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NOTE : ALL FILES ARE IN IPYTHON EXTENSION CODED IN PYTHON 2. SO TO HAVE A BETTER UNDERSTANDING OF CODES WITH PROVIDED OUTPUT

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You're suggested to have IPYTHON installed for ease.
You'll need graphlab to run the following two programs:
- K_FOLD_CROSS_VALIDATION.ipynb
- REGRESSION_ANALYSIS_L2_NORM_GRADIENT DESCENT.ipynb
Rest of the programs could be simply executed with Numpy.

The files contains different algorithmic approach to solve the Edit Distance Problem and generating possible alignment to minimize the complexity in terms of both Time and Space Complexity.
The above two files are based on Regression analysis with gradient descent, and implementing the 10-fold cross validation algorithm for choosing the the best tuning parameter for regularization. (corresponding data set has also been provided)

Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
house_data.gl		house_data.gl
Alignment_on_Dynamic_Programming.ipynb		Alignment_on_Dynamic_Programming.ipynb
BLOCKCHAIN DEMO.ipynb		BLOCKCHAIN DEMO.ipynb
BRANCH AND BOUND.ipynb		BRANCH AND BOUND.ipynb
DIVIDE AND CONQUER (LINEAR SPACE).ipynb		DIVIDE AND CONQUER (LINEAR SPACE).ipynb
Da.R		Da.R
Da1.R		Da1.R
Greedy Approach for Edit Distance.ipynb		Greedy Approach for Edit Distance.ipynb
K_FOLD_CROSS_VALIDATION.ipynb		K_FOLD_CROSS_VALIDATION.ipynb
MEMOIZED_EDIT_DSTANCE.ipynb		MEMOIZED_EDIT_DSTANCE.ipynb
RECURSIVE EDIT DISTANCE.ipynb		RECURSIVE EDIT DISTANCE.ipynb
REGRESSION_ANALYSIS_L2_NORM_GRADIENT DESCENT.ipynb		REGRESSION_ANALYSIS_L2_NORM_GRADIENT DESCENT.ipynb
Readme.md		Readme.md