lispandfound

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lispandfound

from pathlib import Path

import numpy as np
import pandas as pd
from empirical.util import openquake_wrapper_vectorized as openquake
from empirical.util import z_model_calculations
from empirical.util.classdef import GMM, TectType
from matplotlib import pyplot as plt
import numexpr as ne
from qcore import coordinates

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(defun treesit-mark-bigger-node ()
    (interactive)
    (let* ((node (treesit-node-on (region-beginning) (region-end))))
      (let ((next-node (treesit-parent-until node (lambda (n)
                                                    (not (and
                                                          (= (region-beginning) (treesit-node-start n))
                                                          (= (region-end) (treesit-node-end n))))))))
        (set-mark (treesit-node-end next-node))
        (goto-char (treesit-node-start next-node)))))

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;; Invoke via M-x doc-view-transient 
(transient-define-prefix doc-view-transient ()
  "Transient for doc-view mode."
  [:class transient-columns ;; the class sets how the transient is displayed; we have each category as a column.
          ["Zoom"
           ("+" "Enlarge" doc-view-enlarge :transient t) ;; :transient commands keep the transient prompt open
           ("-" "Shrink" doc-view-shrink :transient t)
           ("w" "Fit window to page" doc-view-fit-window-to-page :transient t)
           ("W" "Fit width to window" doc-view-fit-width-to-window :transient t)]
          ["Navigation"

lispandfound

;; -*- mode: Lisp -*-
#-quicklisp
(let ((quicklisp-init (merge-pathnames "quicklisp/setup.lisp"
                                       (user-homedir-pathname))))
  (when (probe-file quicklisp-init)
    (load quicklisp-init)))


(in-package :stumpwm)
(require 'swank)

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LoadPackage("fining");

e := 3;
q := 2 ^ e;
aut := 2^((e + 1) / 2);
pg := PG(2, q^2);
F := GF(q);
Ex := GF(q^2);
w := Z(q^2);

lispandfound

from collections import Counter

import tensorflow as tf
import tensorflow.keras as keras
import tensorflow.keras.layers as layers
from nltk import word_tokenize


class BengioModel(keras.Model):
    ''' Model that replicates the architecture of Bengio et al.  '''

lispandfound

module Lib
     where

import Graphics.Gloss

density :: Float
density = 0.1

spiral :: [Float] -> [(Float, Float)]
spiral pts = map f pts

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from enum import Enum, auto


class HeadMove(Enum):
    LEFT = -1
    RIGHT = 1
    NOTHING = 0


class TuringMachine:

lispandfound

program := 0;
i := 0;
read program;
read i;
j := i;
l := 0;
data := 0;
while j >= 0 do
      pow := 1;
      k := 0;