Everything runs normally up until the MinMaxScaler is called in the for loop.
high_prices = df.loc[:,'High'].as_matrix()
low_prices = df.loc[:,'Low'].as_matrix()
mid_prices = (high_prices+low_prices)/2.0
print(mid_prices)
[20.25 19.865 20.34 ... 27.97 27.62 27.3425]
main:1: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
main:2: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
smoothing_window_size = 2500
for di in range(0,10000,smoothing_window_size):
scaler.fit(train_data[di:di+smoothing_window_size,:])
train_data[di:di+smoothing_window_size,:] = scaler.transform(train_data[di:di+smoothing_window_size,:])
You normalize the last bit of remaining data
scaler.fit(train_data[di+smoothing_window_size:,:])
train_data[di+smoothing_window_size:,:] = scaler.transform(train_data[di+smoothing_window_size:,:])
Traceback (most recent call last):
File "", line 3, in
scaler.fit(train_data[di:di+smoothing_window_size,:])
File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py", line 325, in fit
return self.partial_fit(X, y)
File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py", line 353, in partial_fit
force_all_finite="allow-nan")
File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 550, in check_array
context))
ValueError: Found array with 0 sample(s) (shape=(0, 1)) while a minimum of 1 is required by MinMaxScaler.