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)
