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Add pep8-naming to pre-commit hooks and fixes incorrect naming conventions (#7062)

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* ci(pre-commit): Add pep8-naming to `pre-commit` hooks (#7038)

* refactor: Fix naming conventions (#7038)

* Update arithmetic_analysis/lu_decomposition.py

Co-authored-by: Christian Clauss <cclauss@me.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* refactor(lu_decomposition): Replace `NDArray` with `ArrayLike` (#7038)

* chore: Fix naming conventions in doctests (#7038)

* fix: Temporarily disable project euler problem 104 (#7069)

* chore: Fix naming conventions in doctests (#7038)

Co-authored-by: Christian Clauss <cclauss@me.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Caeden
2022-10-12 23:54:20 +01:00
committed by GitHub
parent e2cd982b11
commit 07e991d553
140 changed files with 1552 additions and 1536 deletions
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36
machine_learning/logistic_regression.py
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@ -35,25 +35,25 @@ 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)))
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])
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)
z = np.dot(x, theta)
h = sigmoid_function(z)
gradient = np.dot(X.T, h - y) / y.size
gradient = np.dot(x.T, h - y) / y.size
theta = theta - alpha * gradient # updating the weights
z = np.dot(X, theta)
z = np.dot(x, theta)
h = sigmoid_function(z)
J = cost_function(h, y)
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
@ -61,23 +61,23 @@ def logistic_reg(alpha, X, y, max_iterations=70000):
if __name__ == "__main__":
iris = datasets.load_iris()
X = iris.data[:, :2]
x = iris.data[:, :2]
y = (iris.target != 0) * 1
alpha = 0.1
theta = logistic_reg(alpha, X, y, max_iterations=70000)
theta = logistic_reg(alpha, x, y, max_iterations=70000)
print("theta: ", theta) # printing the theta i.e our weights vector
def predict_prob(X):
def predict_prob(x):
return sigmoid_function(
np.dot(X, theta)
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())
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)