karthikraja95

neural_model = NeuralProphet()
metrics = neural_model.fit(stock_prophet_df, freq='D')
metrics.head()

neural_future = neural_model.make_future_dataframe(stock_prophet_df, periods = 365, n_historic_predictions=len(stock_prophet_df))
neural_forecast = neural_model.predict(neural_future)

# Plot the Predictions
neural_figure = neural_model.plot(neural_forecast)

karthikraja95

stock_prophet_df = stock_df[['formatted_date', 'adjclose']]
stock_prophet_df = stock_prophet_df.rename(columns = {'formatted_date': 'ds' , 'adjclose': 'y'})
stock_prophet_df

karthikraja95

# Arrange the data in asecending order
stock = stock_df.sort_values('formatted_date')

# Plot date and average price
plt.figure(figsize = (10,10))
plt.plot(stock_df['formatted_date'], stock_df['adjclose'])

karthikraja95

# import libraries 
import pandas as pd # Import Pandas for data manipulation using dataframes
import numpy as np # Import Numpy for data statistical analysis 
import matplotlib.pyplot as plt # Import matplotlib for data visualisation
import random
import seaborn as sns
from neuralprophet import NeuralProphet
from yahoofinancials import YahooFinancials

karthikraja95

fig, ax = plt.subplots(1, 2, figsize=(18, 6))

## Code for plot 1 ##

sns.lineplot(global_cases.index, np.exp(y), ax=ax[0])
sns.lineplot(global_cases.index, np.exp(model.predict(x)), ax=ax[0])

ax[0].grid(axis='both', alpha=.2)

sns.despine(top=True, left=True)

karthikraja95

g = global_cases/global_cases.shift(1)


fig, ax = plt.subplots(figsize=(18, 6))
                       
sns.lineplot(x=g.index, y=g.values, linewidth=3, ax=ax)
sns.lineplot(x=g.index, y=g.rolling(5).mean().values, linewidth=2, ax=ax, color='blue', alpha=.5)
sns.lineplot(x=g.index, y=g.rolling(15).mean().values, linewidth=2, ax=ax, color='orange')

sns.despine(top=True, left=True)

karthikraja95

ig, ax = plt.subplots(1, 2, figsize=(18, 6))


## Code for plot 1 ##

sns.lineplot(x=global_cases.index, y=global_cases.values, linewidth=3, ax=ax[0])

sns.despine(top=True, left=True)

ax[0].grid(axis='both', alpha=.2)

karthikraja95

import matplotlib.pyplot as plt
import numpy as np 
import os
import pandas as pd
import seaborn as sns
import warnings

from datetime import datetime, timedelta
from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet
from sklearn.metrics import r2_score

karthikraja95

# weights for each style layer 
# weighting earlier layers more will result in *larger* style artifacts
# notice we are excluding `conv4_2` our content representation
style_weights = {'conv1_1': 1.,
                 'conv2_1': 0.8,
                 'conv3_1': 0.5,
                 'conv4_1': 0.3,
                 'conv5_1': 0.1}

# you may choose to leave these as is

karthikraja95

def get_features(image, model, layers=None):
    """ Run an image forward through a model and get the features for 
        a set of layers. Default layers are for VGGNet matching Gatys et al (2016)
    """
    
    ## The mapping layer names of PyTorch's VGGNet to names from the paper
    ## Need the layers for the content and style representations of an image
    if layers is None:
        layers = {'0': 'conv1_1',
                  '5': 'conv2_1',