package sklearn

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Scikit-learn machine learning library for OCaml

Install 

dune-project
 Dependency

github.com Readme Edit opam file Versions (4)

Authors

  1. R
    Ronan Le Hy

Maintainers

  1. R
    Ronan Le Hy <ronan.lehy@gmail.com>

Sources

0.22-0.2.0.tar.gz
md5=5219c6159701b598f4df2656f48f7dc3
sha512=2ab4806b05c01ce429fbac6b64cba038f29d3f06553a3bafe64679589bea9e6096ca2a3c1b65b13c4a527645edb367d310475c4d310a50d8415b972cb7cb4b86

doc/sklearn/Sklearn/Arr/index.html

Module Sklearn.ArrSource

type t
val show : t -> string

### show

Pretty-print an Array into a string.

val pp : Format.formatter -> t -> unit

### pp

Pretty-print the Array.

Sourceval get : t -> [ `Ndarray of Ndarray.t | `Csr_matrix of Csr_matrix.t ]

### get

Get the underlying dense Ndarray or sparse Csr_matrix. No data is copied.

Sourceval get_ndarray : t -> Ndarray.t

### get_ndarray

Get the underlying dense Ndarray, or raise Invalid_argument if the underlying array is sparse.

Sourceval get_csr_matrix : t -> Csr_matrix.t

### get_csr_matrix

Get the underlying sparse Csr_matrix, or raise Invalid_argument if the underlying array is dense.

Sourceval of_ndarray : Ndarray.t -> t

### of_ndarray

Build an Array.t from a dense Ndarray.t. Data is shared.

Sourceval of_csr_matrix : Csr_matrix.t -> t

### of_csr_matrix

Build an Array.t from a Csr_matrix.t. Data is shared.

val of_bigarray : ('a, 'b, 'c) Bigarray.Genarray.t -> t

### of_bigarray

Build an Array from a bigarray.

val to_int_array : t -> int array
val to_float_array : t -> float array
module List : PyList.S with type elt := t

## module Array.List

module Dtype : sig ... end
val shape : t -> int array

### shape

Shape (dimensions) of an Arr.

val arange : ?start:int -> ?step:int -> int -> t

### arange

~~~python arange(start, stop, step,, dtype=None) ~~~

Return evenly spaced values within a given interval.

Values are generated within the half-open interval ``start, stop)`` (in other words, the interval including `start` but excluding `stop`). For integer arguments the function is equivalent to the Python built-in `range` function, but returns an ndarray rather than a list. When using a non-integer step, such as 0.1, the results will often not be consistent. It is better to use `numpy.linspace` for these cases. #### Parameters ???+ info "start : number, optional" Start of interval. The interval includes this value. The default start value is 0. ???+ "stop : number" End of interval. The interval does not include this value, except in some cases where `step` is not an integer and floating point round-off affects the length of `out`. ???+ info "step : number, optional" Spacing between values. For any output `out`, this is the distance between two adjacent values, ``out[i+1] - out[i]``. The default step size is 1. If `step` is specified as a position argument, `start` must also be given. ???+ info "dtype : dtype" The type of the output array. If `dtype` is not given, infer the data type from the other input arguments. #### Returns ???+ info "arange : ndarray" Array of evenly spaced values. For floating point arguments, the length of the result is ``ceil((stop - start)/step)``. Because of floating point overflow, this rule may result in the last element of `out` being greater than `stop`. #### See Also numpy.linspace : Evenly spaced numbers with careful handling of endpoints. numpy.ogrid: Arrays of evenly spaced numbers in N-dimensions. numpy.mgrid: Grid-shaped arrays of evenly spaced numbers in N-dimensions. #### Examples ~~~python >>> np.arange(3) array([0, 1, 2]) >>> np.arange(3.0) array([ 0., 1., 2.]) >>> np.arange(3,7) array([3, 4, 5, 6]) >>> np.arange(3,7,2) array([3, 5]) ~~~

val min : t -> float
val max : t -> float
val argsort : t -> t
val ones : ?dtype:Dtype.t -> int list -> t
val zeros : ?dtype:Dtype.t -> int list -> t
val set : [ `Colon | `I of int ] array -> [ `I of int | `F of float | `S of string ] -> t -> unit
val get_int : int list -> t -> int
val get_float : int list -> t -> float
val slice : ?i:int -> ?j:int -> ?step:int -> unit -> [> `Slice of Wrap_utils.Slice.t ]
val get_sub : [ `I of int | `Slice of Wrap_utils.Slice.t | `Arr of t ] list -> t -> t
module Ops : sig ... end
module Float : sig ... end

## module Array.Float

module Int : sig ... end

## module Array.Int

module String : sig ... end

## module Array.String

module Object : sig ... end

## module Arr.Object

val to_pyobject : t -> Py.Object.t

### to_pyobject

Convert the Array to a Py.Object.t.

Sourceval of_pyobject : Py.Object.t -> t

### of_pyobject

Build an Array from a Py.Object.t.