The matrix as a scipy.sparse matrix in CSR format. Example: A = model.getA() sense = numpy.array(model.getAttr("Sense",model.getConstrs())) # extract sub matrices of (in)equality constraints Aeq = A[sense == '=', :] Ale = A[sense == '<', :] Age = A[sense == '>', ...
PyData Sparse is maturing, but there's still some way to go. Note that it does have a format (CSD) that generalizes CSR/CSC, seeAdd CSDpydata/sparse#258. The roadmaphttps://scipy.github.io/devdocs/roadmap.html#implement-sparse-arrays-in-addition-to-sparse-matricesstates that at some ...
GPUGNN/ ├── graph_generation.py # Generates synthetic and real-world test graphs ├── claude_pycuda_sparse.py # Base sparse matrix multiplication implementation ├── claude_pycuda_sparse_csr_csc.py # CSR-CSC optimized implementation ├── claude_pycuda_sparse_tiled.py # Tiled implement...
If as_csr=True and as_binary_data_stream=False then return the transformed data in CSR (sparse matrix) format. If as_binary_data_stream is also true then that parameter takes precedence over as_csr and the output will be an IDV file. Returns 展开表 TypeDescription Returns a pandas ...
csr_matrix(y_dense) >>> print(y_sparse) (0, 0) 1 (1, 2) 1 (2, 0) 1 (3, 1) 1 OneVsRestClassifierhttps://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#sklearn.multiclass.OneVsRestClassifier...
Additional arguments. Ifas_csr=Trueandas_binary_data_stream=Falsethen return the transformed data in CSR (sparse matrix) format. Ifas_binary_data_streamis also true then that parameter takes precedence overas_csrand the output will be an IDV file. ...
from scipy.sparse import lil_matriximport numpy as npfrom .validation import check_array, _assert_all_finitedef _unique_multiclass(y): if hasattr(y, "__array__"): return np.unique(np.asarray(y)) else: return set(y)def _unique_indicator(y): ...
Hello! We are having an issue in our repo where 8.3.4 pytest (but not 8.3.3) caused our classes to have strange behavior at runtime with errors like: if (dest_type, src_type, modifiers) not in self.write: > raise IORegistryError._from_wr...
(features_path,header=None,index_col=1,sep="\t",usecols=[0,1])obs.index.name=Nonevar.index.name=Nonevar.columns=["gene_ids"]# print(var.head())mtx=mmread(mtx_path)mtx=mtx.Tmtx=csr_matrix(mtx)adata=sc.AnnData(mtx,obs=obs,var=var)adata.var_names_make_unique()returnadata>>>Save...
In the single label multiclass case, the rows of the returned matrix sum to 1.Parameters --- X : array-like, shape = [n_samples, n_features]Returns --- T : (sparse) array-like, shape = [n_samples, n_classes] Returns the probability...