codes_for_articles's People
Forkers
alaparthishivaji torreaopt molly-abisage oozsan zolbanin mukul-mschauhan parthi29 zehavi11 reachanu21 lucas-kitzmueller stevenchng388 miksict imane-elhaouzi chenxshani krenusz rrfaria edgarin1st emily23king jinyoung seonghankim s8devnav p5149247263 hzahedi jakn09ab adarshv96 patrikios levansy-ai rchaitanyapradeep snussik amirunpri2018 danielschulz csmmei helenwxp kinji20 drroad florpose sainiudit silverf62 teddovanmierle ravishankar-as gayathrig269 minamousa-cognite ctrivino1 rodrigo305 casimeer datasolver ritwish madrigaljose vlamv anuragiitr pacov anuragvyas1989 glaceage akashchauhansoftengi songnku luge67 matthiasak usadetroit satkol artifice-shell blessvskp aseyoum123 d3mi4n taehoonkoo davidmayag jairof002 sylviaaaaa23 maodo86 marc499 ashish7398 daiyirita expertxe ganastas alexdgithub shaon11579 liuxiaolong98 ginger-tec josemagalan cjelsa wanludrame rnibhanupudi3 nausya risto-trajanov wac81 simomaestri nicolizamacorrea sibeer theresazhu21 fnsneuralsimulator kkasravi mugipham fd2013 darshitpurohit101 atewamba lyimeng tmk1363 asallon akpathak78 diveyez pa-wancodes_for_articles's Issues
Question, trying to retrofit your code to work with adj close, and volume to predict price
I read your article on medium
https://medium.com/swlh/a-technical-guide-on-rnn-lstm-gru-for-stock-price-prediction-bce2f7f30346
Modified part of the code to accept volume
def ts_train_test(all_data,time_steps,for_periods):
'''
input:
data: dataframe with dates and price data
output:
X_train, y_train: data from 2013/1/1-2018/12/31
X_test: data from 2019 -
sc: insantiated MinMaxScaler object fit to the training data
'''
# create training and test set
#all_data.iloc[:,[0, -1]].values
ts_train = all_data[:'2018'].iloc[:,[0, -1]].values
ts_test = all_data['2019':].iloc[:,[0, -1]].values
ts_train_len = len(ts_train)
ts_test_len = len(ts_test)
# create training data of s samples and t time steps
X_train = []
y_train = []
y_train_stacked = []
for i in range(time_steps,ts_train_len-1):
X_train.append(ts_train[i-time_steps:i,0:])
y_train.append(ts_train[i:i+for_periods,0:])
X_train, y_train = np.array(X_train), np.array(y_train)
# Reshaping X_train for efficient modelling
X_train = np.reshape(X_train, (X_train.shape[0],X_train.shape[1],2))
inputs = pd.concat((all_data[["Adj Close","Volume"]][:'2018'], all_data[["Adj Close","Volume"]]['2019':]),axis=0).values
inputs = inputs[len(inputs)-len(ts_test) - time_steps:]
inputs = inputs.reshape(-1,2)
#inputs
# Preparing X_test
X_test = []
for i in range(time_steps,ts_test_len+time_steps-for_periods):
X_test.append(inputs[i-time_steps:i,0:])
X_test = np.array(X_test)
X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1],2))
return X_train, y_train , X_test
X_train, y_train, X_test = ts_train_test(all_data,5,2)
X_train.shape[0],X_train.shape[1]
but when I get to this part, I'm confused how to modify the hidden layers
def simple_rnn_model(X_train, y_train, X_test):
'''
create single layer rnn model trained on X_train and y_train
and make predictions on the X_test data
'''
# create a model
from keras.models import Sequential
from keras.layers import Dense, SimpleRNN
my_rnn_model = Sequential()
my_rnn_model.add(SimpleRNN(64, return_sequences=True))
#my_rnn_model.add(SimpleRNN(32, return_sequences=True))
#my_rnn_model.add(SimpleRNN(32, return_sequences=True))
my_rnn_model.add(SimpleRNN(64))
my_rnn_model.add(Dense(2)) # The time step of the output
my_rnn_model.compile(optimizer='rmsprop', loss='mean_squared_error')
# fit the RNN model
my_rnn_model.fit(X_train, y_train, epochs=100, batch_size=150, verbose=0)
# Finalizing predictions
rnn_predictions = my_rnn_model.predict(X_test)
return my_rnn_model, rnn_predictions
my_rnn_model, rnn_predictions = simple_rnn_model(X_train, y_train, X_test)
rnn_predictions[1:10]
Script execution error. Syntax error in stock.py
Is Autoencoder only trained with normal samples for Anomaly Detection?
Hello,
I have looked into "PyOD Tutorial - autoencoder.ipynb", as I understood the X_train dataframe contains normal and anomalous samples. This is the way I also would choose, but in other explanations/articles the model is fitted only with normal samples. So I am not sure which one is the correct one, or are both ways correct?
I would be grateful, if you could answer me this question.
Thank you!
there maybe sth wrong in the code ( VGG16.ipynb ( In 94 (model.to('cuda'))
model.to(‘cuba’) should be changed to vgg16.to('cuda')
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.