def main():
# Benchmarking program
t = Timer(verbose=True)
# Dataset
X = [[1], [2], [3], [4], [5], [6], [7]]
Y = [1, 2, 3, 4, 5, 6, 7]
# Minimizing using gradient descent method
print("\n> Using Gradient Descent")
lr = LinearRegression(X, Y, 0.03)
t.start()
lr.setAlpha(0.035706)
lr.gradientDescent()
t.finish()
predicted_Y2 = [lr.predict(x) for x in X]
print("Error rate: %f" % lr.getErrorRate())
# Plotting example (2d graph)
plt.plot(X, Y, 'o') # Dots are original distribution
plt.plot(X, predicted_Y2, 'g') # Green line is Gradient descent
plt.show()
return 0
def main():
# Modeling dataset by adding x2=x1^2 and x3=x1^3
# so that y(x) = ax1^3 + bx1^2 + cx1 + d (polynomial curve)
X2 = [x + [x[0]**2, x[0]**3] for x in X]
# Minimizing using normal equation method
lr = LinearRegression(X2, Y)
lr.normalEquation()
predicted_Y1 = [lr.predict(x) for x in X2]
# Minimizing using gradient descent method
lr = LinearRegression(X2, Y)
lr.setAlpha(0.000001)
lr.gradientDescent()
predicted_Y2 = [lr.predict(x) for x in X2]
# Plotting example (2d graph)
plt.plot(X, Y, 'o') # Dots are original distribution
plt.plot(X, predicted_Y1, 'r') # Red line is Gradient descent
plt.plot(X, predicted_Y2, 'g') # Green line is Gradient descent
plt.show()
return 0
