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psf/black code formatting (#1277)

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This commit is contained in:
William Zhang
2019-10-05 01:14:13 -04:00
committed by Christian Clauss
parent 07f04a2e55
commit 9eac17a408
291 changed files with 6014 additions and 4571 deletions
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47
machine_learning/logistic_regression.py
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@ -9,8 +9,8 @@
# 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'''
""" 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
@ -24,6 +24,7 @@ from sklearn import datasets
# sigmoid function or logistic function is used as a hypothesis function in classification problems
def sigmoid_function(z):
return 1 / (1 + np.exp(-z))
@ -31,17 +32,14 @@ def sigmoid_function(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)) )
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,
):
def logistic_reg(alpha, X, y, max_iterations=70000):
theta = np.zeros(X.shape[1])
for iterations in range(max_iterations):
@ -53,42 +51,35 @@ def logistic_reg(
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
print(f"loss: {J} \t") # printing the loss after every 100 iterations
return theta
# In[68]:
if __name__ == '__main__':
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
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
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')
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))
(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.contour(xx1, xx2, probs, [0.5], linewidths=1, colors="black")
plt.legend()
plt.show()