52424aae5e284440efdb4aea30266a69553d1dde,umap/umap_.py,UMAP,transform,#UMAP#Any#,1665

Before Change

                X, self._raw_data, metric=self.input_metric, **self._input_metric_kwds
            )
            indices = np.argsort(dmat)
            dists = np.sort(dmat)  // TODO: more efficient approach
            indices = indices[:, : self.n_neighbors]
            dists = dists[:, : self.n_neighbors]
        else:

After Change

        
        // If we fit just a single instance then error
        if self.embedding_.shape[0] == 1:
            raise ValueError("Transform unavailable when model was fit with"
                             "only a single data sample.")
        // If we just have the original input then short circuit things
        X = check_array(X, dtype=np.float32, accept_sparse="csr")
        x_hash = joblib.hash(X)
        if x_hash == self._input_hash:

Instances