def run():
theta = sp.zeros((3, 1))
data = sp.copy(admission_data)
X = data[:, [0, 1]]
y = data[:, [2]]
m = sp.shape(y)[0]
# Add intercept term to x
X = sp.concatenate((sp.ones((m, 1)), X), axis=1)
"""
Part 1: Plotting
"""
print('Plotting data with + indicating (y = 1) examples and o indicating (y = 0) examples.')
logres.plotData(data)
plt.xlabel('Exam 1 score')
plt.ylabel('Exam 2 score')
plt.legend('Admitted', 'Not admitted')
plt.show()
print('Program paused. Press enter to continue.')
raw_input()
"""
Part 2: Compute Cost and Gradient
"""
(m, n) = X.shape
initial_theta = sp.zeros((n, 1))
(cost, grad) = logres.costFunction(initial_theta, X, y)
print('Cost at initial theta (zeros): ', cost)
print('Gradient at initial theta (zeros): ', grad)
print('Program paused. Press enter to continue.')
raw_input()
"""
Part 3: Optimizing using fminunc
"""
(theta, cost) = logres.find_minimum_theta(theta, X, y)
print('Cost at theta found by fmin: ', cost)
print('Theta: ', theta)
logres.plotDecisionBoundary(data, X, theta)
plt.show()
"""
Part 4: Predict and Accuracies
"""
prob = logres.sigmoid(sp.asmatrix([1, 45, 85]).dot(theta))
print('For a student with scores 45 and 85, we predict an admission probability of ', prob[0, 0])
print('Program paused. Press enter to continue.')
def run():
theta = sp.zeros((3, 1))
data = sp.copy(microchip_data)
X = data[:, [0, 1]]
y = data[:, [2]]
m = sp.shape(y)[0]
logres.plotData(data)
plt.xlabel('Microchip Test 1')
plt.ylabel('Microchip Test 2')
plt.legend(['y = 1', 'y = 0'])
plt.show()
"""
Regularized Logistic Regression
"""
X = logres.mapFeature(data[:, 0], data[:, 1])
initial_theta = sp.zeros((X.shape[1], 1))
lmbda = 1
(J, grad) = logres.costFunctionReg(initial_theta, X, y, lmbda)
print('Cost at initial theta (zeros): ', J[0,0])
print('Program paused. Press enter to continue.')
raw_input()
"""
Regularization and Accuracies
"""
initial_theta = sp.zeros((X.shape[1], 1))
lmbda = 1
(theta, J) = logres.find_minimum_theta_reg(initial_theta, X, y, lmbda)
logres.plotDecisionBoundary(data, X, theta)
plt.legend(['y = 1', 'y = 0', 'Decision Boundary'])
plt.show()
