Human-Activity-Recognition--Using-Deep-NN
Human-Activity-Recognition--Using-Deep-NN copied to clipboard
UdiBhaskar
throw some more light on the code
def fit(self, X):
# remove overlaping
remove = int(X.shape[1] / 2)
temp_X = X[:, -remove:, :]
Can you explain a bit more briefly as to why we are performing the above code ?
regards jana
Hi,
from sklearn.base import BaseEstimator, TransformerMixin
class scaling_tseries_data(BaseEstimator, TransformerMixin):
from sklearn.preprocessing import StandardScaler
def __init__(self):
self.scale = None
def transform(self, X):
temp_X1 = X.reshape((X.shape[0] * X.shape[1], X.shape[2]))
temp_X1 = self.scale.transform(temp_X1)
return temp_X1.reshape(X.shape)
def fit(self, X):
# remove overlaping
remove = int(X.shape[1] / 2)
temp_X = X[:, -remove:, :]
# flatten data
temp_X = temp_X.reshape((temp_X.shape[0] *
temp_X.shape[1], temp_X.shape[2])) scale = StandardScaler() scale.fit(temp_X) ##saving for furter usage ## will use in predicton pipeline pickle.dump(scale,open('Scale_2class.p','wb')) self.scale = scale return self
My question is regarding these two lines
remove = int(X.shape[1] / 2)
temp_X = X[:, -remove:, :]
I understand that you are trying to split the data into two datasets, but why is this required.
regards
On Fri, Oct 30, 2020 at 2:59 PM atomtony [email protected] wrote:
What version of python
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