Python library for:

1. ML: machine learning
2. dynamic: dynamic programing
3. graph: graph method
4. numeric:
5. polynomial:
6. string_: string
7. structure: data structures

##Description

###ML machine learning algorithms:

• gradient descent: linear and classification
• optimizers: like momentum,AdaGrad,Adam
• K-nn
• K-means
• generative models
• loss function to estimate the error like: recall, precision, F_score, accuracy
• GMM

###dynamic function of dynamic programing:

###graph graph method:

• bfs,dfs,topology order,roots
• distance: like bellman ford, dijkstra,floyd warshall,jonson
• minimal spanning tree - prim, kruskal
• max flow: ford fulkerson,scaling,Maximum pairing

###numeric numeric methods

###polynomial functions to treat polynomials

• interpolation: with vandermonde matrix, lagrangh, newton,fft
• multiply polynomials in Coefficients or values representation

###string_ algorithms for treating strings

• pattern matching: naive mache, KMP, rabin karp

###structure data structures

• graph: vertex, edge, grape
• heap: max/min
• list: one/two direction