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DynamicTimeWarping

Description

Input is cost matrix where each value in input[r][c] is the cost for pass the point (r, c). From current point(r, c), points (r+1, c), (r+1, c+1) or (r, c+1) could be arrived in next move. Given such cost matrix, return dynamic time warping of shape [2, x], where the path made by all points (output[0][t], output[1][t])have the lowest cost among all paths from (0, 0) to (M-1, N-1).

 

Input parameters

 

specified_outputs_name : array, this parameter lets you manually assign custom names to the output tensors of a node.
 input (heterogeneous) – F : object, input cost tensor, it must be 2D tensor of shape M x N, or 1 x M x N.

 Parameters : cluster,

 training? : boolean, whether the layer is in training mode (can store data for backward).
Default value “True”.
 lda coeff : float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Default value “1”.

 name (optional) : string, name of the node.

Output parameters

 

 output (heterogeneous) – I : object, output tensor. shape is [2, x], where max(M, N) <= x < M + N.

Type Constraints

F in (tensor(float)) : Constrain to float tensors.

I in (tensor(int32)) : Constrain to integer types.

Example

All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Deep Learning library to run it).
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