logistik.py

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

# In[1]:


#import required modules
import numpy as np
 
class LogisticRegression:
    def __init__(self,x,y):      
        self.intercept = np.ones((x.shape[0], 1))  
        self.x = np.concatenate((self.intercept, x), axis=1)
        self.weight = np.zeros(self.x.shape[1])
        self.y = y
         
    #Sigmoid method
    def sigmoid(self, x, weight):
        z = np.dot(x, weight)
        return 1 / (1 + np.exp(-z))
     
    #method to calculate the Loss
    def loss(self, h, y):
        return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean()
     
    #Method for calculating the gradients
    def gradient_descent(self, X, h, y):
        return np.dot(X.T, (h - y)) / y.shape[0]
 
     
    def fit(self, lr , iterations):
        for i in range(iterations):
            sigma = self.sigmoid(self.x, self.weight)
             
            loss = self.loss(sigma,self.y)
 
            dW = self.gradient_descent(self.x , sigma, self.y)
             
            #Updating the weights
            self.weight -= lr * dW
 
        return print('fitted successfully to data')
     
    #Method to predict the class label.
    def predict(self, x_new , treshold):
        x_new = np.concatenate((self.intercept, x_new), axis=1)
        result = self.sigmoid(x_new, self.weight)
        result = result >= treshold
        y_pred = np.zeros(result.shape[0])
        for i in range(len(y_pred)):
            if result[i] == True: 
                y_pred[i] = 1
            else:
                continue
                 
        return y_pred


# In[2]:


from sklearn.datasets import load_breast_cancer
 
#Loading the data
data = load_breast_cancer()
 
#Preparing the data
x = data.data
y = data.target
 
#creating the class Object
regressor = LogisticRegression(x,y)
 
#
regressor.fit(0.1 , 5000)
 
 
y_pred = regressor.predict(x,0.5)
 
print('accuracy -> {}'.format(sum(y_pred == y) / y.shape[0]))