# theta = 0.5
# y_pred = sigmoid_(x * theta)
# m = 1
# length of y_true is 1
# print(log_loss_(y_true, y_pred, m))
# 0.12692801104297152
# # Test n.2
# x = [1, 2, 3, 4]
# y_true = 0
# theta = [-1.5, 2.3, 1.4, 0.7]
# x_dot_theta = sum([a*b for a, b in zip(x, theta)])
# y_pred = sigmoid_(x_dot_theta)
# m = 1
# print(log_loss_(y_true, y_pred, m))
# # 10.100041078687479
# # # Test n.3
x_new = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
y_true = [1, 0, 1]
theta = [-1.5, 2.3, 1.4, 0.7]
x_dot_theta = []
for i in range(len(x_new)):
my_sum = 0
# for j in range(len(x_new[i])):
# my_sum += x_new[i][j] * theta[j]
my_sum = sum([a * b for a, b in zip(x_new[i], theta)])
x_dot_theta.append(my_sum)
y_pred = sigmoid_(x_dot_theta)
m = len(y_true)
print(log_loss_(y_true, y_pred, m))
# # 7.233346147374828
import numpy as np
from log_loss import log_loss_
from log_pred import logistic_predict_
y1 = np.array([1])
x1 = np.array([4])
theta1 = np.array([[2], [0.5]])
y_hat1 = logistic_predict_(x1, theta1)
print(log_loss_(y1, y_hat1))
print("---------------------------------------")
# Output:
# 0.01814992791780973
# Example 2:
y2 = np.array([[1], [0], [1], [0], [1]])
x2 = np.array([[4], [7.16], [3.2], [9.37], [0.56]])
theta2 = np.array([[2], [0.5]])
y_hat2 = logistic_predict_(x2, theta2)
print(log_loss_(y2, y_hat2))
print("---------------------------------------")
# Output:
# 2.4825011602474483
# Example 3:
y3 = np.array([[0], [1], [1]])
x3 = np.array([[0, 2, 3, 4], [2, 4, 5, 5], [1, 3, 2, 7]])
theta3 = np.array([[-2.4], [-1.5], [0.3], [-1.4], [0.7]])
y_hat3 = logistic_predict_(x3, theta3)
print(log_loss_(y3, y_hat3))
print("---------------------------------------")
# Output: