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classificationDefinitionMap | DBFlute
code=FOO; name=Foo; alias=Who; comment=Fooさん ; subItemMap=map:{...codeType=String} ; map: {code=PRV;name=Provisional;alias=仮会員 ;c...dbflute.seasar.org/ja/manual/reference/dfprop/classificationdefinition/index.html -
Density Estimation for a Gaussian mixture — sci...
CS = plt . contour ( X , Y , Z , norm = LogNorm ( vmin = 1.0...CB = plt . colorbar ( CS , shrink = 0.8 , extend = "both" ) plt...scikit-learn.org/stable/auto_examples/mixture/plot_gmm_pdf.html -
LeavePOut — scikit-learn 1.6.1 documentation
Train: index=[2 3] Test: index=[0 1] Fold 1: Train: index=[1 3] Test:...Test: index=[0 2] Fold 2: Train: index=[1 2] Test: index=[0 3] Fold...scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeavePOut.html -
Kernel Density Estimation — scikit-learn 1.6.1 ...
pca = PCA ( n_components = 15 , whiten = False ) data = pca ....new_data = kde . sample ( 44 , random_state = 0 ) new_data = pca ....scikit-learn.org/stable/auto_examples/neighbors/plot_digits_kde_sampling.html -
FastICA — scikit-learn 1.6.1 documentation
fun = 'logcosh' , fun_args = None , max_iter = 200 , tol = 0.0001...n_components = None , * , algorithm = 'parallel' , whiten = 'unit-variance'...scikit-learn.org/stable/modules/generated/sklearn.decomposition.FastICA.html -
k_means — scikit-learn 1.6.1 documentation
sample_weight = None , init = 'k-means++' , n_init = 'auto' , max_iter...max_iter = 300 , verbose = False , tol = 0.0001 , random_state...scikit-learn.org/stable/modules/generated/sklearn.cluster.k_means.html -
Blind source separation using FastICA — scikit-...
n_samples = 2000 time = np . linspace ( 0 , 8 , n_samples ) s1 = np...( size = S . shape ) # Add noise S /= S . std ( axis = 0 ) # Standardize...scikit-learn.org/stable/auto_examples/decomposition/plot_ica_blind_source_separation.html -
Recursive feature elimination with cross-valida...
n_informative = 3 , n_redundant = 2 , n_repeated = 0 , n_classes = 8 ,..., y = make_classification ( n_samples = 500 , n_features = 15...scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_with_cross_validation.html -
Univariate Feature Selection — scikit-learn 1.6...
y = load_iris ( return_X_y = True ) # Some noisy...y_train , y_test = train_test_split ( X , y , stratify = y , random_state...scikit-learn.org/stable/auto_examples/feature_selection/plot_feature_selection.html -
NuSVC — scikit-learn 1.6.1 documentation
nu = 0.5 , kernel = 'rbf' , degree = 3 , gamma = 'scale'...coef0 = 0.0 , shrinking = True , probability = False , tol = 0.001...scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVC.html