scturtle

#include <fstream>
#include <iostream>
#include <sstream>
#include <string>
#include <vector>

namespace SATurtle {
struct CNF {
  CNF(const std::string &fn);
  int vc = 0;                       // #var

scturtle

#include <Eigen/Core>
#include <ceres/ceres.h>
#include <ceres/rotation.h>
#include <fstream>
#include <gflags/gflags.h>
#include <glog/logging.h>
#include <vector>

DEFINE_string(problem, "problem-49-7776-pre.txt", "dataset file name");

scturtle

#include <iostream>

template<int N>
struct fibo_result { int val = N; };

template<int ...Ns>
struct fibo_impl;

template<int a, int b, int I, int ...Ns>
struct fibo_impl<a, b, I, I, Ns...>

scturtle

#!/usr/bin/env python3
import sys
import clang.cindex

INDENT = 4
K = clang.cindex.CursorKind

def is_std_ns(node):
    return node.kind == K.NAMESPACE and node.spelling == 'std'

scturtle

;;; packages.el --- lsp-cquery layer packages file for Spacemacs.
;;
;; Copyright (c) 2012-2017 Sylvain Benner & Contributors
;;
;; Author: Shen Chao <[email protected]>
;; URL: https://github.com/syl20bnr/spacemacs
;;
;; This file is not part of GNU Emacs.
;;
;;; License: GPLv3

scturtle

icon_gravity E
geometry 5x1-0+0
max_geometry 5x1-0+0
background "#000000"
icon_size 20
kludges force_icons_size
skip_taskbar

scturtle

from __future__ import print_function
import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow.contrib.layers as tfl
import tensorflow.contrib.framework as tff

def minibatch(features):
    size_a, size_b, size_c = features.get_shape()[1], 3, 3

scturtle

import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib.patches import Arrow, Circle

fig = plt.figure("pf", figsize=(7., 7.))
ax = fig.gca()

landmarks = [[20., 20.], [20., 50.], [20., 80.],

scturtle

from __future__ import print_function
import string
import zipfile
import numpy as np
import tensorflow as tf
import tensorflow.contrib.layers as layers

class BatchGenerator:

    def __init__(self, text, vocabulary, batch_size, num_unrollings):

scturtle

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from transforms3d import euler

fig = plt.figure()
ax = fig.gca(projection='3d')

p1 = np.zeros((3, 100))
p1[0, :] = np.linspace(1, 3, 100)