Did I find the right examples for you? yes no      Crawl my project      Python Jobs

All Samples(5)  |  Call(5)  |  Derive(0)  |  Import(0)

src/s/c/scikit-learn-0.14.1/sklearn/datasets/covtype.py   scikit-learn(Download)
        y = Xy[:, -1].astype(np.int32)
 
        joblib.dump(X, samples_path, compress=9)
        joblib.dump(y, targets_path, compress=9)
 

src/s/c/scikit-learn-0.14.1/sklearn/datasets/twenty_newsgroups.py   scikit-learn(Download)
        X_train = vectorizer.fit_transform(data_train.data).tocsr()
        X_test = vectorizer.transform(data_test.data).tocsr()
        joblib.dump((X_train, X_test), target_file, compress=9)
 
    # the data is stored as int16 for compactness

src/s/c/scikit-learn-0.14.1/sklearn/datasets/olivetti_faces.py   scikit-learn(Download)
        mfile = loadmat(buf)
        faces = mfile['faces'].T.copy()
        joblib.dump(faces, join(data_home, TARGET_FILENAME), compress=6)
        del mfile
    else:

src/s/c/scikit-learn-0.14.1/sklearn/datasets/california_housing.py   scikit-learn(Download)
            # skip the first 27 lines (documentation)
            cal_housing = np.loadtxt(cadata, skiprows=27)
            joblib.dump(cal_housing, join(data_home, TARGET_FILENAME),
                        compress=6)
        finally: