CODE HERE ======================
-# Instructions: Compute the cost of a particular choice of theta.
-#               You should set J to the cost.
-#               Compute the partial derivatives and set grad to the partial
-#               derivatives of the cost w.r.t. each parameter in theta
-# =============================================================
+    # ====================== YOUR CODE HERE ======================
+    # Instructions: Compute the cost of a particular choice of theta.
+    #               You should set J to the cost.
+    #               Compute the partial derivatives and set grad to the partial
+    #               derivatives of the cost w.r.t. each parameter in theta
+    # =============================================================
 
     return J

ex2/ex2.py

@@ -11,6 +11,8 @@
 from sigmoid import sigmoid
 from predict import predict
 from show import show
+from ml import plotData, plotDecisionBoundary
+
 
 ## Machine Learning Online Class - Exercise 2: Logistic Regression
 #
@@ -20,7 +22,7 @@
 #  This file contains code that helps you get started on the second part
 #  of the exercise which covers regularization with logistic regression.
 #
-#  You will need to complete the following functions in this exericse:
+#  You will need to complete the following functions in this exercise:
 #
 #     sigmoid.py
 #     costFunction.py
@@ -37,7 +39,7 @@
 #  or any other files other than those mentioned above.
 #
 
-from ml import plotData, plotDecisionBoundary
+
 # Load Data
 #  The first two columns contains the exam scores and the third column
 #  contains the label.

ex2/ex2_reg.py

@@ -35,7 +35,6 @@ def plotBoundary(theta, X, y):
     show()
 
 
-
 # Initialization
 
 # Load Data

ex2/gradientFunction.py

@@ -1,5 +1,5 @@
+import numpy as np
 from sigmoid import sigmoid
-from numpy import squeeze, asarray
 
 
 def gradientFunction(theta, X, y):
@@ -12,10 +12,10 @@ def gradientFunction(theta, X, y):
 
     m = len(y)   # number of training examples
 
-# ====================== YOUR CODE HERE ======================
-# Instructions: Compute the gradient of a particular choice of theta.
-#               Compute the partial derivatives and set grad to the partial
-#               derivatives of the cost w.r.t. each parameter in theta
-# =============================================================
+    # ====================== YOUR CODE HERE ======================
+    # Instructions: Compute the gradient of a particular choice of theta.
+    #               Compute the partial derivatives and set grad to the partial
+    #               derivatives of the cost w.r.t. each parameter in theta
+    # =============================================================
 
     return grad

ex2/gradientFunctionReg.py

@@ -1,5 +1,4 @@
-from numpy import asfortranarray, squeeze, asarray
-
+import numpy as np
 from gradientFunction import gradientFunction
 
 
@@ -12,10 +11,10 @@ def gradientFunctionReg(theta, X, y, Lambda):
     """
     m = len(y)   # number of training examples
 
-# ====================== YOUR CODE HERE ======================
-# Instructions: Compute the gradient of a particular choice of theta.
-#               Compute the partial derivatives and set grad to the partial
-#               derivatives of the cost w.r.t. each parameter in theta
-# =============================================================
+    # ====================== YOUR CODE HERE ======================
+    # Instructions: Compute the gradient of a particular choice of theta.
+    #               Compute the partial derivatives and set grad to the partial
+    #               derivatives of the cost w.r.t. each parameter in theta
+    # =============================================================
 
     return grad

ex2/ml.py

@@ -4,7 +4,7 @@
 from mpl_toolkits.mplot3d import axes3d
 
 
-def plotData(X,y):
+def plotData(X, y):
     pos = X[np.where(y == 1, True, False).flatten()]
     neg = X[np.where(y == 0, True, False).flatten()]
     plt.plot(pos[:, 0], pos[:, 1], '+', markersize=7, markeredgecolor='black', markeredgewidth=2)

ex2/predict.py

@@ -1,5 +1,4 @@
-from numpy import round
-
+import numpy as np
 from sigmoid import sigmoid
 
 
@@ -9,11 +8,11 @@ def predict(theta, X):
     (i.e., if sigmoid(theta'*x) >= 0.5, predict 1)
     """
 
-# ====================== YOUR CODE HERE ======================
-# Instructions: Complete the following code to make predictions using
-#               your learned logistic regression parameters.
-#               You should set p to a vector of 0's and 1's
-#
-# =========================================================================
+    # ====================== YOUR CODE HERE ======================
+    # Instructions: Complete the following code to make predictions using
+    #               your learned logistic regression parameters.
+    #               You should set p to a vector of 0's and 1's
+    #
+    # =========================================================================
 
     return p

ex2/sigmoid.py

@@ -1,4 +1,4 @@
-from numpy import e
+import numpy as np
 
 
 def sigmoid(z):

ex2/submit.py

@@ -28,12 +28,12 @@
 
 def output(part_id):
     X = np.column_stack((np.ones(20),
-                          (np.exp(1) * np.sin(np.linspace(1, 20, 20))),
-                          (np.exp(0.5) * np.cos(np.linspace(1, 20, 20)))))
+                        (np.exp(1) * np.sin(np.linspace(1, 20, 20))),
+                        (np.exp(0.5) * np.cos(np.linspace(1, 20, 20)))))
     Y = np.sin(X[:, 0] + X[:, 1]) > 0
 
     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: