package caisar

You can search for identifiers within the package.

in-package search v0.2.0

caisar.ir
- Ir
  - Nier_cfg
    
    Edge
    
    MakeVertex
    
    I
    
    NierCFG
    
    E
    
    I
    
    V
    
    NierCFGFloat
    
    E
    
    V
    
    Node
    
    Tensor
    
    VInput
caisar.nnet
- Nnet
caisar.onnx
- Onnx
caisar.ovo
- Ovo
caisar.xgboost
- Caisar_xgboost
  - Input
  - Parser
  - Predict
  - Tree

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include Graph.Sig.I
  with type V.t = (Base.float, Stdlib.Bigarray.float64_elt) Node.t
   and type V.label = (Base.float, Stdlib.Bigarray.float64_elt) Node.t
   and type E.t =
         (Base.float, Stdlib.Bigarray.float64_elt) Node.t
         * Edge.t
         * (Base.float, Stdlib.Bigarray.float64_elt) Node.t
   and type E.label = Edge.t

type t

module V : sig ... end

type vertex = V.t

module E : sig ... end

type edge = E.t

val is_directed : bool

val is_empty : t -> bool

val nb_vertex : t -> int

val nb_edges : t -> int

val out_degree : t -> vertex -> int

val in_degree : t -> vertex -> int

val mem_vertex : t -> vertex -> bool

val mem_edge : t -> vertex -> vertex -> bool

val mem_edge_e : t -> edge -> bool

val find_edge : t -> vertex -> vertex -> edge

val find_all_edges : t -> vertex -> vertex -> edge list

val succ : t -> vertex -> vertex list

val pred : t -> vertex -> vertex list

val succ_e : t -> vertex -> edge list

val pred_e : t -> vertex -> edge list

val iter_vertex : (vertex -> unit) -> t -> unit

val fold_vertex : (vertex -> 'a -> 'a) -> t -> 'a -> 'a

val iter_edges : (vertex -> vertex -> unit) -> t -> unit

val fold_edges : (vertex -> vertex -> 'a -> 'a) -> t -> 'a -> 'a

val iter_edges_e : (edge -> unit) -> t -> unit

val fold_edges_e : (edge -> 'a -> 'a) -> t -> 'a -> 'a

val map_vertex : (vertex -> vertex) -> t -> t

val iter_succ : (vertex -> unit) -> t -> vertex -> unit

val iter_pred : (vertex -> unit) -> t -> vertex -> unit

val fold_succ : (vertex -> 'a -> 'a) -> t -> vertex -> 'a -> 'a

val fold_pred : (vertex -> 'a -> 'a) -> t -> vertex -> 'a -> 'a

val iter_succ_e : (edge -> unit) -> t -> vertex -> unit

val fold_succ_e : (edge -> 'a -> 'a) -> t -> vertex -> 'a -> 'a

val iter_pred_e : (edge -> unit) -> t -> vertex -> unit

val fold_pred_e : (edge -> 'a -> 'a) -> t -> vertex -> 'a -> 'a

val create : ?size:int -> unit -> t

val clear : t -> unit

val copy : t -> t

val add_vertex : t -> vertex -> unit

val remove_vertex : t -> vertex -> unit

val add_edge : t -> vertex -> vertex -> unit

val add_edge_e : t -> edge -> unit

val remove_edge : t -> vertex -> vertex -> unit

val remove_edge_e : t -> edge -> unit

val init_cfg : t

val vertex_list : t -> vertex Base.list

val preds : t -> vertex -> vertex Base.list

preds_names g v returns a list of names of predecessors nodes

val preds_names : t -> vertex -> Base.string Base.list

val succs : t -> vertex -> vertex Base.list

succs_names g v returns a list of names of predecessors nodes

val succs_names : t -> vertex -> Base.string Base.list

input_node g returns the nodes considered as describing the inputs of the neural network.

val input_nodes : t -> vertex Base.list

val find_vertices : t -> (vertex -> Base.bool) -> vertex Base.list

data_node_of n returns one node containing a tensor * data among the predecessors of n

val data_node_of : vertex -> t -> vertex Base.option

infer_shape g n sh o_b returns the inferred shape of the output of node n in NIER g with input shape sh. Shape inference is made using the node operator and its predecessors shapes. o_b is true when performing backward propagation, to choose which matrix size to consider.

val infer_shape : 
  t ->
  vertex ->
  Node.shape ->
  on_backward:Base.bool ->
  Node.shape