def plotBoundary(theta, X, y):
plotDecisionBoundary(theta, X.values, y.values)
plt.title(r'$\lambda$ = ' + str(Lambda))
# Labels and Legend
plt.xlabel('Microchip Test 1')
plt.ylabel('Microchip Test 2')
def plotBoundary(theta, X, y):
plotDecisionBoundary(theta, X.values, y.values)
plt.title(r'$\lambda$ = ' + str(Lambda))
# Labels and Legend
plt.xlabel('Microchip Test 1')
plt.ylabel('Microchip Test 2')
plt.show()
args=(X, y),
options={
'gtol': 1e-3,
'disp': True,
'maxiter': 1000
})
#Nelder-Mead method works similar to the Octave code
theta = res.x
cost = res.fun
# Print theta to screen
print('Cost at theta found by scipy: %f' % cost)
print('theta:', ["%0.4f" % i for i in theta])
# Plot Boundary
plotDecisionBoundary(theta, X, y)
# Labels and Legend
plt.legend(['Admitted', 'Not admitted'],
loc='upper right',
shadow=True,
fontsize='x-large',
numpoints=1)
plt.xlabel('Exam 1 score')
plt.ylabel('Exam 2 score')
plt.show()
input("Program paused. Press Enter to continue...")
# ============== Part 4: Predict and Accuracies ==============
print 'Gradient at initial theta (zeros): ' + str(grad)
# ============= Part 3: Optimizing using scipy =============
res = minimize(costFunction, initial_theta, method='TNC',
jac=False, args=(X, y), options={'gtol': 1e-3, 'disp': True, 'maxiter': 1000})
theta = res.x
cost = res.fun
# Print theta to screen
print 'Cost at theta found by scipy: %f' % cost
print 'theta:', ["%0.4f" % i for i in theta]
# Plot Boundary
plotDecisionBoundary(theta, X, y)
# Labels and Legend
plt.legend(['Admitted', 'Not admitted'], loc='upper right', shadow=True, fontsize='x-large', numpoints=1)
plt.xlabel('Exam 1 score')
plt.ylabel('Exam 2 score')
# ============== Part 4: Predict and Accuracies ==============
# Predict probability for a student with score 45 on exam 1
# and score 85 on exam 2
prob = sigmoid(np.array([1, 45, 85]).dot(theta))
print 'For a student with scores 45 and 85, we predict an admission probability of %f' % prob
