Implement plain KNN classifier and testing infrastructure

- Add plain KNN implementation with JSONL data processing
- Create Docker deployment setup with python:3.13-slim base
- Add comprehensive OJ-style testing system with accuracy validation
- Update README with detailed scoring mechanism explanation
- Add run.sh script following competition manual requirements

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

Dockerfile

@@ -0,0 +1,22 @@
+FROM python:3.13-slim
+
+# Create necessary directories
+RUN mkdir -p /home/admin/predict \
+  /home/admin/data \
+  /home/admin/workspace/job/logs \
+  /home/admin/workspace/job/output/predictions \
+  /home/admin/workspace/job/input
+
+# Copy files to required locations according to manual
+COPY run.sh /home/admin/predict/run.sh
+RUN chmod 777 /home/admin/predict/run.sh
+
+# Copy the compiled binary as 'test' executable
+COPY target/release/hfe_knn /home/admin/predict/test
+RUN chmod +x /home/admin/predict/test
+
+# Copy training data
+COPY dataset/train.jsonl /home/admin/data/data.jsonl
+
+# Set default command
+CMD ["/home/admin/predict/run.sh"]