package tezos-benchmark

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in-package search v0.2.0

tezos-benchmark
- Tezos_benchmark
  - Base_samplers
    
    Adversarial
  - Benchmark
    
    Benchmark_base
    
    S
    
    Simple
    
    Simple_with_num
  - Benchmark_helpers
  - Builtin_benchmarks
    
    Timer_latency_bench
  - Builtin_models
  - Codegen
    
    Parser
  - Config
  - Costlang
    
    Arg_names
    
    Beta_normalize
    
    X
    
    Eval
    
    Eval_linear_combination_impl
    
    Eval_to_vector
    
    Fold_constants
    
    X
    
    Free_variables
    
    Hash_cons
    
    X
    
    Hash_cons_vector
    
    Identity
    
    X
    
    Let_lift
    
    X
    
    Pp
    
    S
    
    Subst
    
    P
    
    X
    
    Transform
    
    X
    
    Void
  - Crypto_samplers
    
    Finite_key_pool_S
    
    Make_finite_key_pool
    
    Arg
    
    P_Finite_key_pool_S
    
    Param_S
    
    V0
    
    Finite_key_pool_S
    
    Make_finite_key_pool
    
    Arg
    
    V1
    
    Finite_key_pool_S
    
    Make_finite_key_pool
    
    Arg
    
    V_latest
    
    Finite_key_pool_S
    
    Make_finite_key_pool
    
    Arg
  - Csv
  - Dep_graph
    
    Graph
    
    Graphviz
    
    Solver
    
    Solved
    
    Unsolved
  - Fixed_point_transform
    
    Apply
    
    P
    
    X
  - Free_variable
    
    Map
    
    Set
    
    Sparse_vec
    
    Op
    
    Table
  - Generator
    
    S
  - Inference
  - Maths
  - Measure
  - Model
    
    Applied
    
    X
    
    Binary_operation
    
    Def
    
    X
    
    Instantiate
    
    M
    
    Def
    
    X
    
    X
    
    Instantiated
    
    Model_impl
    
    Def
    
    X
  - Namespace
    
    Hashtbl
    
    Map
    
    Set
  - Ocamlformat
  - Override
  - Plot
    
    Bar
    
    Color
    
    Data
    
    Fill
    
    Histogram
    
    Line
    
    Pointtype
    
    Scatter
    
    Style
    
    Tics
  - Pyinference
    
    LinearModel
  - Pyinit
  - Pytools
  - Registration
  - Scikit_matrix
  - Sparse_vec
    
    Make
    
    M
    
    Op
    
    S
    
    Op
    
    String
    
    Op
  - Structure_samplers

Legend:
Library
Module
Module type
Parameter
Class
Class type

type t

Matrices implemented as 2d bigarrays.

type gen =
  (float, Stdlib.Bigarray.float64_elt, Stdlib.Bigarray.c_layout)
    Stdlib.Bigarray.Genarray.t

Genarrays.

val create : lines:int -> cols:int -> t

Create a matrix and initializes it to 0.0

val init : lines:int -> cols:int -> f:(int -> int -> float) -> t

Create and initializes a matrix.

val dim1 : t -> int

Number of lines.

val dim2 : t -> int

Number of columns.

val shape : t -> int * int

Lines x Columns

val set : t -> int -> int -> float -> unit

set m i j v sets the element on line i and column j of m to v.

val get : t -> int -> int -> float

get m i j gets the element on line i and column j.

val map : t -> (float -> float) -> t

map m f creates a new matrix by mapping f pointwise to m.

val column : t -> int -> t

column m i returns the ith column (starting from 0)

val concat_columns_horiz : t list -> t

concat_columns_horiz [c1;...;cn] returns the matrix having c1...cn as rows. The function fails if the columns have incoherent dimensions.

val to_genarray : t -> gen

Converts the 2d bigarray to a genarray.

val of_genarray : gen -> t

Converts a 2d genarray back to a matrix.

val to_numpy : t -> Pytypes.pyobject

Converts to a numpy Python object.

val to_numpy_vector : 
  (float, Stdlib.Bigarray.float64_elt, Stdlib.Bigarray.c_layout)
    Stdlib.Bigarray.Array1.t ->
  Pytypes.pyobject

Converts a vector to a numpy Python object.

val of_numpy : Pytypes.pyobject -> t

Converts from a numpy Python object.

val numpy_mul : t -> t -> t

val numpy_add : t -> t -> t

val numpy_sub : t -> t -> t

Numpy implementations of matrix operations.

val numpy_show : t -> string

String representation of matri via numpy.