semvec/pypi-packages huggingface dataset:
Topsis-suddhasattwa-102003687 0.0.0
A python package to implement topsis
Requires Python: >=3.6
Keywords: topsis_suddhasattwa_khan_102003687,topsis-suddhasattwa-102003687,topsis,Maths

# Topsis implemented by Suddhasattwa Khan 102003687

[![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/)  
[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)

## Functionality of the Package

- Outputs the result(topsis score) and ranking of items in output.csv file you will mention in terminal or command promt
- Inputs the file path of input csv file in terminal

## Example

```
#test.py
##
type:
from Topsis_Suddhasattwa_102003687 import main
n=len(sys.argv)
inputFile= sys.argv[1]
weight=sys.argv[2]
impact=sys.argv[3]
outputFile=sys.argv[4]
# main.topsis(inputFile, weight, impact, outputFile)
```

## Usage

- Make sure you have Python installed in your system.
- Run Following command in the CMD.

```
 pip install Topsis_Suddhasattwa_102003687
```

## Run the following Script.

```
 python <Filename.py> <inputfile.csv> weight impact <outputfile.csv>
```

## Note

- I have tried to implement all the functionality, it might have some bugs also. Ignore that or please try to solve that bug.
ContextBertWordEmbedding 0.0.1
Extract bert word embedding from sentence (based on the context)
Keywords: python,nlp,bert,text,transformers,embedding,word embedding,sentence embedding,context embedding,word embedding from context,sentence embedding from context,context embedding from context,similarity

# bert-word-embeddings-from-context-sentence :shipit:

*One way to confirm contextually dependent vectors is by evaluating the model's performance on tasks that require understanding the meaning of words in context, such as natural language inference or question answering. Another way is to perform a qualitative analysis by comparing the embeddings generated for a specific word in different sentences or contexts, and observing if they are similar or dissimilar.*

```python
from ContextBertWordEmbedding import  get_word_embedding_from_sentence

text_1 = "bring some money"
text_2 = "bring your focus to your heart"

word = "bring"

embedding_1 = get_word_embedding_from_sentence(text_1,word)
embedding_2 = get_word_embedding_from_sentence(text_2,word)

print(embedding_1[0:5])
print(embedding_2[0:5])

```
> Output 1 : tensor([ 0.1592,  0.4534,  2.8211, -1.0691,  1.4695])

> Output 2 : tensor([ 2.2381,  0.2831,  1.0606, -2.3082, -0.1782])

As you can see, the same word has different embeddings depending on the context :+1:

### 📝 If a sentence contains more than one word, you get them all by setting `all_index=True` .

```python
text = "After stealing money from the bank vault, the bank robber was seen fishing on the Mississippi river bank."
word = "bank"

embedding = get_word_embedding_from_sentence(text,word,all_index=True)
print(embedding)
```
> Output : [embedding_1,embedding_2,embedding_3]
ContextBertWordEmbeddings 0.0.1
Extract bert word embedding from sentence (based on the context)
Keywords: python,nlp,bert,text,transformers,embedding,word embedding,sentence embedding,context embedding,word embedding from context,sentence embedding from context,context embedding from context,similarity

# bert-word-embeddings-from-context-sentence :shipit:

*One way to confirm contextually dependent vectors is by evaluating the model's performance on tasks that require understanding the meaning of words in context, such as natural language inference or question answering. Another way is to perform a qualitative analysis by comparing the embeddings generated for a specific word in different sentences or contexts, and observing if they are similar or dissimilar.*

```python
from ContextBertWordEmbeddingsFrom import  get_word_embedding_from_sentence

text_1 = "bring some money"
text_2 = "bring your focus to your heart"

word = "bring"

embedding_1 = get_word_embedding_from_sentence(text_1,word)
embedding_2 = get_word_embedding_from_sentence(text_2,word)

print(embedding_1[0:5])
print(embedding_2[0:5])

```
> Output 1 : tensor([ 0.1592,  0.4534,  2.8211, -1.0691,  1.4695])

> Output 2 : tensor([ 2.2381,  0.2831,  1.0606, -2.3082, -0.1782])

As you can see, the same word has different embeddings depending on the context :+1:

### 📝 If a sentence contains more than one word, you get them all by setting `all_index=True` .

```python
text = "After stealing money from the bank vault, the bank robber was seen fishing on the Mississippi river bank."
word = "bank"

embedding = get_word_embedding_from_sentence(text,word,all_index=True)
print(embedding)
```
> Output : [embedding_1,embedding_2,embedding_3]
cppchips 0.0.1
Fast C++ Code Generation
Requires Python: >=3.7

# cppchips: C++17 code generator

#### :exclamation: Early development stage :vertical_traffic_light:

![a](https://img.shields.io/github/commit-activity/y/eSkry/cppchips?style=flat-square) ![license](https://img.shields.io/github/license/eSkry/cppchips?style=flat-square)

--------------------------------------

#### :capital_abcd: Languages: **EN** [RU](docs/README.ru.md)

#### :four_leaf_clover: Supported C++ constructions
- **class** and **struct**.
- **class constructors**.
- **class member fields** - with autogeneration getter and setter.
- **initializer list** in constructor (withoout initialize base class).
- **class member functions**.
- **virtual** functions and **override in child classes**.
- simple functions, variables.


## :rocket: Simple start

**cppchips input**:
```python
from src.cppchips import *

animalClass = CppClass('Animal')
animalClass.private \
                .add_variable(CppString(), 'var1') \

animalClass.public \
                .add_virtual_method('say', CppVoid(), [], 'std::cout << "---\\n";', noexcept=True) \
                .add_virtual_method('pay', CppInt(), [CppVariable(CppDouble(), 'cost')], '// pay to win!') \
                .add_constructor() \
                .add_getter('var1') \
                .add_setter('var1')

print(animalClass.gen_definition_str())


catClass = CppClass('Cat')
catClass.add_base_class(animalClass).public \
                                    .add_override_method('say') \
                                    .add_constructor() \
                                    .add_override_method('pay')

print(catClass.gen_definition_str())
```

**C++ result**:
```cpp
class Animal {
public:
  Animal() {}
  virtual void say() noexcept { std::cout << "---\n"; }
  virtual int pay(double cost) {
    // pay to win!
  }
  auto &getvar1() noexcept { return mvar1; }
  const auto &getvar1() const noexcept { return mvar1; }
  void setvar1(const std::string &avar1) { mvar1 = avar1; }

private:
  std::string mvar1;
};

class Cat : public Animal {
public:
  Cat() {}
  void say() noexcept override {}
  int pay(double cost) override { return {}; }
};
```
ContextBertWordEmbeddingsFrom 0.0.1
Extract bert word embedding from sentence (based on the context)
Keywords: python,nlp,bert,text,transformers,embedding,word embedding,sentence embedding,context embedding,word embedding from context,sentence embedding from context,context embedding from context,similarity

# bert-word-embeddings-from-context-sentence :shipit:

*One way to confirm contextually dependent vectors is by evaluating the model's performance on tasks that require understanding the meaning of words in context, such as natural language inference or question answering. Another way is to perform a qualitative analysis by comparing the embeddings generated for a specific word in different sentences or contexts, and observing if they are similar or dissimilar.*

```python
from ContextBertWordEmbeddingsFrom import  get_word_embedding_from_sentence

text_1 = "bring some money"
text_2 = "bring your focus to your heart"

word = "bring"

embedding_1 = get_word_embedding_from_sentence(text_1,word)
embedding_2 = get_word_embedding_from_sentence(text_2,word)

print(embedding_1[0:5])
print(embedding_2[0:5])

```
> Output 1 : tensor([ 0.1592,  0.4534,  2.8211, -1.0691,  1.4695])

> Output 2 : tensor([ 2.2381,  0.2831,  1.0606, -2.3082, -0.1782])

As you can see, the same word has different embeddings depending on the context :+1:

### 📝 If a sentence contains more than one word, you get them all by setting `all_index=True` .

```python
text = "After stealing money from the bank vault, the bank robber was seen fishing on the Mississippi river bank."
word = "bank"

embedding = get_word_embedding_from_sentence(text,word,all_index=True)
print(embedding)
```
> Output : [embedding_1,embedding_2,embedding_3]