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gaussian_process.ml
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gaussian_process.ml
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(* GaussianProcessClassifier *)
(*
>>> from sklearn.datasets import load_iris
>>> from sklearn.gaussian_process import GaussianProcessClassifier
>>> from sklearn.gaussian_process.kernels import RBF
>>> X, y = load_iris(return_X_y=True)
>>> kernel = 1.0 * RBF(1.0)
>>> gpc = GaussianProcessClassifier(kernel=kernel,
... random_state=0).fit(X, y)
>>> gpc.score(X, y)
0.9866...
>>> gpc.predict_proba(X[:2,:])
array([[0.83548752, 0.03228706, 0.13222543],
[0.79064206, 0.06525643, 0.14410151]])
*)
(* TEST TODO
let%expect_test "GaussianProcessClassifier" =
let open Sklearn.Gaussian_process in
let x, y = load_iris ~return_X_y:true () in
let kernel = 1.0 * RBF(1.0) in
let gpc = GaussianProcessClassifier(kernel=kernel,random_state=0).fit ~x y () in
print_ndarray @@ GaussianProcessClassifier.score ~x y gpc;
[%expect {|
0.9866...
|}]
print_ndarray @@ GaussianProcessClassifier.predict_proba x[:2 :] gpc;
[%expect {|
array([[0.83548752, 0.03228706, 0.13222543],
[0.79064206, 0.06525643, 0.14410151]])
|}]
*)
(* GaussianProcessRegressor *)
(*
>>> from sklearn.datasets import make_friedman2
>>> from sklearn.gaussian_process import GaussianProcessRegressor
>>> from sklearn.gaussian_process.kernels import DotProduct, WhiteKernel
>>> X, y = make_friedman2(n_samples=500, noise=0, random_state=0)
>>> kernel = DotProduct() + WhiteKernel()
>>> gpr = GaussianProcessRegressor(kernel=kernel,
... random_state=0).fit(X, y)
>>> gpr.score(X, y)
0.3680...
>>> gpr.predict(X[:2,:], return_std=True)
*)
(* TEST TODO
let%expect_test "GaussianProcessRegressor" =
let open Sklearn.Gaussian_process in
let x, y = make_friedman2(n_samples=500, noise=0, random_state=0) in
let kernel = DotProduct() + WhiteKernel() in
let gpr = GaussianProcessRegressor(kernel=kernel,random_state=0).fit ~x y () in
print_ndarray @@ GaussianProcessRegressor.score ~x y gpr;
[%expect {|
0.3680...
|}]
print_ndarray @@ GaussianProcessRegressor.predict x[:2 :] ~return_std:true gpr;
[%expect {|
|}]
*)
(*--------- Examples for module Sklearn.Gaussian_process.Kernels ----------*)
(* cdist *)
(*
>>> from scipy.spatial import distance
>>> coords = [(35.0456, -85.2672),
... (35.1174, -89.9711),
... (35.9728, -83.9422),
... (36.1667, -86.7833)]
>>> distance.cdist(coords, coords, 'euclidean')
array([[ 0. , 4.7044, 1.6172, 1.8856],
[ 4.7044, 0. , 6.0893, 3.3561],
[ 1.6172, 6.0893, 0. , 2.8477],
[ 1.8856, 3.3561, 2.8477, 0. ]])
*)
(* TEST TODO
let%expect_test "cdist" =
let open Sklearn.Gaussian_process in
let coords = [(35.0456, -85.2672),(35.1174, -89.9711),(35.9728, -83.9422),(36.1667, -86.7833)] in
print_ndarray @@ .cdist ~coords coords 'euclidean' distance;
[%expect {|
array([[ 0. , 4.7044, 1.6172, 1.8856],
[ 4.7044, 0. , 6.0893, 3.3561],
[ 1.6172, 6.0893, 0. , 2.8477],
[ 1.8856, 3.3561, 2.8477, 0. ]])
|}]
*)
(* cdist *)
(*
>>> a = np.array([[0, 0, 0],
... [0, 0, 1],
... [0, 1, 0],
... [0, 1, 1],
... [1, 0, 0],
... [1, 0, 1],
... [1, 1, 0],
... [1, 1, 1]])
>>> b = np.array([[ 0.1, 0.2, 0.4]])
>>> distance.cdist(a, b, 'cityblock')
array([[ 0.7],
[ 0.9],
[ 1.3],
[ 1.5],
[ 1.5],
[ 1.7],
[ 2.1],
*)
(* TEST TODO
let%expect_test "cdist" =
let open Sklearn.Gaussian_process in
let a = .array [(vectori [|0; 0; 0|]) (vectori [|0; 0; 1|]) (vectori [|0; 1; 0|]) (vectori [|0; 1; 1|]) (vectori [|1; 0; 0|]) (vectori [|1; 0; 1|]) (vectori [|1; 1; 0|]) (vectori [|1; 1; 1|])] np in
let b = matrix [|[| 0.1; 0.2; 0.4|]|] in
print_ndarray @@ .cdist ~a b 'cityblock' distance;
[%expect {|
array([[ 0.7],
[ 0.9],
[ 1.3],
[ 1.5],
[ 1.5],
[ 1.7],
[ 2.1],
|}]
*)
(* namedtuple *)
(*
>>> Point = namedtuple('Point', ['x', 'y'])
>>> Point.__doc__ # docstring for the new class
'Point(x, y)'
>>> p = Point(11, y=22) # instantiate with positional args or keywords
>>> p[0] + p[1] # indexable like a plain tuple
33
>>> x, y = p # unpack like a regular tuple
>>> x, y
(11, 22)
>>> p.x + p.y # fields also accessible by name
33
>>> d = p._asdict() # convert to a dictionary
>>> d['x']
11
>>> Point( **d) # convert from a dictionary
Point(x=11, y=22)
>>> p._replace(x=100) # _replace() is like str.replace() but targets named fields
*)
(* TEST TODO
let%expect_test "namedtuple" =
let open Sklearn.Gaussian_process in
let Point = namedtuple 'Point' ['x' 'y'] () in
print_ndarray @@ Point.__doc__ # docstring for the new class;
[%expect {|
'Point(x, y)'
|}]
let p = Point(11, y=22) # instantiate with positional args or keywords in
print_ndarray @@ p(vectori [|0|]) + p(vectori [|1|]) # indexable like a plain tuple;
[%expect {|
33
|}]
let x, y = p # unpack like a regular tuple in
print_ndarray @@ x, y;
[%expect {|
(11, 22)
|}]
print_ndarray @@ p.x + p.y # fields also accessible by name;
[%expect {|
33
|}]
let d = p._asdict() # convert to a dictionary in
print_ndarray @@ d['x'];
[%expect {|
11
|}]
print_ndarray @@ Point( **d) # convert from a dictionary;
[%expect {|
Point(x=11, y=22)
|}]
print_ndarray @@ p._replace(x=100) # _replace() is like .replace str but targets named fields;
[%expect {|
|}]
*)