我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用scipy.sparse.isspmatrix_coo()。
def sparse_to_tuple(sparse_mx): if not sp.isspmatrix_coo(sparse_mx): sparse_mx = sparse_mx.tocoo() coords = np.vstack((sparse_mx.row, sparse_mx.col)).transpose() values = sparse_mx.data shape = sparse_mx.shape return coords, values, shape
def inverse_transform(self, X): """Return terms per document with nonzero entries in X. Parameters ---------- X : {array, sparse matrix}, shape = [n_samples, n_features] Returns ------- X_inv : list of arrays, len = n_samples List of arrays of terms. """ if sp.isspmatrix_coo(X): # COO matrix is not indexable X = X.tocsr() elif not sp.issparse(X): # We need to convert X to a matrix, so that the indexing # returns 2D objects X = np.asmatrix(X) n_samples = X.shape[0] terms = np.array(self.k_to_i.keys()) indices = np.array(self.k_to_i.values()) inverse_vocabulary = terms[np.argsort(indices)] return [inverse_vocabulary[X[i, :].nonzero()[1]].ravel() for i in xrange(n_samples)]
def save_sparse(sparse_matrix, output_filename): assert sparse.issparse(sparse_matrix) if sparse.isspmatrix_coo(sparse_matrix): coo = sparse_matrix else: coo = sparse_matrix.tocoo() row = coo.row col = coo.col data = coo.data shape = coo.shape np.savez(output_filename, row=row, col=col, data=data, shape=shape)
def test_add_dummy_feature_coo(): X = sparse.coo_matrix([[1, 0], [0, 1], [0, 1]]) X = add_dummy_feature(X) assert_true(sparse.isspmatrix_coo(X), X) assert_array_equal(X.toarray(), [[1, 1, 0], [1, 0, 1], [1, 0, 1]])
def add_dummy_feature(X, value=1.0): """Augment dataset with an additional dummy feature. This is useful for fitting an intercept term with implementations which cannot otherwise fit it directly. Parameters ---------- X : {array-like, sparse matrix}, shape [n_samples, n_features] Data. value : float Value to use for the dummy feature. Returns ------- X : {array, sparse matrix}, shape [n_samples, n_features + 1] Same data with dummy feature added as first column. Examples -------- >>> from sklearn.preprocessing import add_dummy_feature >>> add_dummy_feature([[0, 1], [1, 0]]) array([[ 1., 0., 1.], [ 1., 1., 0.]]) """ X = check_array(X, accept_sparse=['csc', 'csr', 'coo'], dtype=FLOAT_DTYPES) n_samples, n_features = X.shape shape = (n_samples, n_features + 1) if sparse.issparse(X): if sparse.isspmatrix_coo(X): # Shift columns to the right. col = X.col + 1 # Column indices of dummy feature are 0 everywhere. col = np.concatenate((np.zeros(n_samples), col)) # Row indices of dummy feature are 0, ..., n_samples-1. row = np.concatenate((np.arange(n_samples), X.row)) # Prepend the dummy feature n_samples times. data = np.concatenate((np.ones(n_samples) * value, X.data)) return sparse.coo_matrix((data, (row, col)), shape) elif sparse.isspmatrix_csc(X): # Shift index pointers since we need to add n_samples elements. indptr = X.indptr + n_samples # indptr[0] must be 0. indptr = np.concatenate((np.array([0]), indptr)) # Row indices of dummy feature are 0, ..., n_samples-1. indices = np.concatenate((np.arange(n_samples), X.indices)) # Prepend the dummy feature n_samples times. data = np.concatenate((np.ones(n_samples) * value, X.data)) return sparse.csc_matrix((data, indices, indptr), shape) else: klass = X.__class__ return klass(add_dummy_feature(X.tocoo(), value)) else: return np.hstack((np.ones((n_samples, 1)) * value, X))
