def run_linear_regression():
print('Plotting data\n')
features = setup()
features.columns = ['Profits', 'CityPopulation']
X = features.Profits
y = features.CityPopulation
m = len(y)
iterations = 1500
alpha = 0.01
theta = np.zeros(m) # Set the initial theta value
lr = LinearRegression(X, y, iterations, alpha)
lr.plot_data(X, y, 'Profits', 'City Population',
'Food Truck Profit v. City Pop')
print('Testing gradient descent algorithm...\n')
# Add a column of ones to X
# X.bias = np.ones((m, 1))
print('Initial cost: {}'.format(lr.cost_function(X, y, theta)))
# Run the gradient descent
theta, cost_history = lr.gradient_descent(X, y, theta, alpha, iterations)
print('Optimum theta found by gradient descent: {}'.format(theta))
ax.set_ylabel('Cost')
ax.set_title('Error vs. Number of Iterations')
plt.show(block=False)
input('Press <Enter> to continue')
if __name__ == '__main__':
xlabel = "Population of City in 10,000s"
ylabel = "Profit in $10,000s"
title = "Population of City vs. Profit"
x, y = load_data('ex1data1.txt')
plot_data(x, y, xlabel, ylabel, title)
theta = np.zeros((2, 1))
iterations = 1000
alpha = 0.01
x.insert(0, 'X0', 1)
X = x.to_numpy()
Y = np.array(y)
lin_reg = LinearRegression()
gradient, cost_history = lin_reg.gradient_descent(X, Y, theta, iterations,
alpha)
plot_trendline(x.iloc[:, 1].to_numpy(), y.iloc[:, 0].to_numpy(), gradient,
xlabel, ylabel, title) #Ignore X0 = 1 in x dataframe
plot_computeCost(cost_history, iterations)
print(lin_reg.predict_intuition(3.5, gradient))
print(lin_reg.predict_vectorized([1, 3.5], gradient))
