Hi, I have been trying to set this up and I receive this error after running the script in the readme. Please let me know how to fix; I am not very familiar with python. Thanks!
File "main.py", line 16, in
rnn = RNN([1, 100, 100, 1]) # number of neurons in each layer
File "/usr/local/lib/python3.7/site-packages/bulbea-0.1.0-py3.7.egg/bulbea/learn/models/ann.py", line 29, in init
TypeError: init() missing 1 required positional argument: 'units'
The whole script:
import bulbea as bb
share = bb.Share('WIKI', 'GOOGL')
share.data
from bulbea.learn.evaluation import split
Xtrain, Xtest, ytrain, ytest = split(share, 'Close', normalize = True)
import numpy as np
Xtrain = np.reshape(Xtrain, (Xtrain.shape[0], Xtrain.shape[1], 1))
Xtest = np.reshape(Xtest, (Xtest.shape[0], Xtest.shape[1], 1))
from bulbea.learn.models import RNN
rnn = RNN([1, 100, 100, 1]) # number of neurons in each layer
rnn.fit(Xtrain, ytrain)
from sklearn.metrics import mean_squared_error
p = rnn.predict(Xtest)
mean_squared_error(ytest, p)
import matplotlib.pyplot as pplt
pplt.plot(ytest)
pplt.plot(p)
pplt.show()
Hi, I have been trying to set this up and I receive this error after running the script in the readme. Please let me know how to fix; I am not very familiar with python. Thanks!
File "main.py", line 16, in
rnn = RNN([1, 100, 100, 1]) # number of neurons in each layer
File "/usr/local/lib/python3.7/site-packages/bulbea-0.1.0-py3.7.egg/bulbea/learn/models/ann.py", line 29, in init
TypeError: init() missing 1 required positional argument: 'units'
The whole script:
import bulbea as bb
share = bb.Share('WIKI', 'GOOGL')
share.data
from bulbea.learn.evaluation import split
Xtrain, Xtest, ytrain, ytest = split(share, 'Close', normalize = True)
import numpy as np
Xtrain = np.reshape(Xtrain, (Xtrain.shape[0], Xtrain.shape[1], 1))
Xtest = np.reshape(Xtest, (Xtest.shape[0], Xtest.shape[1], 1))
from bulbea.learn.models import RNN
rnn = RNN([1, 100, 100, 1]) # number of neurons in each layer
rnn.fit(Xtrain, ytrain)
from sklearn.metrics import mean_squared_error
p = rnn.predict(Xtest)
mean_squared_error(ytest, p)
import matplotlib.pyplot as pplt
pplt.plot(ytest)
pplt.plot(p)
pplt.show()