patricoferris’s gists

patricoferris / simple-graphql-post.ml

Last active April 27, 2021 14:37

Simple Cohttp Post

	let uri = "https://countries.trevorblades.com/"

	let query = {\| query {
	countries {
	name
	}
	}\|}

	let send_graphql_query query =
	let open Lwt.Infix in

patricoferris / index.ml

Last active January 23, 2021 14:10

State management with Javascript objects

	open Mithril
	open Brr

	module State = struct
	type t = Jv.t

	let state = Jv.obj [\| ("id", Jv.of_int 0) \|]

	let get_id () = Jv.get state "id" \|> Jv.to_int

patricoferris / stream.ml

Created April 20, 2020 09:54

	open Lwt.Infix

	let add_to_table tbl str =
	Base.(Hashtbl.set tbl ~key:str ~data:(Hashtbl.find_or_add tbl str ~default:(fun () -> 0) + 1))

	let stream_stdin () =
	Lwt_stream.from (fun () -> try Lwt.return (Some (input_line stdin)) with End_of_file -> Lwt.return None)

	let main c tbl =
	Lwt_stream.iter_n ~max_concurrency:c (fun a -> Lwt.return (add_to_table tbl a)) (stream_stdin ());;

patricoferris / partial-app.ml

Created October 8, 2019 23:54

	(* A fibonacci function *)
	let rec fib x = if x = 0 \|\| x = 1 then 1 else fib (x - 1) + fib (x - 2);;

	(* A function that allows for partial evaluation *)
	let h x = let z = fib x in fun y -> y + z;;

	(* fib 30 will only be calculated once *)
	List.map (h 30) [1; 2; 3; 4; 5];;

patricoferris / parser.mly

Last active October 6, 2019 13:36

	(* Part of the %inline expr_attrs productions *)
	\| FUN ext_attributes labeled_simple_pattern fun_def
	{ let (l,o,p) = $3 in
	Pexp_fun(l, o, p, $4), $2 }

	(* the fun_def, lident_list and ext_attributes is then defined as the following *)
	fun_def:
	MINUSGREATER seq_expr
	{ $2 }
	\| mkexp(COLON atomic_type MINUSGREATER seq_expr

patricoferris / lexer.mll

Last active October 6, 2019 10:40

	(* lowercase definition *)
	let lowercase = ['a'-'z' '_']

	(* identchar definition *)
	let identchar = ['A'-'Z' 'a'-'z' '_' '\'' '0'-'9']

	(* Some stuff before *)
	\| lowercase identchar * as name
	{ try Hashtbl.find keyword_table name
	with Not_found -> LIDENT name }

patricoferris / calculations.py

Last active October 2, 2019 14:05

	# First we calculate mu_s
	alpha = 2.
	K_xy = rbf_kernel(xs_train, xs_test, alpha)
	K_yy = rbf_kernel(xs_test, xs_test, alpha)
	K_xx = rbf_kernel(xs_train, xs_train, alpha)
	np.random.seed(20)
	mu_s = np.matmul(K_xy.T, np.matmul(np.linalg.pinv(K_xx), (ys_train)))

	# Second we calculate sigma_s
	sigma_s = K_yy - np.matmul(K_xy.T, np.matmul(np.linalg.pinv(K_xx), K_xy))

patricoferris / example_draw.py

Created October 2, 2019 13:41

	from sklearn.gaussian_process.kernels import RBF

	# First we set up the training data
	xs_train = training_points # The points that will reign in our prediction
	ys_train = train_temp # The corresponding temperatures for those points

	# Then the space for which we are trying to predict
	xs_test = np.atleast_2d(np.linspace(0, 20, 1000)).T

	# We will use the SKLearn RBF function

patricoferris / init_data.py

Created October 2, 2019 13:34

	# Get the New York temperatures
	n = 20
	new_york_temp = city_temps.loc[city_temps['City'] == "New York"][-n:]
	temps = np.array(new_york_temp["AverageTemperature"])
	temps = temps / np.linalg.norm(temps)

	# Extract some random training points from the 20 we are using (set random seed for reproducibility)
	training_points = np.atleast_2d([1, 5, 7, 10, 14, 16, 19]).T
	np.random.seed(20)
	train_temp = temps[training_points]

patricoferris / multivariate-gaussian.py

Created April 25, 2019 21:37

	# Generate the linear evenly spaced xs and ys
	n = 50
	three_xs = np.linspace(-5, 5, n)
	three_ys = np.linspace(-5, 5, n)

	# Generate the values for both the X and Y (two parts to the Z)
	three_zs_a = np.array(list(map(lambda x: guass_func(x, 0, 1), three_xs)))
	three_zs_b = np.array(list(map(lambda y: guass_func(y, 0, 2), three_ys)))

	# Use the Kronecker product of the two https://docs.scipy.org/doc/numpy/reference/generated/numpy.kron.html

Patrick Ferris patricoferris