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class AttentionWithContext(Layer):
    """
        Attention operation, with a context/query vector, for temporal data.
        Supports Masking.
        Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf]
        "Hierarchical Attention Networks for Document Classification"
        by using a context vector to assist the attention
        # Input shape
            3D tensor with shape: `(samples, steps, features)`.
        # Output shape

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#! encoding=UTF-8
"""
kernel canonical correlation analysis
"""

import numpy as np
from scipy.linalg import svd
from sklearn.metrics.pairwise import pairwise_kernels, euclidean_distances

class KCCA(object):

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import pandas as pd
data = pd.read_csv("file.csv", sep=",")
print(data.head(2))
with open('file.json', 'w') as f:
    f.write(data.to_json(orient='records', lines=True))

# check
data = pd.read_json("file.json", lines=True)
print(data.head(2))

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LaTeX to PNG on Windows

Prerequisite

• Windows System (Tested on Windows 10)
• pdflatex TexLive (/w standalone package)
  • Installing Tex Live over the Internet
• magick Image Magick
  • choco install imagemagick
• gswin32c Ghost Script (x86)

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from ekphrasis.classes.preprocessor import TextPreProcessor
from ekphrasis.classes.tokenizer import SocialTokenizer
from ekphrasis.dicts.emoticons import emoticons
import numpy as np

import re
import io

label2emotion = {0: "others", 1: "happy", 2: "sad", 3: "angry"}
emotion2label = {"others": 0, "happy": 1, "sad": 2, "angry": 3}

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import spacy
from spacy.lang.pt.stop_words import STOP_WORDS
from sklearn.feature_extraction.text import CountVectorizer
import pt_core_news_sm
nlp = pt_core_news_sm.load()

with open("original_text.txt", "r", encoding="utf-8") as f:
        text = " ".join(f.readlines())
        
doc = nlp(text)

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corpus = [sent.text.lower() for sent in doc.sents ]
cv = CountVectorizer(stop_words=list(STOP_WORDS))   
cv_fit=cv.fit_transform(corpus)    
word_list = cv.get_feature_names();    
count_list = cv_fit.toarray().sum(axis=0)    

"""The zip(*iterables) function takes iterables as arguments and returns an iterator. 
This iterator generates a series of tuples containing elements from each iterable. 
Let's convert these tuples to {word:frequency} dictionary"""

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val=sorted(word_frequency.values())
higher_word_frequencies = [word for word,freq in word_frequency.items() if freq in val[-3:]]
print("\nWords with higher frequencies: ", higher_word_frequencies)

# gets relative frequencies of words
higher_frequency = val[-1]
for word in word_frequency.keys():  
    word_frequency[word] = (word_frequency[word]/higher_frequency)

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sentence_rank={}
for sent in doc.sents:
    for word in sent :       
        if word.text.lower() in word_frequency.keys():            
            if sent in sentence_rank.keys():
                sentence_rank[sent]+=word_frequency[word.text.lower()]
            else:
                sentence_rank[sent]=word_frequency[word.text.lower()]

top_sentences=(sorted(sentence_rank.values())[::-1])

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summary=[]
for sent,strength in sentence_rank.items():  
    if strength in top_sent:
        summary.append(sent)
    else:
        continue

for i in summary:
    print(i,end=" ")