#!/usr/bin/env python
# coding: utf-8

# # Logistic Regression from scratch

# In[62]:


''' Implementing logistic regression for classification problem 
     Helpful resources : 1.Coursera ML course    2.https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac'''


# In[63]:


#importing all the required libraries
import numpy as np
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')
from sklearn import datasets


# 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()

# 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):
    converged=False
    iterations=0
    theta=np.zeros(X.shape[1])
    
    
    while not converged:
        z=np.dot(X,theta)
        h=sigmoid_function(z)
        gradient = np.dot(X.T,(h-y))/y.size
        theta=theta-(alpha)*gradient
        
        z=np.dot(X,theta)
        h=sigmoid_function(z)
        J=cost_function(h,y)
       
     
        
        iterations+=1   #update iterations
        
        
        if iterations== max_iterations:
            print("Maximum iterations exceeded!")
            print("Minimal cost function J=",J)
            converged=True
            
    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)
    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();