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 predict(Theta1, Theta2, X):
m = X.shape[0]
num_labels = Theta2.shape[0]
a1 = sp.c_[sp.ones((m, 1)), X]
z2 = Theta1.dot(a1.T)
a2 = logres.sigmoid(z2)
a2 = sp.r_[sp.ones((1, a2.shape[1])), a2]
# Output layer
z3 = Theta2.dot(a2)
a3 = logres.sigmoid(z3)
result = []
for i in range(0, m):
prediction = sp.argmax(a3[:, i]) + 1
result.append(prediction)
return result
def computeCost( theta, X, y, lamda ):
"""Copied cost function because fmin_cg messes up the dimensions"""
m = sp.shape( X )[0]
hypo = logres.sigmoid( X.dot( theta ) )
term1 = sp.log( hypo ).dot( -y )
term2 = sp.log( 1.0 - hypo ).dot( 1 - y )
left_hand = (term1 - term2) / m
right_hand = theta.T.dot( theta ) * lamda / (2*m)
return left_hand + right_hand
def logistic_regression():
"""
Predicts the probability that a student will be admitted
to a university based on how well he did on two exams
Params:
exam1: Integer score
exam2: Integer score
"""
exam1 = int(request.args.get('exam1'))
exam2 = int(request.args.get('exam2'))
prob = sigmoid(sp.asmatrix([1, exam1, exam2]).dot(theta))
return jsonify({
'probability_accepted': prob[0,0]
})
def gradientCost( theta, X, y, lamda ):
"""Copied gradient function because fmin_cg messes up the dimensions"""
m = sp.shape( X )[0]
grad = X.T.dot( logres.sigmoid( X.dot( theta ) ) - y ) / m
grad[1:] = grad[1:] + ( (theta[1:] * lamda ) / m )
return grad
