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Hi Can you please help with scale back those prediction value function?
I tried this but values are bit different Note: made preprocessor as global variable
def normalize_data(x, y): global preprocessor linear_x = x.reshape((x.shape[0], x.shape[1] * x.shape[2])) #print("x.shape", x.shape) #print("linear_x.shape", linear_x.shape) xy = np.c_[ linear_x, y, y, y, y, y, y] #print("xy.shape", xy.shape) xy_scaled = preprocessor.inverse_transform(xy, copy=True) #print("xy_scaled.shape", xy_scaled.shape) #print(xy_scaled[:, -1].tolist())
return xy_scaled[:, -1].tolist()
Will be waiting for your reply
The text was updated successfully, but these errors were encountered:
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Hi
Can you please help with scale back those prediction value function?
I tried this but values are bit different
Note: made preprocessor as global variable
def normalize_data(x, y):
global preprocessor
linear_x = x.reshape((x.shape[0], x.shape[1] * x.shape[2]))
#print("x.shape", x.shape)
#print("linear_x.shape", linear_x.shape)
xy = np.c_[ linear_x, y, y, y, y, y, y]
#print("xy.shape", xy.shape)
xy_scaled = preprocessor.inverse_transform(xy, copy=True)
#print("xy_scaled.shape", xy_scaled.shape)
#print(xy_scaled[:, -1].tolist())
Will be waiting for your reply
The text was updated successfully, but these errors were encountered: