kumar-abhishek

n_steps = None
# convert into input/output
i=1
n=1
x_input = np.array([dataXScaler[0]])

x_input = x_input.reshape((1, len(x_input), n_features))
while i<len(dataXScaler):
  # demonstrate prediction
  print('Input: ')

kumar-abhishek

x_input = dataXScaler[2:30]
x_input = x_input.reshape((1, len(x_input), 4))
print(scaler.inverse_transform(dataXScaler)[2:30])
yhat = model.predict(x_input, verbose=0)
print('\nPredicted output: \n', scaler.inverse_transform(yhat))
print('\nExpected: \n', scaler.inverse_transform(dataYScaler)[30])
>>>
[[72.33333333 70.22222222 67.         58.23529412]
 [72.33333333 70.22222222 67.         58.23529412]
 [73.66666667 70.22222222 60.         55.58823529]

kumar-abhishek

x_input = dataXScaler[2:30]
x_input = x_input.reshape((1, len(x_input), 4))
print(scaler.inverse_transform(dataXScaler)[2:30])
yhat = model.predict(x_input, verbose=0)
print(scaler.inverse_transform(yhat))
print('expected: ', scaler.inverse_transform(dataYScaler)[30])
>>>
[[72.33333333 70.22222222 67.         58.23529412]
 [72.33333333 70.22222222 67.         58.23529412]
 [73.66666667 70.22222222 60.         55.58823529]

kumar-abhishek

from keras.models import Sequential
from keras import optimizers
from keras.layers import LSTM
from keras.layers import Dense, Dropout, BatchNormalization, TimeDistributed

n_features = 4
# choose a number of time steps
n_steps = None

model = Sequential()

kumar-abhishek

scaler = MinMaxScaler(feature_range=(0, 1))
dataXScaler = scaler.fit_transform(dataX)
dataYScaler = scaler.fit_transform(dataY)
print(dataXScaler[0:5])
print(dataYScaler[0:5])
>>>
[[0.44444444 0.77777778 1.         0.88235294]
 [0.44444444 0.77777778 1.         0.88235294]
 [0.44444444 0.77777778 1.         0.88235294]
 [0.44444444 0.77777778 1.         0.88235294]

kumar-abhishek

X_train_000 = pd.read_csv('chorale_000.csv')
print(X_train_000.head())
>>> 
  note0  note1  note2  note3
0     74     70     65     58
1     74     70     65     58
2     74     70     65     58
3     74     70     65     58
4     75     70     58     55

kumar-abhishek

class ChatApplication {
  login (username, password) {},
    
  logout (user) {},
    
  joinChatRoom(roomId, user) {}
    
  sendMessage(message, roomId, user) {}

  sendPrivateMessage(message, receiverId, user) {}

kumar-abhishek

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

1. Clarify and agree on the scope of the system

• User cases (description of sequences of events that, taken together, lead to a system doing something useful)
  • Who is going to use it?
  • How are they going to use it?

kumar-abhishek

###Index

• Meta-Lists
• Graphics Programming
• Language Agnostic
• Android
• Autotools
• ASP.NET MVC
• Assembly Language
• Bash
• C / C++

kumar-abhishek

Programming Achievements: How to Level Up as a Developer

This gist is part of a blog post. Check it out at:

http://jasonrudolph.com/blog/2011/08/09/programming-achievements-how-to-level-up-as-a-developer