使用toarray()方法,我们可以将稀疏矩阵转换为普通矩阵。 # 将稀疏矩阵转换为普通矩阵dense_matrix=sparse_matrix.toarray() 1. 2. toarray()方法将稀疏矩阵转换为一个常规的 Numpy 数组。 4. 输出普通矩阵 最后,我们可以输出转换后的普通矩阵,以便检查结果。 # 输出转换后的普通矩阵print(dense_matrix) 1. 2....
import scipy.sparse as sparse import scipy.io as sio import scipy.stats as stats import numpy as np创建一个稀疏矩阵np.random.seed(42) rvs = stats.poisson(15, loc=10).rvs sparse_matrix = sparse.random(500, 25, density=0.25, format="csr")将稀疏矩阵转换成稠密矩阵sparse_matrix.todense() ...
5,2])row_indices=np.array([0,1,2])col_indices=np.array([2,1,3])sparse_matrix=csr_matrix((data,(row_indices,col_indices)),shape=(3,4))print("原始稀疏矩阵:")print(sparse_matrix.todense())# 进行行归一化normalized_matrix=normalize(sparse_matrix,axis=1,norm...
dok_matrix(arg1[, shape, dtype, copy])Dictionary Of Keys based sparse matrix dok_matrix可以高效地逐渐构造稀疏矩阵。 >>> S = dok_matrix((5, 5), dtype=np.float32) >>> for i in range(5): ... for j in range(5): ... S[i, j] = i + j >>> S.toarray() array([[0., ...
>>> from scipy.sparse import coo_matrix, vstack >>> A = coo_matrix([[1,2],[3,4]]) >>> B = coo_matrix([[5,6]]) >>> vstack( [A,B] ).todense() matrix([[1, 2], [3, 4], [5, 6]]) 但是经过测试,如果A和B的数据形式不一样,不能合并。比如A存储的是字符串,B是数字,...
csr_matrix(train_dummies.astype(np.int8)) train_dtm_numeric = sparse.hstack((train_dtm, train_numeric)) 多项式逻辑回归 逻辑回归还提供了一种多项式训练选项,比一对多实现更快且更准确。我们使用lbfgs求解器(有关详细信息,请参阅 GitHub 上链接的 sklearn 文档): multi_logreg = LogisticRegression(C=1...
x = (sparse.csc_matrix((data[:,2], x_p.T)).astype(float))[:, :].todense() nUser = x.shape[0] #可视化矩阵 pyplot.imshow(x, interpolation='nearest') pyplot.xlabel('用户') pyplot.ylabel('用户') pyplot.xticks(range(nUser)) ...
# 创建一个矩阵matrix = np.array([[1,2,3],[1, 2, 4],[1, 2, 5]]) 1 1.3创建稀疏矩阵 稀疏矩阵(Sparse Matrix)是一种特殊类型的矩阵,其中大多数元素都是零。与稠密矩阵(Dense Matrix)相比,稀疏矩阵具有许多零元素,这些零元素...
Embedding(len(word_index) + 1, 300, weights=[embedding_matrix], trainable=False)(input_layer) embedding_layer = layers.SpatialDropout1D(0.3)(embedding_layer) # Add the LSTM Layer lstm_layer = layers.LSTM(100)(embedding_layer) # Add the output Layers output_layer1 = layers.Dense(50, ...
@bryan-woodsI was able to find a better work around withtocsc. There is probably some performance penalty but not nearly as bad as making it a dense matrix. Including this in my sklearn pipeline right before xgboost worked classCSCTransformer(TransformerMixin):deftransform(self,X,y=None,**fit...