mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-11-27 15:01:08 +00:00
5f4da5d616
* updating DIRECTORY.md
* isort --profile black .
* Black after
* updating DIRECTORY.md
Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
88 lines
2.4 KiB
Python
88 lines
2.4 KiB
Python
#!/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()
|