data = np.asanyarray(data)
self.X = data
n, d = data.shape
return data, d
def _process_chunks(self, data, chunks):
chunks = np.asanyarray(chunks)
num_chunks = chunks.max() + 1
After Change
return self.transformer_
def _process_data(self, X):
self.X_ = X = check_array(X)
// PCA projection to remove noise and redundant information.
if self.pca_comps is not None:
pca = decomposition.PCA(n_components=self.pca_comps, svd_solver="full")
