poly_features=np.power(features,np.arange(max_degree).reshape(1,-1))什么意思

这行代码的意思是将features的每个元素分别取0到max_degree-1次幂，并将结果按行组成一个二维数组poly_features。其中，np.power函数是numpy库中的函数，可以计算数组元素的幂。reshape(1,-1)的作用是将一维数组转换为二维数组，其中第一个维度为1，第二个维度自动计算。

相关问题

poly_features=np.power(features,np.arange(max_degree).reshape(1,-1))

This code creates a matrix of polynomial features from the input features. The variable "max_degree" specifies the highest degree of the polynomial features.

The function "np.power()" raises each element of the input features to the power of the corresponding element in the sequence of degrees from 0 to (max_degree-1). The resulting matrix has one row for each input feature and one column for each degree of the polynomial.

For example, if the input features are [x1, x2] and max_degree=3, the resulting matrix would be:

[[1, x1, x1^2, x1^3],
[1, x2, x2^2, x2^3]]

def load_cora(): path = 'data/cora/' data_name = 'cora' print('Loading from raw data file...') idx_features_labels = np.genfromtxt("{}{}.content".format(path, data_name), dtype=np.dtype(str)) features = sp.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32) _, _, labels = np.unique(idx_features_labels[:, -1], return_index=True, return_inverse=True) idx = np.array(idx_features_labels[:, 0], dtype=np.int32) idx_map = {j: i for i, j in enumerate(idx)} edges_unordered = np.genfromtxt("{}{}.cites".format(path, data_name), dtype=np.int32) edges = np.array(list(map(idx_map.get, edges_unordered.flatten())), dtype=np.int32).reshape(edges_unordered.shape) adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])), shape=(labels.shape[0], labels.shape[0]), dtype=np.float32) adj = adj.T + adj adj = adj.minimum(1) return features.toarray(), idx_map, adj.toarray(), labels

这是一个函数，用于从Cora数据集的原始数据文件中加载数据。它返回特征、节点索引映射、邻接矩阵和标签。具体来说，它使用numpy库中的genfromtxt函数从包含节点特征和标签的文件中加载数据；使用coo_matrix函数从包含节点之间引用关系的文件中加载数据，并将其转换为邻接矩阵形式；最后，对邻接矩阵进行了处理，使其成为一个二元关系矩阵。