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How to write a table of contents in a Readme.md?

Guillaume_igt3nq

Hi,

I'm trying to add a table of contents into a Readme.md for one of my repositories but I can't find a way to do it.

I tried to use the same syntax as GitHub, something similar to:

# Table of Contents

1. [My first title](#my-first-title)
2. [My second title](#my-second-title)
## My first title
Some text.
## My second title

But it doesn't work. Nothing happens when I click on the links generated into the ToC.

I'm running Bitbucket server 5.4. Is it possible to do such a thing please? I didn't find anything relevant into the Atlassian documentation.

Thanks for your help.

Guillaume.

Accepted answers

smolkin

For bitbucket server:

1. [ Description. ](#desc)
2. [ Usage tips. ](#usage)

<a name="desc"></a>
## 1. Description

sometext

<a name="usage"></a>
## 2. Usage tips

sometext

All comments

Gonchik Tsymzhitov

Hi Guillaume,

Have you tried this tutorial for README.md?

https://bitbucket.org/tutorials/markdowndemo/overview#markdown-header-tables

Hope it helps.

Cheers,

Gonchik Tsymzhitov

Guillaume_igt3nq

Hi Gonchik,

Thank you for your help, but it doesn't answered my question. I'm not trying to create a table (I have no problems for this), but a table of contents.

Like:

Sélection_041.png

In other words: a list of links to the titles inside the README.

Guillaume.

Levi Moore

You need to add "markdown-header-" to your link parentheses.

1. [My First Title](#markdown-header-my-first-title)

2. [My Second Title](#markdown-header-my-second-title)

This worked for me.

D K

This didn't work for me I tried with and without `markdown-header-`

Chris Black_mb9dxw

Same for me... does not work...

smolkin

For bitbucket server:

1. [ Description. ](#desc)
2. [ Usage tips. ](#usage)

<a name="desc"></a>
## 1. Description

sometext

<a name="usage"></a>
## 2. Usage tips

sometext

Guillaume_igt3nq

It works! Thank you very much!

Saad Benbouzid

same here, doesn't work...

hh-lohmann

Obviously works because, as you said, you are hosting your own bitbucket server.

Others, reading this post and using bitbucket.org: Markdown on bitbucket.org does not allow HTML tags (for security reasons). github.com and other Markdown environments do, so in a common Markdown editor on your PC it should work, but you cannot publish that on bitbucket.org.

hh-lohmann

On bitbucket.org, just write "[TOC]", see https://confluence.atlassian.com/bitbucket/add-a-table-of-contents-to-a-wiki-221451163.html

For other editors see their references. Retext, e.g., and maybe any Python based Markdown editor, allow to define "extensions", one of which is "markdown.extensions.toc" which provides the "[TOC]" functionality.

limanzhang

It works! Thank you so much!

aapetroaei

Try to use it like this:
1. [Description.](#markdown-header-desc)

Tim Black_gwnp9l

This is a Bitbucket server question, and [TOC] does not work on Bitbucket server.

The accepted answer basically just shows how to do this with manually-written html anchors and references/links. I'm looking for a way to have this TOC generated from existing headers.

A search for "table of contents" for Bitbucket Server produces zero hits:

https://confluence.atlassian.com/search/?productName=Bitbucket&productVersion=6.2&queryString=%22table%20of%20contents%22

Anyone know how to autogenerate TOC in Bitbucket Server? We're on 6.2.

Looks like it can be done with external software, like this, or this. But I would really like for this to be done in Bitbucket without having to add any of my own "glue"..

Tim Black_kdm6rj

It's not possible with BB Server out of the box. Please vote for one or bothfboth othe related feature requests:

https://jira.atlassian.com/browse/BSERV-12289

https://jira.atlassian.com/browse/BSERV-10895

Graham Hannington

Atlassian,

Please add [TOC] (and other extensions?) to Bitbucket Server.

John Lawlor

This seems to be related to the quite old version of CommonMark (which Bitbucket README markdown is based on) bundled into Bitbucket server.

[user@server app]$ find . -name "*.jar" | grep common 
./WEB-INF/atlassian-bundled-plugins/atlassian-remote-event-common-plugin-6.2.2.jar 
./WEB-INF/atlassian-bundled-plugins/bitbucket-ao-common-7.9.0.jar 
./WEB-INF/lib/atlassian-cache-common-impl-5.3.3.jar 
./WEB-INF/lib/commonmark-0.15.2.jar 
./WEB-INF/lib/commons-beanutils-1.9.4.jar 
./WEB-INF/lib/commons-codec-1.14.jar 
./WEB-INF/lib/commons-compiler-2.7.8.jar 
./WEB-INF/lib/commons-compress-1.19.jar 
./WEB-INF/lib/atlassian-extras-common-3.4.1.jar 
./WEB-INF/lib/commons-digester-1.8.1.jar 
./WEB-INF/lib/commons-fileupload-1.3.3.jar 
./WEB-INF/lib/commons-io-2.6.jar 
./WEB-INF/lib/commons-lang-2.6.jar 
./WEB-INF/lib/commons-lang3-3.8.1.jar 
./WEB-INF/lib/commons-pool-1.6.jar 
./WEB-INF/lib/commons-validator-1.4.1.jar 
./WEB-INF/lib/atlassian-plugins-webresource-common-5.4.0-01c481fbd.jar 
./WEB-INF/lib/bitbucket-git-common-7.9.0.jar 
./WEB-INF/lib/bitbucket-scm-common-7.9.0.jar 
./WEB-INF/lib/bitbucket-web-common-7.9.0.jar 
./WEB-INF/lib/commonmark-ext-autolink-0.15.2.jar 
./WEB-INF/lib/commonmark-ext-gfm-strikethrough-0.15.2.jar 
./WEB-INF/lib/commonmark-ext-gfm-tables-0.15.2.jar 
./WEB-INF/lib/commonmark-ext-heading-anchor-0.15.2.jar 
./WEB-INF/lib/commonmark-ext-image-attributes-0.15.2.jar 
./WEB-INF/lib/commons-collections-3.2.2.jar 
./WEB-INF/lib/crowd-common-4.1.6.jar 
./WEB-INF/lib/crowd-integration-client-common-4.1.6.jar 
./WEB-INF/lib/crowd-rest-common-4.1.6.jar 
./WEB-INF/lib/crowd-server-common-4.1.6.jar 
./WEB-INF/lib/hibernate-commons-annotations-5.1.2.Final.jar 
./WEB-INF/lib/istack-commons-runtime-3.0.7.jar 
./WEB-INF/lib/netty-common-4.1.53.Final.jar 
./WEB-INF/lib/netty-transport-native-unix-common-4.1.53.Final.jar 
./WEB-INF/lib/proto-google-common-protos-1.17.0.jar 
./WEB-INF/osgi-framework-bundles/commons-lang-2.6.jar 
./WEB-INF/osgi-framework-bundles/commons-pool-1.6.jar 
./WEB-INF/savant/commons-text-1.8.jar

The bundled version (0.15.2) is from 2014, it could do with an update.
See also: https://jira.atlassian.com/browse/BSERV-11795

Nuriddin

### Pythonda vektorlar haqida tushuncha va ularning taqqoslanishi.


#### Vektor nima?

Oddiy so'z bilan aytganda vektorni bir o'lchovli massiv deb hisoblash mumkin. Python tiliga kelsak, vektor ro'yxatlarning bir o'lchovli massividir. U elementlarni Python ro'yxatiga o'xshash tarzda egallaydi.

* Massiv: <br>

Dasturlash tillarida ro’yxat yoki jadval ko’rinishidagi ma’lumotlarni massiv deb atashadi. Massiv so’zining ma’nosi o’lcham, o’lchov demakdir. Massivning barcha elementlari bitta turga mansub bo’lib, ular bitta nom bilan nomlanadi va bir-birlaridan nomerlari (indekslari) bilan farq qiladi.

#### Vektor yaratish

Python NumPy moduli vektor yaratish uchun ishlatiladi. Bir o'lchovli massiv, ya'ni vektor yaratish uchun numpy.array() metodidan foydalanamiz.

Syntax:

```python
numpy.array(list)
```

Example 1: Gorizontal vektor

```python
import numpy as np 

lst = [10, 20, 30, 40, 50] 

vctr = np.array(lst) 

print("List buyicha yaratilgan vektor:") 
print(vctr)
```
Output:
```terminal
List buyicha yaratilgan vektor:
[10 20 30 40 50]
```

Example 2: Vertikal Vector

```python
import numpy as np 

lst = [[2], 
        [4], 
        [6],
          [10]]  

vctr = np.array(lst) 

print("List buyicha yaratilgan vektor:") 
print(vctr)
```

Output:
```python
List buyicha yaratilgan vektor:
[[ 2]
 [ 4]
 [ 6]
 [10]]
```

#### Python vektorida asosiy operatsiyalar

Vektorni yaratgandan so'ng, keling, endi ushbu Vektorlar ustida bir nechta asosiy operatsiyalarni bajaramiz! <br>
Quyidagilar vektorda bajarilishi mumkin bo'lgan asosiy operatsiyalar ro'yxati - 

* Addition (qo'shish)
* Subtraction (ayirish)
* Multiplication (ko'paytirish)
* Division (bo'lish)
* Dot Product (nuqta amali) va boshqalar

#### 1. Python vektorida qo'shish operatsiyasini bajarish

Bunda ikki vektorning elementlari bo'yicha amal bajariladi va ikkala vektorning ham uzunligi bir xil bo'ladi.

Example:

```python
import numpy as np 

lst1 = [10, 20, 30, 40, 50] 
lst2 = [1, 2, 3, 4, 5]


vctr1 = np.array(lst1) 

vctr2= np.array(lst2) 


print("list 1 dan vektor yaratildi:") 
print(vctr1) 
print("list 2 dan vektor yaratildi:") 
print(vctr2) 

vctr_add = vctr1 + vctr2
print("Vektorlarning yig'indisi: ",vctr_add)
```

Output:
```console
list 1 dan vektor yaratildi:
[10 20 30 40 50]
list 2 dan vektor yaratildi:
[1 2 3 4 5]
Vektorlarning yig'indisi:  [11 22 33 44 55]
```

#### 2. Python vektorida ayirish operatsiyasini bajarish

Example:

```python
import numpy as np 

lst1 = [10, 20, 30, 40, 50] 
lst2 = [1, 2, 3, 4, 5]


vctr1 = np.array(lst1) 

vctr2= np.array(lst2) 


print("list 1 dan vektor yaratildi:") 
print(vctr1) 
print("list 2 dan vektor yaratildi:") 
print(vctr2) 

vctr_sub = vctr1-vctr2
print("Vektorlarning ayirmasi: ",vctr_sub)
```

Output:
```console
list 1 dan vektor yaratildi:
[10 20 30 40 50]
list 2 dan vektor yaratildi:
[1 2 3 4 5]
Vektorlarning ayirmasi:  [ 9 18 27 36 45]
```

#### 3. Python vektorida ko'paytirish operatsiyasini bajarish

Bunda ikki vektor elementlari ko'paytiriladi va ikkila vektor uzunligi teng bo'ladi.

Keling, ko'paytirish amalini tasavvur qilishga harakat qilaylik:  <br>
x = [10, 20] va y = [1, 2] ikkita vektor mavjud. Shunday qilib, mahsulot vektori v[ ] bo'ladi, <br>

v[0] = x[0] * y[0] <br>
v[1] = x[1] * y[1]

Quyidagi kodni ko'rib chiqing!

```python
import numpy as np 

lst1 = [10, 20, 30, 40, 50] 
lst2 = [1, 2, 3, 4, 5]

vctr1 = np.array(lst1) 

vctr2= np.array(lst2) 

print("list 1 dan vektor yaratildi:") 
print(vctr1) 
print("list 2 dan vektor yaratildi:") 
print(vctr2) 

vctr_mul = vctr1*vctr2
print("Vektorlaring ko'paytmasi: ",vctr_mul)
```

Output:
```console
list 1 dan vektor yaratildi:
[10 20 30 40 50]
list 2 dan vektor yaratildi:
[1 2 3 4 5]
Vektorlaring ko'paytmasi:  [ 10  40  90 160 250]
```

#### 4. Python vektorida bo'lish operatsiyasini bajarish

Yaxshiroq tushunish uchun quyidagi misolni ko'rib chiqing.

x = [10, 20] va y = [1, 2] ikkita vektor mavjud. Shunday qilib, mahsulot vektori v[ ] bo'ladi, <br>

v[0] = x[0] / y[0] <br>
v[1] = x[1] / y[1]

Example:
```python
import numpy as np 
 
lst1 = [10, 20, 30, 40, 50] 
lst2 = [10, 20, 30, 40, 50]
 
vctr1 = np.array(lst1) 
 
vctr2= np.array(lst2) 
 
print("list 1 dan vektor yaratildi:") 
print(vctr1) 
print("list 2 dan vektor yaratildi:") 
print(vctr2) 
 
vctr_div = vctr1/vctr2
print("Vektorlar bo'linmasi: ",vctr_div)
```

Output:
```console
list 1 dan vektor yaratildi:
[10 20 30 40 50]
list 2 dan vektor yaratildi:
[10 20 30 40 50]
Vektorlar bo'linmasi:  [ 1 1 1 1 1 ]
```

#### Vektor Dot 

Bu amalda biz ikki vektorning elementlari buyicha bajaramiz.
<br>
`Vektor Dot` amalini tushunib olish uchun quyidagi misolga e'tibor bering: <br>
`vector c = x . y = (x1 * y1 + x2 * y2)`

Example:
```python
import numpy as np 

lst1 = [10, 20, 30, 40, 50] 
lst2 = [1, 1, 1, 1, 1]


vctr1 = np.array(lst1) 

vctr2= np.array(lst2) 


print("list 1 dan vektor yaratildi:") 
print(vctr1) 
print("list 2 dan vektor yaratildi:") 
print(vctr2) 

vctr_dot = vctr1.dot(vctr2)
print("Vektor Dot amalinining natijasi: ",vctr_dot)
```

Output:
```console
list 1 dan vektor yaratildi:
[10 20 30 40 50]
list 2 dan vektor yaratildi:
[1 1 1 1 1]
Vektor Dot amalinining natijasi: 150
```

* Vektorlar haqida tushuncha hosil qilib oldik, endi vektorlarning qanday taqqoslanishini ko'rib chiqamiz.

#### Vektorlarni taqqoslash

Vektorlarni taqqoslanishini bir nechta usullari mavjud.
Masalan: 
Euclidean, Cosine, Hamming, Manhattan va boshqalar.

<p align="center">
    <img src="./image/vektor_taqqoslash_usullari.jpg">
</p>


Biz `cosine similarity` formulasi orqali vektorlarni taqqoslashni ko'rib o'tamiz.

<p align="center">
    <img src="./image/cosine_formulasi.png">
</p>

* Formuladagi amallarni pythonda tartib buyicha bajarib ko'ramiz.
<br>
* Vektorlarni hosil qilib olamiz.
```python
v1 = [1, 2, 3, 4, 5]
v2 = [1, 2, 3, 4, 5]
v3 = [1, 2, 3, 4, 6]
v4 = [100, 2, 3, 4, 5]
```
* Keling birinchi kasrning surat qismini bajarib olamiz. Bunda biz A va B vektorlarning elementlarini ko'paytirib qushib borishimiz kerak. Bu jarayonni `while` yordamida bajarib o'tamiz. Tushunarli bo'lishi uchun har bir jarayonni funksiya orqali bajarib o'tamiz.

```python
def surat(a,b):
    i = 0
    kasr_surati = 0
    while i < len(a):
        kasr_surati += (a[i] * b[i])
        i += 1
    return kasr_surati
```

* E'tibor bersangiz formulamizning maxraj qismida ildiz amali mavjud. Bu amalni bajarish uchun `ildiz` nomli funksiya yaratib o'tamiz va kerakli joyda funksiyani chaqirib ishlatamiz.

```python
def ildiz(x):
    return math.sqrt(x)
```

* Kasrning maxraj qismida bizdan vektorlarimizning xar bir elementini olib, va uni kvadratga oshirib, qushib borishimizni talab qilmoqda. Hosil bo'lgan yig'indilarni ildizlarining ko'paytmasini bajarib o'tishimiz lozim.
<br>
Maxraj qismining ishlanishi:

```python
def maxraj(a,b):
    # modul
    i = 0
    modul_a = 0
    modul_b = 0
    while i < len(a):
        modul_a += (a[i] * a[i])
        modul_b += (b[i] * b[i])
        i += 1

    modul_a = ildiz(modul_a)
    modul_b = ildiz(modul_b)
    kasr_maxraji = modul_a * modul_b
    return kasr_maxraji
```

* Endi biz `vector_taq` nomli yangi funksiya hosil qilib olamiz. Kasrning surat qismini maxraj qismiga bo'lsak yetarli.

```python
def vector_taq(a, b):
    # kosinus o'xshashlik https://en.wikipedia.org/wiki/Cosine_similarity
    # 1*1 + 2*2 + 2*2 + 1*2 + 1*1 + 1*1
    if len(a) == len(b):
        yaqinlik = surat(a,b) / maxraj(a,b)
        return yaqinlik
    else:
        print("Elementlar soni teng bo'lish kerak!")
```
* Yuqorida vektorlarning uzunliklari teng bulishligi lozim deb shart berib otdik. 

* Bajargan jarayonlarimizning umumiy ko'rinishi.

```python
import math

def surat(a,b):
    i = 0
    kasr_surati = 0
    while i < len(a):
        kasr_surati += (a[i] * b[i])
        i += 1
    return kasr_surati

def ildiz(x):
    return math.sqrt(x)

def maxraj(a,b):
    # modul
    i = 0
    modul_a = 0
    modul_b = 0
    while i < len(a):
        modul_a += (a[i] * a[i])
        modul_b += (b[i] * b[i])
        i += 1

    modul_a = ildiz(modul_a)
    modul_b = ildiz(modul_b)
    kasr_maxraji = modul_a * modul_b
    return kasr_maxraji

def vector_taq(a, b):
    # kosinus o'xshashlik https://en.wikipedia.org/wiki/Cosine_similarity
    # 1*1 + 2*2 + 2*2 + 1*2 + 1*1 + 1*1
    if len(a) == len(b):
        yaqinlik = surat(a,b) / maxraj(a,b)
        return yaqinlik
    else:
        print("Elementlar soni teng bo'lish kerak!")


v1 = [1, 2, 3, 4, 5]
v2 = [1, 2, 3, 4, 5]
v3 = [1, 2, 3, 4, 6]
v4 = [100, 2, 3, 4, 5]


natija1 = vector_taq(v1, v2)
natija2 = vector_taq(v1, v3)
natija3 = vector_taq(v1, v4)

print(natija1)
print(natija2)
print(natija3)
```

* Bajarib o'tkan dasturimizni ishlatib o'tsak quyidagi natijani olamiz.

```console
1.0
0.9958591954639383
0.20709515355464156
```

#### Vektorlarni taqqoslash uchun tayyor kutibxonadan foydalanish

* Vektorlarni taqqoslashda biz tayyor kutibxonadan foydalanishni ko'ramiz. Buning uchun biz `cosine_similarity` va `numpy` kutubxonalarini `import` qilib olishimiz lozim.

```python
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

v1 = [1, 2, 3, 4, 5]
v2 = [1, 2, 3, 4, 5]
v3 = [1, 2, 3, 4, 6]
v4 = [100, 2, 3, 4, 5]

natija1 = cosine_similarity(np.array(v1).reshape(1, -1), np.array(v2).reshape(1, -1))[0][0]
natija2 = cosine_similarity(np.array(v1).reshape(1, -1), np.array(v3).reshape(1, -1))[0][0]
natija3 = cosine_similarity(np.array(v1).reshape(1, -1), np.array(v4).reshape(1, -1))[0][0]

print(natija1)
print(natija2)
print(natija3)
```
* Yuqorida bajarib o'tkanimizdek bunda ham bir xil natijani olamiz.

```console
1.0
0.9958591954639383
0.2070951535546416
```