def run():
data = sp.copy(housing_data)
x = data[:, [0, 1]]
y = data[:, [2]]
m = sp.shape(y)[0]
# Normalize the x values
(x, mu, sigma) = graddesc.featureNormalize(x)
# Add intercept term to x
x = sp.concatenate((sp.ones((m, 1)), x), axis=1)
# Init Theta and run Gradient Descent
num_iters = 400
# Choose some alpha value
alphas = [0.01, 0.03, 0.1, 0.3, 1.0]
for alpha in alphas:
theta = sp.zeros((3, 1))
(theta, J_history) = graddesc.gradientDescent(x, y, theta, alpha, num_iters)
# Plot the value of J by number of iterations
plt.plot(range(1, J_history.size+1), J_history, '-b')
plt.title('Alpha = %f' % (alpha))
plt.xlabel('Number of iterations')
plt.ylabel('J')
plt.xlim([0, 50])
plt.show(block=True)
# Estimate the price of a 1650 sq-ft, 3 br house
price = 0
house = sp.array([[1.0, 1650.0, 3.0]])
# Normalize the features
house[0, 1:] = (house[0, 1:] - mu) / sigma
price = house.dot(theta)
print('The estimated price with alpha', alpha, 'is', price[0, 0])
# Reload the data
data = sp.copy(housing_data)
x = data[:, [0, 1]]
y = data[:, [2]]
# Add intercept term to x
x = sp.concatenate((sp.ones((m, 1)), x), axis=1)
# Calculate the normal equation
theta = graddesc.normalEqn(x, y)
print('Theta computed from the normal equations:')
print(theta)
import scipy as sp from app.ex1 import housing_data from app.ex1.gradient_descent import computeCost, featureNormalize, gradientDescent, normalEqn data = sp.copy(housing_data) x = data[:, [0, 1]] y = data[:, [2]] m = sp.shape(y)[0] # Normalize the x values (x, mu, sigma) = featureNormalize(x) # Add intercept term to x x = sp.concatenate((sp.ones((m, 1)), x), axis=1) # Init Theta and run Gradient Descent num_iters = 400 # Choose some alpha value alpha = 0.3 # Calculate theta theta = sp.zeros((3, 1)) (theta, J_history) = gradientDescent(x, y, theta, alpha, num_iters) normal_theta = sp.zeros((3, 1)) normal_theta = normalEqn(x, y)