#!/usr/bin/python

# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

"""
Implementing logistic regression for classification problem
Helpful resources:
Coursera ML course
https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac
"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets

# get_ipython().run_line_magic('matplotlib', 'inline')


# In[67]:

# sigmoid function or logistic function is used as a hypothesis function in
# classification problems


def sigmoid_function(z):
 return 1 / (1 + np.exp(-z))


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


def log_likelihood(X, Y, weights):
 scores = np.dot(X, weights)
 return np.sum(Y * scores - np.log(1 + np.exp(scores)))


# here alpha is the learning rate, X is the feature matrix,y is the target matrix
def logistic_reg(alpha, X, y, max_iterations=70000):
 theta = np.zeros(X.shape[1])

 for iterations in range(max_iterations):
 z = np.dot(X, theta)
 h = sigmoid_function(z)
 gradient = np.dot(X.T, h - y) / y.size
 theta = theta - alpha * gradient # updating the weights
 z = np.dot(X, theta)
 h = sigmoid_function(z)
 J = cost_function(h, y)
 if iterations % 100 == 0:
 print(f"loss: {J}\t") # printing the loss after every 100 iterations
 return theta


# In[68]:

if __name__ == "__main__":
 iris = datasets.load_iris()
 X = iris.data[:, :2]
 y = (iris.target != 0) * 1

 alpha = 0.1
 theta = logistic_reg(alpha, X, y, max_iterations=70000)
 print("theta: ", theta) # printing the theta i.e our weights vector

 def predict_prob(X):
 return sigmoid_function(
 np.dot(X, theta)
 ) # predicting the value of probability from the logistic regression algorithm

 plt.figure(figsize=(10, 6))
 plt.scatter(X[y == 0][:, 0], X[y == 0][:, 1], color="b", label="0")
 plt.scatter(X[y == 1][:, 0], X[y == 1][:, 1], color="r", label="1")
 (x1_min, x1_max) = (X[:, 0].min(), X[:, 0].max())
 (x2_min, x2_max) = (X[:, 1].min(), X[:, 1].max())
 (xx1, xx2) = np.meshgrid(np.linspace(x1_min, x1_max), np.linspace(x2_min, x2_max))
 grid = np.c_[xx1.ravel(), xx2.ravel()]
 probs = predict_prob(grid).reshape(xx1.shape)
 plt.contour(xx1, xx2, probs, [0.5], linewidths=1, colors="black")

 plt.legend()
 plt.show()