self.covariances = {'spherical':.5+ rng.rand(n_components),'diag': (.5+ rng.rand(n_components, n_features)) **2,'tied':make_spd_matrix(n_features, random_state=rng),'full': np.array([make_spd_matrix(n_features, random_state=rng) *.5for_inrange(n_components)])} sel...
# 需要導入模塊: from sklearn import datasets [as 別名]# 或者: from sklearn.datasets importmake_spd_matrix[as 別名]defmake_covar_matrix(covariance_type, n_components, n_features, random_state=None):mincv =0.1prng = check_random_state(random_state)ifcovariance_type =='spherical':return(minc...
def make_sparse_spd_matrix( n_dim=None, n_dim=1, *, alpha=0.95, norm_diag=False, smallest_coef=0.1, largest_coef=0.9, sparse_format=None, random_state=None, dim="deprecated", ): """Generate a sparse symmetric definite positive matrix. @@ -1732,12 +1726,6 @@ def make_sparse_spd...
def make_sparse_spd_matrix( dim=1, n_dim=None, *, alpha=0.95, norm_diag=False, smallest_coef=0.1, largest_coef=0.9, sparse_format=None, random_state=None, dim="deprecated", ): """Generate a sparse symmetric definite positive matrix. Read more in the :ref:`User Guide <sample_generat...
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# CUDA 10.1 introduces "mma" in PTX performing collective matrix multiply operations. # if (CUDA_VERSION VERSION_LESS 10.1) set(CUTLASS_ENABLE_TENSOR_CORE_MMA_DEFAULT OFF) else() set(CUTLASS_ENABLE_TENSOR_CORE_MMA_DEFAULT ON) endif() # Trace levels for debugging set(CUTLASS_DEBUG...
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The article offers information on the application of selection matrix, a ranking technique to evaluate potential projects, problems, choices or remedies based on particular standards or quality dimensions. It explores the use of the simple matrix, providing steps for completing a selection matrix templa...
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