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This module is a custom implementation of the Large Margin Nearest Neighbor classification scheme of Weinberger, Saul, et al. (2009). It contains an implementation of the k-nearest neighbor and LMNN classifier as well as (most importantly) gradient calculation schemes on the LMNN cost function given a sequential data set and a user-choice of alignment algorithm. This enables users to learn parameters of the alignment distance in question using a gradient descent on the LMNN cost function. More information on this approach can be found in the Masters Thesis "Adaptive Affine Sequence Alignment Using Algebraic Dynamic Programming"

This module is a custom implementation of the Large Margin Nearest Neighbor classification scheme of Weinberger, Saul, et al. (2009). It contains an implementation of the k-nearest neighbor and LMNN classifier as well as (most importantly) gradient calculation schemes on the LMNN cost function given a sequential data set and a user-choice of alignment algorithm. This enables users to learn parameters of the alignment distance in question using a gradient descent on the LMNN cost function. More information on this approach can be found in the Masters Thesis "Adaptive Affine Sequence Alignment Using Algebraic Dynamic Programming"

This module is a custom implementation of the Large Margin Nearest Neighbor classification scheme of Weinberger, Saul, et al. (2009). It contains an implementation of the k-nearest neighbor and LMNN classifier as well as (most importantly) gradient calculation schemes on the LMNN cost function given a sequential data set and a user-choice of alignment algorithm. This enables users to learn parameters of the alignment distance in question using a gradient descent on the LMNN cost function. More information on this approach can be found in the Masters Thesis "Adaptive Affine Sequence Alignment Using Algebraic Dynamic Programming"

This module is a custom implementation of the Large Margin Nearest Neighbor classification scheme of Weinberger, Saul, et al. (2009). It contains an implementation of the k-nearest neighbor and LMNN classifier as well as (most importantly) gradient calculation schemes on the LMNN cost function given a sequential data set and a user-choice of alignment algorithm. This enables users to learn parameters of the alignment distance in question using a gradient descent on the LMNN cost function. More information on this approach can be found in the Masters Thesis "Adaptive Affine Sequence Alignment Using Algebraic Dynamic Programming"

This module is a custom implementation of the Large Margin Nearest Neighbor classification scheme of Weinberger, Saul, et al. (2009). It contains an implementation of the k-nearest neighbor and LMNN classifier as well as (most importantly) gradient calculation schemes on the LMNN cost function given a sequential data set and a user-choice of alignment algorithm. This enables users to learn parameters of the alignment distance in question using a gradient descent on the LMNN cost function. More information on this approach can be found in the Masters Thesis "Adaptive Affine Sequence Alignment Using Algebraic Dynamic Programming"

This module is a custom implementation of the Large Margin Nearest Neighbor classification scheme of Weinberger, Saul, et al. (2009). It contains an implementation of the k-nearest neighbor and LMNN classifier as well as (most importantly) gradient calculation schemes on the LMNN cost function given a sequential data set and a user-choice of alignment algorithm. This enables users to learn parameters of the alignment distance in question using a gradient descent on the LMNN cost function. More information on this approach can be found in the Masters Thesis "Adaptive Affine Sequence Alignment Using Algebraic Dynamic Programming"

This module is a custom implementation of the Large Margin Nearest Neighbor classification scheme of Weinberger, Saul, et al. (2009). It contains an implementation of the k-nearest neighbor and LMNN classifier as well as (most importantly) gradient calculation schemes on the LMNN cost function given a sequential data set and a user-choice of alignment algorithm. This enables users to learn parameters of the alignment distance in question using a gradient descent on the LMNN cost function. More information on this approach can be found in the Masters Thesis "Adaptive Affine Sequence Alignment Using Algebraic Dynamic Programming"