import numpy as np


class LogisticRegression:
    def __init__(
                 self,
                 lr=0.01,
                 num_iter=100000,
                 fit_intercept=True,
                 verbose=False
                 ):
        self.lr = lr
        self.num_iter = num_iter
        self.fit_intercept = fit_intercept

    def __add_intercept(self, X):
        intercept = np.ones((X.shape[0], 1))
        return np.concatenate((intercept, X), axis=1)

    def __sigmoid(self, z):
        return 1 / (1 + np.exp(-z))

    def __loss(self, h, y):
        return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean()

    def fit(self, X, y):
        """
        lr.fit(X,y)
        """
        if self.fit_intercept:
            X = self.__add_intercept(X)

        # weights Initializaation
        self.theta = np.zeros(X.shape[1])

        for i in range(self.num_iter):
            z = np.dot(X, self.theta)
            h = self.__sigmoid(z)
            gradient = np.dot(X.T, (h - y)) / y.size
            self.theta -= self.lr * gradient

            if self.verbose is True and i % 10000 == 0:
                z = np.dot(X, self.theta)
                h = self.__sigmoid(z)
                print(f"Loss :{self.__loss(h,y)} \t")

    def predict_prob(self, X):
        if self.fit_intercept:
            x = self.__add_intercept(X)

        return self.__sigmoid(np.dot(x, self.theta))

    def predict(self, X, threshold):
        """
        lr.predict(X,y)
        """
        return self.predict_prob(X) >= threshold
