label='0.001') plt.plot([x / 1000 for x in range(5001)], [regressor4.predict({'x': x / 1000}) for x in range(5001)], label='0.0001') plt.legend() plt.savefig('log.png') df = DataFrame.from_array([[1, 0], [2, 0], [3, 0], [2, 1], [3, 1], [4, 1]], columns=['x', 'y']) reg = LogisticRegressor(df, dependent_variable='y') reg.set_coefficients({'constant': 0.5, 'x': 0.5}) alpha = 0.01 delta = 0.01 num_steps = 20000 reg.gradient_descent(alpha, delta, num_steps) print(reg.coefficients) #{'constant': 2.7911, 'x': -1.1165} plt.clf() plt.style.use('bmh') plt.plot([point[0] for point in points], [point[1] for point in points]) plt.plot([x / 1000 for x in range(5001)],
reg.set_coefficients({'constant': 0.5, 'x': 0.5}) print(reg.calc_rss()) print(reg.calc_gradient(delta)) reg.gradient_descent(alpha, delta, num_steps) print(reg.coefficients) ''' df = DataFrame.from_array([[2, 1], [3, 0]], columns=['x', 'y']) alpha = 0.2 delta = 0.1 num_steps = 20000 reg = LogisticRegressor(df, dependent_variable='y', premade=True) reg.set_coefficients({'constant': 1, 'x': 1}) print(reg.calc_rss()) print(reg.calc_gradient(delta)) #reg.gradient_descent(alpha, delta, num_steps) #print(reg.coefficients) '''' import matplotlib.pyplot as plt plt.style.use('bmh') points = {'x': [], 'y': []} x = -5 while x <= 10 : points['x'].append(x)