mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-11-24 13:31:07 +00:00
32c0418f63
* Infinite loop was fixed. Removed issue of unused variables. * Update logistic_regression.py * Update logistic_regression.py * correct spacing according to PEP8
95 lines
2.5 KiB
Python
95 lines
2.5 KiB
Python
#!/usr/bin/python
|
|
# -*- coding: utf-8 -*-
|
|
|
|
## Logistic Regression from scratch
|
|
|
|
# In[62]:
|
|
|
|
# In[63]:
|
|
|
|
# importing all the required libraries
|
|
|
|
''' Implementing logistic regression for classification problem
|
|
Helpful resources : 1.Coursera ML course 2.https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac'''
|
|
|
|
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()
|
|
|
|
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()
|