-
Notifications
You must be signed in to change notification settings - Fork 0
/
trainingOpen.py
44 lines (31 loc) · 1.19 KB
/
trainingOpen.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
'''
Data preprosessing and reshaping
Training only the data of open column from df
'''
# get the data and reduce to open set
from ProcessedDataframe import trainData
df = trainData()
open_trainig_set = df['Open']
import warnings
warnings.filterwarnings('ignore')
#################################################################
import numpy as np
# converting the data set to np array and reshaping
open_trainig_set = np.array(open_trainig_set)
open_trainig_set = open_trainig_set.reshape(-1,1)
###################################################################
# scaling the dataset
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range = (0,1))
# transfrom every data to a particular value either 0 or < 1
training_set_scaled = sc.fit_transform(open_trainig_set)
###################################################################
x_train = []
y_train = []
# creating data structure with 60 timesteps with 1 output
for i in range(60,1258):
x_train.append(training_set_scaled[i-60:i, 0])
y_train.append(training_set_scaled[i,0])
x_train, y_train = np.array(x_train), np.array(y_train)
# reshaping
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))