Purpose: Code coverage analysis tool
Measures how much of your source code is exercised by your unit tests
Tracks:
Generates visual HTML reports showing what parts of code are untested
| Algorithm | Type | Generative/Discriminative | Linear/Non-linear | Notes |
|---|---|---|---|---|
| Naive Bayes | Classification | Generative | Linear | Assumes feature independence, uses Bayes' theorem |
| Logistic Regression | Classification | Discriminative | Linear | Uses sigmoid function, mainly for binary classification |
| Decision Trees | Classification | Discriminative | Non-linear | Handles both numerical and categorical features |
| SVM | Classification | Discriminative | Linear/Non-linear | Uses kernel trick for non-linear, finds optimal hyperplane |
| k-NN | Classification | Discriminative | Non-linear | Instance-based, sensitive to feature scaling |
| Neural Networks | Classification | Discriminative | Non-linear | Can model complex patterns, requires large datasets |
| Linear Regression | Regression | Discriminative | Linear | Predicts continuous values, sensitive to multicollinearity |
| k-Means | Clustering | - | Linear | Unsupervised, sensitive to initial centroids and outliers |
| PCA | Dimensionality Reduction | - | Linear | Unsupervised, maximizes variance, does not consider class labels |
| Hierarchical Clustering | Clustering | - | Non-linear | Unsupervised, builds nested clusters, linkage methods affect results |
| Perceptron | Classification | Discriminative | Linear | Simple model, only for linearly separable data |
| Multi-Layer Perceptron | Classification/Regression | Discriminative | Non-linear | Can solve XOR problem, uses backpropagation for training |
# server.py
from mcp.server.fastmcp import FastMCP
# Create an MCP server
mcp = FastMCP("Demo")
# Add an addition tool
@mcp.tool()
def add(a: int, b: int) -> int:
"""Add two numbers"""
return a + b
# Add a dynamic greeting resource
@mcp.resource("greeting://{name}")
def get_greeting(name: str) -> str:
"""Get a personalized greeting"""
return f"Hello, {name}!"
if __name__ == "__main__":
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Depth-First Traversal Preorder Traversal (Recursive, Iterative) Left-Right Inorder Traversal (Recursive, Iterative) Left-Middle-Right Postorder Traversal (Recursive, Iterative) Left-Right-Middle Breadth-First Traversal Level Order Traversal (Iterative)