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fixing bugs in transform, expert demo processing, main train function, and behavior cloning class. need to get bc class parameters to return nonempty list

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Arec
2021-07-21 09:44:20 -07:00
parent 5758af5dd8
commit 1ca9914bf9
6 changed files with 30 additions and 28 deletions
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10
src/util/transform.py
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@@ -52,27 +52,27 @@ class SciKitTransform(Transform):
e.g. with reduce_dim=2, (A, B, C, D, E) will be reshaped to (A*B, C*D*E)
"""
self.tf = tf
self.reduce_dim
self.reduce_dim = reduce_dim
super(SciKitTransform, self).__init__()
def fit(self, X):
nd = X.ndim
if self.reduce_dim:
self.nfeatures = X.shape[reduce_dim:].prod()
self.nfeatures = int(torch.tensor(X.shape[self.reduce_dim:]).prod())
else:
assert nd==2, 'Invalid ndim'
self.nfeatures = X.shape[1]
self.tf.fit(X.reshape((-1,selfnfeatures)))
self.tf.fit(X.reshape((-1,self.nfeatures)))
def transform(self, X):
shape = X.shape
t = torch.tensor(self.tf.transform(X.reshape((-1,selfnfeatures))), dtype=torch.float)
t = torch.tensor(self.tf.transform(X.reshape((-1,self.nfeatures))), dtype=torch.float)
return t.reshape(shape)
def inverse_transform(self, X):
shape = X.shape
it = torch.tensor(self.tf.inverse_transform(X.reshape((-1,selfnfeatures))), dtype=torch.float)
it = torch.tensor(self.tf.inverse_transform(X.reshape((-1,self.nfeatures))), dtype=torch.float)
return it.reshape(shape)
class SciKitStandardScaler(SciKitTransform):