Next Nest

wusa!~(•㉨•)

Highlight!

发表于 Valine：

To make sure that all the code blocks highlighted correctly. Most of the code samples are come from the demo of https://highlightjs.org/ …

Sharing the World
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Sharing our World
Sharing our World
I can see this future is right now
Cause my voice is always going around
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Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book.

Diff

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Index: languages/ini.js
===================================================================
--- languages/ini.js    (revision 199)
+++ languages/ini.js    (revision 200)
@@ -1,8 +1,7 @@
 hljs.LANGUAGES.ini =
 {
   case_insensitive: true,
-  defaultMode:
-  {
+  defaultMode: {
     contains: ['comment', 'title', 'setting'],
     illegal: '[^\\s]'
   },

*** /path/to/original timestamp
--- /path/to/new      timestamp
***************
*** 1,3 ****
--- 1,9 ----
+ This is an important
+ notice! It should
+ therefore be located at
+ the beginning of this
+ document!

! compress the size of the
! changes.

  It is important to spell

Rust

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#[derive(Debug)]
pub enum State {
    Start,
    Transient,
    Closed,
}
impl From<&'a str> for State {
    fn from(s: &'a str) -> Self {
        match s {
            "start" => State::Start,
            "closed" => State::Closed,
            _ => unreachable!(),
        }
    }
}

JSON

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[
  {
    "title": "apples",
    "count": [12000, 20000],
    "description": {"text": "...", "sensitive": false}
  },
  {
    "title": "oranges",
    "count": [17500, null],
    "description": {"text": "...", "sensitive": false}
  }
]

Python

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@requires_authorization
def somefunc(param1='', param2=0):
    r'''A docstring'''
    if param1 > param2: # interesting
        print 'Gre\'ater'
    return (param2 - param1 + 1 + 0b10l) or None
class SomeClass:
    pass
>>> message = '''interpreter
... prompt'''

CNN-Training with Keras

PythonMNIST_CNN
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'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''

from __future__ import print_function

import requests
import bs4

import numpy as np
import scipy, sympy
import pandas as pd

import matplotlib as mpl, matplotlib.pyplot as plt
mpl.style.use("solarized-light")

import keras
from keras import backend as T

class Constants(object):
    BATCH_SIZE = 128
    NUM_CLASSES = 10
    NUM_EPOCHES = 12
    INPUT_IMG = {
        "DIM_ROWS": 28, # input image dimensions
        "DIM_COLS": 28,
        "SHAPE": (1, 28, 28) if T.image_data_format() == "channels_first" else (28, 28, 1)
    }
    pass

class ImgData(object):

    def __init__(self, src):

        # the data, split between train and test sets
        self.__xtrain = src[0][0]
        self.__ytrain = keras.utils.to_categorical(src[0][1], Constants.NUM_CLASSES)    # convert class vectors to binary class matrices
        self.__xtest = src[1][0]
        self.__ytest = keras.utils.to_categorical(src[1][1], Constants.NUM_CLASSES)     # 把标签处理成 one-hot 型数据

        # 考虑到Theano和TensorFlow两个框架的数据结构不同, 于此需要根据不同的后端调整数据维度顺序
        if T.image_data_format() == "channels_first":
            self.__xtrain = self.__xtrain.reshape(
                self.__xtrain.shape[0],
                1,
                Constants.INPUT_IMG["DIM_ROWS"],
                Constants.INPUT_IMG["DIM_COLS"],
            ).astype("float32") / 255
            self.__xtest = self.__xtest.reshape(
                self.__xtest.shape[0],
                1,
                Constants.INPUT_IMG["DIM_ROWS"],
                Constants.INPUT_IMG["DIM_COLS"],
            ).astype("float32") / 255

        else:
            self.__xtrain = self.__xtrain.reshape(
                self.__xtrain.shape[0],
                Constants.INPUT_IMG["DIM_ROWS"],
                Constants.INPUT_IMG["DIM_COLS"],
                1,
            ).astype("float32") / 255
            self.__xtest = self.__xtest.reshape(
                self.__xtest.shape[0],
                Constants.INPUT_IMG["DIM_ROWS"],
                Constants.INPUT_IMG["DIM_COLS"],
                1,
            ).astype("float32") / 255

        return

    def log_datashape(self):
        print("X Shape: %s" % self.__xtrain.shape[ 1 : ])
        print("Training on %s Data, Validating on %s." % (self.__xtrain.shape[0], self.__xtest.shape[0]))
        return

    @property
    def xtrain(self):
        return self.__xtrain

    @property
    def ytrain(self):
        return self.__ytrain

    @property
    def xtest(self):
        return self.__xtest

    @property
    def ytest(self):
        return self.__ytest

    pass

if __name__ == "__main__":

    data = ImgData(keras.datasets.mnist.load_data())

    # 创建一个 Sequential 模型, 并把所有涉及的计算层按顺序投入其中
    seq = keras.models.Sequential(layers = [
        keras.layers.convolutional.Conv2D(
            32,
            kernel_size = (3, 3),
            activation = "relu",
            input_shape = Constants.INPUT_IMG["SHAPE"]
        ),
        keras.layers.convolutional.Conv2D(64, (3, 3), activation="relu"),
        keras.layers.pooling.MaxPooling2D(pool_size=(2, 2)),
        keras.layers.core.Dropout(rate=0.25),
        keras.layers.core.Flatten(),
        keras.layers.core.Dense(units=128, activation="relu"),
        keras.layers.core.Dropout(rate=0.5),
        keras.layers.core.Dense(units=Constants.NUM_CLASSES, activation="softmax")
    ])

    # 架构并编译模型, 同时声明模型计算的 loss(损失函数), optimizer(优化器), metric(评价算法)
    seq.compile(
        loss=keras.losses.categorical_crossentropy,
        optimizer=keras.optimizers.Adadelta(),
        metrics=["accuracy"]
    )

    # 拟合数据, 训练模型
    history = seq.fit(
        x = data.xtrain,
        y = data.ytrain,
        batch_size = Constants.BATCH_SIZE,
        epochs = Constants.NUM_EPOCHES,
        verbose = 1,
        validation_data = (data.xtest, data.ytest)
    )

    # 评估模型
    _eval = seq.evaluate(x=data.xtest, y=data.ytest, verbose=0)
    print("Test Loss: %s" % _eval[0])
    print("Test Accuracy: %s" % _eval[1])

    pass

HTML

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<!DOCTYPE html>
<title>Title</title>

<style>body {width: 500px;}</style>

<script type="application/javascript">
  function $init() {return true;}
</script>

<body>
  <p checked class="title" id='title'>Title</p>
  <!-- here goes the rest of the page -->
</body>

JavaScript

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function $initHighlight(block, cls) {
  try {
    if (cls.search(/\bno\-highlight\b/) != -1)
      return process(block, true, 0x0F) +
             ` class="${cls}"`;
  } catch (e) {
    /* handle exception */
  }
  for (var i = 0 / 2; i < classes.length; i++) {
    if (checkCondition(classes[i]) === undefined)
      console.log('undefined');
  }
  return (
    <div>
      <web-component>{block}</web-component>
    </div>
  )
}
export $initHighlight;

C/C++

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#include <iostream>
int main(int argc, char *argv[]) {
  /* An annoying "Hello World" example */
  for (auto i = 0; i < 0xFFFF; i++)
    cout << "Hello, World!" << endl;
  char c = '\n';
  unordered_map <string, vector<string> > m;
  m["key"] = "\\\\"; // this is an error
  return -2e3 + 12l;
}

Number Theoretic Transform

C/C++
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#include<iostream>
using namespace std;
void NTT(int *A, int type) {
    for (int i = 0; i < limit; ++i) if (i < r[i]) swap(A[i], A[r[i]]);
    for (int mid = 1; mid < limit; mid <<= 1) {
        ll Wn = poww(type == 1 ? G : Gi, mid << 1);
        for (int R = mid << 1, j = 0; j < limit; j += R) {
            ll w=1;
            for(int k = 0; k < mid; k++, w = mul(Wn, w)) {
                int x = A[j + k], y = A[j + mid + k];
                A[j + k] = add(x, y);
                A[j + k + mid]=dec(x, y);
            }
        }
    }
    if(type == -1) {
        for(int Inv = poww(limit, mod - 2), i = 0; i < limit; ++i) {
            A[i] = mul(A[i], Inv);
        }
    }
}
int main()
{
cout<<"                ********    "<<endl;
cout<<"               ************    "<<endl;
cout<<"               ####....#.    "<<endl;
cout<<"             #..###.....##....    "<<endl;
cout<<"             ###.......######              ###            ###    "<<endl;
cout<<"                ...........               #...#          #...#    "<<endl;
cout<<"               ##*#######                 #.#.#          #.#.#    "<<endl;
cout<<"            ####*******######             #.#.#          #.#.#    "<<endl;
cout<<"           ...#***.****.*###....          #...#          #...#    "<<endl;
cout<<"           ....**********##.....           ###            ###    "<<endl;
cout<<"           ....****    *****....    "<<endl;
cout<<"             ####        ####    "<<endl;
cout<<"           ######        ######    "<<endl;
cout<<"##############################################################    "<<endl;
cout<<"#...#......#.##...#......#.##...#......#.##------------------#    "<<endl;
cout<<"###########################################------------------#    "<<endl;
cout<<"#..#....#....##..#....#....##..#....#....#####################    "<<endl;
cout<<"##########################################    #----------#    "<<endl;
cout<<"#.....#......##.....#......##.....#......#    #----------#    "<<endl;
cout<<"##########################################    #----------#    "<<endl;
cout<<"#.#..#....#..##.#..#....#..##.#..#....#..#    #----------#    "<<endl;
cout<<"##########################################    ############    "<<endl;
    return 0;
}

SQL

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CREATE TABLE "topic" (
    "id" serial NOT NULL PRIMARY KEY,
    "forum_id" integer NOT NULL,
    "subject" varchar(255) NOT NULL
);
ALTER TABLE "topic"
ADD CONSTRAINT forum_id FOREIGN KEY ("forum_id")
REFERENCES "forum" ("id");
-- Initials
insert into "topic" ("forum_id", "subject")
values (2, 'D''artagnian');

Objective-C

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#import <UIKit/UIKit.h>
#import "Dependency.h"
@protocol WorldDataSource
@optional
- (NSString*)worldName;
@required
- (BOOL)allowsToLive;
@end
@property (nonatomic, readonly) NSString *title;
- (IBAction) show;
@end

Java

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/**
 * @author John Smith <john.smith@example.com>
*/
package l2f.gameserver.model;
public abstract class L2Char extends L2Object {
  public static final Short ERROR = 0x0001;
  public void moveTo(int x, int y, int z) {
    _ai = null;
    log("Should not be called");
    if (1 > 5) { // wtf!?
      return;
    }
  }
}

Swift

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import Foundation
@objc class Person: Entity {
  var name: String!
  var age:  Int!
  init(name: String, age: Int) {
    /* /* ... */ */
  }
  // Return a descriptive string for this person
  func description(offset: Int = 0) -> String {
    return "\(name) is \(age + offset) years old"
  }
}

CSS

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@font-face {
  font-family: Chunkfive; src: url('Chunkfive.otf');
}
body, .usertext {
  color: #F0F0F0; background: #600;
  font-family: Chunkfive, sans;
}
@import url(print.css);
@media print {
  a[href^=http]::after {
    content: attr(href)
  }
}

Ruby

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# The Greeter class
class Greeter
  def initialize(name)
    @name = name.capitalize
  end
  def salute
    puts "Hello #{@name}!"
  end
end
g = Greeter.new("world")
g.salute

Makefile

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# Makefile
BUILDDIR      = _build
EXTRAS       ?= $(BUILDDIR)/extras
.PHONY: main clean
main:
	@echo "Building main facility..."
	build_main $(BUILDDIR)
clean:
	rm -rf $(BUILDDIR)/*

Go

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package main
import "fmt"
func main() {
    ch := make(chan float64)
    ch <- 1.0e10    // magic number
    x, ok := <- ch
    defer fmt.Println(`exitting now\`)
    go println(len("hello world!"))
    return
}

Bash

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#!/bin/bash
###### CONFIG
ACCEPTED_HOSTS="/root/.hag_accepted.conf"
BE_VERBOSE=false
if [ "$UID" -ne 0 ]
then
  echo "Superuser rights required"
  exit 2
fi
genApacheConf(){
  echo -e "# Host ${HOME_DIR}$1/$2 :"
}

TOML, also INI

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; boilerplate
[package]
name = "some_name"
authors = ["Author"]
description = "This is \
a description"
[[lib]]
name = ${NAME}
default = True
auto = no
counter = 1_000

R

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library(ggplot2)

centre <- function(x, type, ...) {
  switch(type,
         mean = mean(x),
         median = median(x),
         trimmed = mean(x, trim = .1))
}

myVar1
myVar.2
data$x
foo "bar" baz
# test "test"
"test # test"

(123) (1) (10) (0.1) (.2) (1e-7)
(1.2e+7) (2e) (3e+10) (0x0) (0xa)
(0xabcdef1234567890) (123L) (1L)
(0x10L) (10000000L) (1e6L) (1.1L)
(1e-3L) (4123.381E-10i)
(3.) (3.E10) # BUG: .E10 should be part of number

# Numbers in some different contexts
1L
0x40
.234
3.
1L + 30
plot(cars, xlim=20)
plot(cars, xlim=0x20)
foo<-30
my.data.3 <- read() # not a number
c(1,2,3)
1%%2

"this is a quote that spans
multiple lines
\"

is this still a quote? it should be.
# even still!

" # now we're done.

'same for
single quotes #'

# keywords
NULL, NA, TRUE, FALSE, Inf, NaN, NA_integer_,
NA_real_, NA_character_, NA_complex_, function,
while, repeat, for, if, in, else, next, break,
..., ..1, ..2

# not keywords
the quick brown fox jumped over the lazy dogs
null na true false inf nan na_integer_ na_real_
na_character_ na_complex_ Function While Repeat
For If In Else Next Break .. .... "NULL" `NULL` 'NULL'

# operators
+, -, *, /, %%, ^, >, >=, <, <=, ==, !=, !, &, |, ~,
->, <-, <<-, $, :, ::

# infix operator
foo %union% bar
%"test"%
`"test"`

Scilab

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// A comment
function I = foo(dims, varargin)
  d=[1; matrix(dims(1:$-1),-1,1)]
  for i=1:size(varargin)
    if varargin(i)==[] then
       I=[],
       return;
    end
  end
endfunction

b = cos(a) + cosh(a);
bar_matrix = [ "Hello", "world" ];
foo_matrix = [1, 2, 3; 4, 5, 6];

Matlab

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n = 20; % number of points
points = [random('unid', 100, n, 1), random('unid', 100, n, 1)];
len = zeros(1, n - 1);
points = sortrows(points);
%% Initial set of points
plot(points(:,1),points(:,2));
for i = 1: n-1
    len(i) = points(i + 1, 1) - points(i, 1);
end
while(max(len) > 2 * min(len))
    [d, i] = max(len);
    k = on_margin(points, i, d, -1);
    m = on_margin(points, i + 1, d, 1);
    xm = 0; ym = 0;
%% New point
    if(i == 1 || i + 1 == n)
        xm = mean(points([i,i+1],1))
        ym = mean(points([i,i+1],2))
    else
        [xm, ym] = dlg1(points([k, i, i + 1, m], 1), ...
            points([k, i, i + 1, m], 2))
    end

    points = [ points(1:i, :); [xm, ym]; points(i + 1:end, :)];
end

%{
    This is a block comment. Please ignore me.
%}

function [net] = get_fit_network(inputs, targets)
    % Create Network
    numHiddenNeurons = 20;  % Adjust as desired
    net = newfit(inputs,targets,numHiddenNeurons);
    net.trainParam.goal = 0.01;
    net.trainParam.epochs = 1000;
    % Train and Apply Network
    [net,tr] = train(net,inputs,targets);
end

foo_matrix = [1, 2, 3; 4, 5, 6]''';
foo_cell = {1, 2, 3; 4, 5, 6}''.'.';

cell2flatten = {1,2,3,4,5};
flattenedcell = cat(1, cell2flatten{:});

Fortran

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subroutine test_sub(k)
    implicit none

  !===============================
  !   This is a test subroutine
  !===============================

    integer, intent(in)           :: k
    double precision, allocatable :: a(:)
    integer, parameter            :: nmax=10
    integer                       :: i

    allocate (a(nmax))

    do i=1,nmax
      a(i) = dble(i)*5.d0
    enddo

    print *, 'Hello world'
    write (*,*) a(:)

end subroutine test_sub