Aditya Singh Aditya1001001

## Vector.py
class Vector():
    def __new__(cls, x, y):
        print("__new__ was invoked")
        instance = object.__new__(cls)
        return instance

    def __init__(self, x, y):
        print("__init__ was invoked")
        self.x = x
        self.y = y

## linear_regression_plot.py
import seaborn as sns
sns.set(rc={'figure.figsize':(12,8)})


df = sns.load_dataset('iris')

sns.regplot(x = "sepal_length",
            y = "petal_length",
            data = df,
           color="r")

## example_loss_plane.py
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import numpy as np

fig = plt.figure()
ax = fig.gca(projection='3d')   # Create the axes

# Data
X = np.linspace(-8, 8, 100)

## polynomial_example_poly_plot.py
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
% matplotlib inline
from sklearn.preprocessing import PolynomialFeatures

n_samples = 100
X = np.linspace(0, 10, 100)
y = X ** 3 + np.random.randn(n_samples) * 100 + 100

## polynomial_example_linear_plot.py
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
% matplotlib inline
from sklearn.linear_model import LinearRegression

n_samples = 100
X = np.linspace(0, 10, 100)
y = X ** 3 + np.random.randn(n_samples) * 100 + 100

## rename_image.py
import os
count = 0
for i in os.listdir():
    os.rename(i,str(count)+ '.'+ i.split('.')[-1])
    count+=1

## index.ipynb

      
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                Aditya1001001
                / index.ipynb
            
            
              Created
              September 15, 2021 11:48
                — forked from georgeblck/index.ipynb
            
              
                Comparison of openCV Interpolation methods by Anthony Tanbakuchi (Backup because his site is down)
              
          
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## consolidated_pipeline.py
def dividend_info(article):
  headline = nlp(article['title'])
  if 'date' in [token.text.lower() for token in headline]:
    date = get_date(headline)
    if date:
      org = get_org(headline)
      ticker = get_ticker(headline)
      amount = get_amount_summary(nlp(article['summary']))
      pay_date = get_pay_date(nlp(article['summary']))
      print("HEADLINE: " + article['title'])

## build_train_doc2vec.py
def tagged_document(list_of_list_of_words):
   for i, list_of_words in enumerate(list_of_list_of_words):
      yield gensim.models.doc2vec.TaggedDocument(list_of_words, [i])

training_data = list(tagged_document(data))
model = gensim.models.doc2vec.Doc2Vec(vector_size=40, min_count=2, epochs=30)

model.build_vocab(training_data)
model.train(training_data, total_examples=model.corpus_count, epochs=model.epochs)

## newscatcher_website_details.py
from newscatcher import describe_url

websites = ['nytimes.com', 'cronachediordinariorazzismo.org', 'libertaegiustizia.it']

for website in websites:
   print(describe_url(website))
	class Vector():
	def __new__(cls, x, y):
	print("__new__ was invoked")
	instance = object.__new__(cls)
	return instance

	def __init__(self, x, y):
	print("__init__ was invoked")
	self.x = x
	self.y = y
	import seaborn as sns
	sns.set(rc={'figure.figsize':(12,8)})


	df = sns.load_dataset('iris')

	sns.regplot(x = "sepal_length",
	y = "petal_length",
	data = df,
	color="r")
	import matplotlib.pyplot as plt
	from mpl_toolkits.mplot3d import Axes3D
	from matplotlib import cm
	import numpy as np

	fig = plt.figure()
	ax = fig.gca(projection='3d') # Create the axes

	# Data
	X = np.linspace(-8, 8, 100)
	import os
	count = 0
	for i in os.listdir():
	os.rename(i,str(count)+ '.'+ i.split('.')[-1])
	count+=1
	def dividend_info(article):
	headline = nlp(article['title'])
	if 'date' in [token.text.lower() for token in headline]:
	date = get_date(headline)
	if date:
	org = get_org(headline)
	ticker = get_ticker(headline)
	amount = get_amount_summary(nlp(article['summary']))
	pay_date = get_pay_date(nlp(article['summary']))
	print("HEADLINE: " + article['title'])
	def tagged_document(list_of_list_of_words):
	for i, list_of_words in enumerate(list_of_list_of_words):
	yield gensim.models.doc2vec.TaggedDocument(list_of_words, [i])

	training_data = list(tagged_document(data))
	model = gensim.models.doc2vec.Doc2Vec(vector_size=40, min_count=2, epochs=30)

	model.build_vocab(training_data)
	model.train(training_data, total_examples=model.corpus_count, epochs=model.epochs)
	from newscatcher import describe_url

	websites = ['nytimes.com', 'cronachediordinariorazzismo.org', 'libertaegiustizia.it']

	for website in websites:
	print(describe_url(website))