--- a/costFunction.m
+++ b/costFunction.m
@@ -1,32 +1,15 @@
 function [J, grad] = costFunction(theta, X, y)
-%COSTFUNCTION Compute cost and gradient for logistic regression
-%   J = COSTFUNCTION(theta, X, y) computes the cost of using theta as the
-%   parameter for logistic regression and the gradient of the cost
-%   w.r.t. to the parameters.
-
-% Initialize some useful values
-m = length(y); % number of training examples
-
-% You need to return the following variables correctly 
-J = 0;
-grad = zeros(size(theta));
+##COSTFUNCTION Compute cost and gradient for logistic regression
+##   J = COSTFUNCTION(theta, X, y) computes the cost of using theta as the
+##   parameter for logistic regression and the gradient of the cost
+##   w.r.t. to the parameters.
 
-% ====================== 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
-%
+  m = length (y);
 
-
-
-
+  ## h_theta(x)
+  ht = sigmoid (X*theta); 
 
-
-
-
-% =============================================================
+  J = -sum (y.*log (ht) + (1 - y).*log (1 - ht))/m
+  grad = X'*(ht - y)/m;
 
 end

--- a/plotData.m
+++ b/plotData.m
@@ -1,28 +1,18 @@
 function plotData(X, y)
-%PLOTDATA Plots the data points X and y into a new figure 
-%   PLOTDATA(x,y) plots the data points with + for the positive examples
-%   and o for the negative examples. X is assumed to be a Mx2 matrix.
+##PLOTDATA Plots the data points X and y into a new figure 
+##   PLOTDATA(x,y) plots the data points with + for the positive examples
+##   and o for the negative examples. X is assumed to be a Mx2 matrix.
 
-% Create New Figure
+## Create New Figure
 figure; hold on;
 
-% ====================== YOUR CODE HERE ======================
-% Instructions: Plot the positive and negative examples on a
-%               2D plot, using the option 'k+' for the positive
-%               examples and 'ko' for the negative examples.
-%
-
-
-
-
-
-
-
-
-
-% =========================================================================
-
-
+## Find Indices of Positive and Negative Examples
+pos = find(y==1); neg = find(y == 0);
+## Plot Examples
+plot (X(pos, 1), X(pos, 2), "k+","LineWidth", 2, 
+      "MarkerSize", 7);
+plot (X(neg, 1), X(neg, 2), "ko", "MarkerFaceColor", 
+      "y", "MarkerSize", 7);
 
 hold off;
 

--- a/predict.m
+++ b/predict.m
@@ -1,27 +1,9 @@
 function p = predict(theta, X)
-%PREDICT Predict whether the label is 0 or 1 using learned logistic 
-%regression parameters theta
-%   p = PREDICT(theta, X) computes the predictions for X using a 
-%   threshold at 0.5 (i.e., if sigmoid(theta'*x) >= 0.5, predict 1)
-
-m = size(X, 1); % Number of training examples
-
-% You need to return the following variables correctly
-p = zeros(m, 1);
+##PREDICT Predict whether the label is 0 or 1 using learned logistic 
+##regression parameters theta
+##   p = PREDICT(theta, X) computes the predictions for X using a 
+##   threshold at 0.5 (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
-%
-
-
-
-
-
-
-
-% =========================================================================
-
+  p = sigmoid (X*theta) >= 0.5;
 
 end

--- a/sigmoid.m
+++ b/sigmoid.m
@@ -1,18 +1,7 @@
 function g = sigmoid(z)
-%SIGMOID Compute sigmoid functoon
-%   J = SIGMOID(z) computes the sigmoid of z.
-
-% You need to return the following variables correctly 
-g = zeros(size(z));
+##SIGMOID Compute sigmoid functoon
+##   J = SIGMOID(z) computes the sigmoid of z.
 
-% ====================== YOUR CODE HERE ======================
-% Instructions: Compute the sigmoid of each value of z (z can be a matrix,
-%               vector or scalar).
-
-
-
-
-
-% =============================================================
+  g = 1./(1 + exp (-z));
 
 end